Introduction

The signature finding of the landmark 1964 Surgeon General's report, Smoking and Health, was the conclusion that cigarette smoking was a cause of lung cancer in men (U.S. Department of Health, Education, and Welfare [USDHEW] 1964). At that time, cancer was a highly feared disease with limited therapeutic options (Mukherjee 2010). Surgery and radiation therapy were essentially the only treatment options, as chemotherapy was in its infancy. The efficacy of chemotherapy for childhood acute lymphoblastic leukemia and for testicular cancer had not yet been established (Proctor 1995). Chemoprevention, as now used for breast cancer, for example, had not been implemented. Screening was employed for only one disease, cervical cancer, using the Papanicolaou (Pap) smear. The first trial of mammographic screening for breast cancer, the Health Insurance Plan (HIP) study, had just been launched (Mukherjee 2010). Many of the most critical advances in mechanistic understanding that are relevant to prevention and treatment today had yet to arrive (Table 6.1) (DeVita and Rosenberg 2012).

Table 6.1

Singular discoveries and major events in the cancer field and changing relative survival rates for persons with cancer in the United States, 1863–2006.

From the perspective of 2014, the understanding 50 years ago of the pathogenesis and etiology of cancer was also quite limited (Figure 6.1) (DeVita and Rosenberg 2012). Radiation was a long-established cause of multiple types of cancer; the increased risk of lung cancer in radon-exposed uranium miners was established; and follow-up of the atomic bomb survivors had documented their increased risk of acute leukemia. Clinical experience and epidemiologic studies were documenting links between occupational exposures, including asbestos and nickel oxides, and cancer. The wave of epidemiologic studies that focused on lifestyle and risk of cancer was just starting, and relatively little attention was given to viruses and bacteria as causes of cancer.

Figure 6.1

Timeline of pivotal events in cancer prevention. Source: DeVita and Rosenberg 2012. Reprinted with permission from Massachusetts Medical Society, © 2012. Notes: BCG = bacille Calmette-Guérin; DCIS = ductal carcinoma in situ; FDA = U.S. (more...)

The process of carcinogenesis was commonly understood as prolonged and involving multiple stages, leading to uncontrolled cell replication (Armitage and Doll 1954; Shimkin 1977). The 1964 Surgeon General report's discussion of carcinogenesis referred to “…a slow multi-stage process” (p. 142) and pointed out that some chemicals are “initiators,” causing permanent changes in cells, while others are “promoters” of the carcinogenic process. The structure of DNA and the genetic code were identified, but research on DNA, mutations, and cancer was just starting (Table 6.1). Of course, many processes now considered to be critical in carcinogenesis (e.g., those involving oncogenes, tumor suppressor genes, and epigenetics) had not yet been discovered.

Figures 4.3 and 4.4 document trends in cancer mortality among men and women for the period 1930–2010 (American Cancer Society [ACS] 2013). However, mortality does not capture the full picture of cancer occurrence, since it matches incidence (i.e., the occurrence of new cases) for only those malignancies for which survival is very poor. For lung cancer, given a 5-year survival rate of around 15%, incidence and death rates are close. In 1964, lung cancer was the leading cause of cancer deaths in men, having passed colorectal cancer about a decade previously. Death rates for stomach cancer had declined steadily in men and women, as had the uterine (corpus and cervix) cancer mortality rate for women. The lung cancer mortality rate in 1964 for women was just starting its upward trajectory. Figure 4.3 charts the continuing course of lung cancer death rates, showing an eventual plateau and decline in men. Figure 4.4 shows a long upward course and then the beginning of a decline in women.

Overall, cancer survival has also improved in the United States. In 1953, relative 5-year survival for people with cancer was only 35% (DeVita and Rosenberg 2012). By 1977, the figure was 49% and the most recent data from the National Cancer Institute's (NCI's) Surveillance, Epidemiology, and End Results (SEER) Program for cases diagnosed between 2003–2009 and followed through 2010 was 68% (NCI 2013).

Since 1973, the incidence of cancer has been tracked in some states and metropolitan areas through the SEER Program. Figures 6.2 and 6.3 show trends for age-adjusted incidence of cigarette-caused cancers across the span covered by the SEER data among men and women. Among men, incidence rates of lung, colorectal, oropharyngeal, stomach, and laryngeal cancers have declined over time, but rates for kidney and liver cancers continue to rise. The trend is similar among women, with the exception of lung cancer for which incidence rates increased in the two decades since 1975, and reached a plateau since the mid-1990s, before declining in 2007 (Howlader et al. 2013). In addition to the SEER areas, the rest of the nation and the District of Columbia are covered by the National Program of Cancer Registries (NPCR) of the Centers for Disease Control and Prevention (CDC). The Annual Report to the Nation on the Status of Cancer, a collaborative publication by the ACS, the North American Association of Central Cancer Registries, CDC, and NCI, provides an ongoing assessment of progress in cancer control. The most recent report reveals a decline in the incidence of lung cancer for both men and women in the first decade of the twenty-first century (Jemal et al. 2013). For men, the rate declined by 2.0% annually during this decade, while the annual decline was 0.2% for women.

Figure 6.2

Surveillance, Epidemiology, and End Results (SEER) age-adjusted incidence, selected sites, males, 1975–2010. Source: Howlader et al. 2013. Note: The data are for nine SEER areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, Seattle, (more...)

Figure 6.3

Surveillance, Epidemiology, and End Results (SEER) age-adjusted incidence, selected sites, females, 1975–2010. Source: Howlader et al. 2013. Note: The data are for nine SEER areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, (more...)

This chapter reviews the evidence on smoking and cancer for malignancies for which the evidence was previously found to be inadequate or was insufficient to reach a causal conclusion. Specifically, four cancer sites are covered—breast, colon and rectum, liver, and prostate—and also the changing cigarette and risk for lung cancer over time. The chapter also covers the relationship between smoking and the outcome of cancer, a topic not previously addressed in the reports of the Surgeon General on smoking and health. Previous reviews related to cancer were included in the 2004 Surgeon General's report on active smoking (U.S. Department of Health and Human Services [USDHHS] 2004) and in the 2006 report on exposure to secondhand smoke (USDHHS 2006). Figure 1.1A shows those malignancies for which the Surgeon General's reports classified the relationship with smoking as causal. The chapter begins with an overview of the mechanisms by which smoking causes cancer, based on the indepth coverage of this topic in the 2010 Surgeon General's report How Tobacco Smoke Causes Disease (USDHHS 2010).

Mechanisms of Cancer Induction by Tobacco Smoke

Classic studies demonstrating the covalent binding of carcinogens, or their reactive electrophilic metabolites, to cellular macromolecules (including DNA) were published at about the same time as the 1964 Surgeon General's report on smoking and health (USDHEW 1964; Miller and Miller 1976). Building on these seminal observations, many researchers explored this mechanistic concept in detail and confirmed it for different classes of chemical carcinogens; that line of research continues even today (Searle 1984; Loebe and Harris 2008; Penning 2011). Tobacco smoke, with its multiple carcinogens, recapitulates the classic mechanisms established in these studies. The general concept of exposure to carcinogens, metabolism to reactive intermediates, and DNA damage leading to mutations in critical genes has been established as one major mechanism by which tobacco smoke causes cancer. This topic was discussed in some detail in Chapter 5 of the 2010 Surgeon General's report. A mechanistic framework encompassing these steps and related phenomena was presented in that report and in related publications, and it is reproduced here as Figure 6.4 (Hecht 1999, 2012a). This section will present a brief overview of the relevant steps in Figure 6.4 and a more detailed discussion of some recent findings pertinent to this overall mechanism.

Figure 6.4

Pathway for causation of cancer by carcinogens in tobacco smoke. Source: Modified from U.S. Department of Health and Human Services 2010.

People begin to smoke cigarettes at a relatively young age, typically have difficulty stopping, and may continue to smoke for decades. Nicotine is addictive, but is not a direct chemical carcinogen (see Chapter 5, “Nicotine”) (Maier et al. 2011; Murphy et al. 2011). However, by creating and sustaining addiction, it leads to the prolonged exposure to tobacco smoke that increases cancer risk for smokers. When smokers inhale smoke, each cigarette puff delivers a mixture of carcinogens and toxicants. Tobacco smoke contains more than 7,000 chemicals, and at least 69 of these can cause cancer (USDHHS 2010). These include polycyclic aromatic hydrocarbons (PAHs); tobacco-specific nitrosamines; aromatic amines; and volatile carcinogens such as formaldehyde, acetaldehyde, 1,3-butadiene, and benzene (as well as various metals).

Most constituents of cigarette smoke, including the carcinogens, are compounds foreign to the human body and, consequently, are acted upon by metabolizing enzymes designed to detoxify them. These enzymes, including cytochrome P-450, glutathione S-transferases, and UDP-glucuronosyl transferases and sulfotranferases, catalyze the conversion of these foreign compounds to more water-soluble products that can be easily excreted from the body. But during this process, certain reactive compounds may be formed as intermediates. Examples of these reactive intermediates include electrophilic carbocations or epoxides that can bind covalently to nucleophilic sites in DNA, including the nitrogen and oxygen atoms of DNA nucleobases. These binding products are known as DNA adducts and are critical in carcinogenesis if they are not fixed by DNA repair enzymes. Persons with rare syndromes in which DNA repair is deficient, such as Xeroderma pigmentosum, are highly prone to cancer development; people with this syndrome develop skin cancer because of the multiple types of DNA damage that result from exposure to sunlight (Weinberg 2007).

There is convincing evidence for the presence of DNA adducts in the lungs and other tissues of smokers in amounts generally higher than those found in nonsmokers. While many of these adducts remain unidentified, a number of studies have characterized specific carcinogen-DNA adducts in the tissues of smokers (Phillips and Venitt 2012).

If the DNA adducts produced by tobacco smoke carcinogens and their metabolites evade repair systems and remain, they can cause miscoding during DNA replication when bypass DNA polymerase enzymes direct the placement of an incorrect nucleobase opposite the adduct (USDHHS 2010). This can result in a permanent mutation in the DNA sequence. If this mutation occurs in an important section of a cellular oncogene such as KRAS, or in a tumor suppressor gene such as TP53, the result can be an alteration of the normal growth control mechanisms, leading to uncontrolled proliferation, further mutations, and cancer. Multiple studies, using state-of-the-art methods, have shown that thousands of mutations are present in the DNA of lung tumors from smokers, including in critical growth regulatory genes, most frequently KRAS and TP53. These genes are discussed in more detail below (Greenman et al. 2007; Ding et al. 2008a; Lee et al. 2010c; Pleasance et al. 2010).

Some constituents of tobacco smoke or their metabolites may bind directly to cellular receptors, leading to activation of protein kinases, growth receptors, and other pathways, which can contribute to carcinogenesis (Chen et al. 2011b). Cigarette smoke contains substances that can induce inflammation resulting in enhanced pneumocyte proliferation, activation of nuclear factor-kappa B (NF-κB), and tumor promotion (Takahashi et al. 2010). Cigarette smoke also has cocarcinogens which, while not carcinogenic themselves, enhance the smoke's carcinogenic effects. Further, cigarette smoke induces oxidative damage and gene promoter methylation, processes that also likely contribute to cancer development.

In the last few years, there have been some developments that were not fully covered in the 2010 Surgeon General's report, but are pertinent to a fuller understanding of the mechanisms of carcinogenesis by cigarette smoke. They are discussed briefly here.

Addiction to nicotine results from its binding to nicotinic acetylcholine receptors (nAChRs). An association between common variants in the CHRNA5-CHRNA3-CHRNB4 nAChRs subunit gene cluster on chromosome 15q25 and the risk of lung cancer was reported in three genome-wide association studies (Amos et al. 2008; Hung et al. 2008; Thorgeirsson et al. 2008). These genes are strongly associated with nicotine dependence (Saccone et al. 2007), and multiple studies have confirmed and amplified these observations (Saccone et al. 2009, 2010; Timofeeva et al. 2011; Wang et al. 2011; Ware et al. 2011; Wassenaar et al. 2011). These results are likely due to changes in smoking behavior causing an increased uptake of nicotine as well as a greater presence of lung carcinogens, such as 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), in carriers of the gene variants described above (Le Marchand et al. 2008). The increased uptake of nicotine, which was confirmed by measurement of its metabolite cotinine in a similar study based on the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, is a surrogate for the uptake of carcinogens and toxicants in cigarette smoke (Timofeeva et al. 2011; Yuan et al. 2011a, 2012). Thus, carriers of the gene variants smoke their cigarettes more intensely and are exposed to higher levels of NNK and other carcinogens in smoke, thereby increasing their risk of lung cancer.

Modern DNA-sequencing methods allow scientists to carry out detailed investigations of mutations in human cancers. Because there are multiple carcinogens in cigarette smoke and multiple DNA adducts in the lungs of smokers, one would expect to find many mutations within critical genes in the lung tumors from smokers. Sequencing studies are consistent with this expectation. For example, when Greenman and colleagues (2007) investigated mutations in the coding exons of more than 500 protein kinase genes, they found that lung cancers were among those with the most somatic mutations (4.21 per megabase). The authors attributed this finding to recurrent exposure to exogenous mutagens (Greenman et al. 2007). Another investigation sequenced 188 primary lung adenocarcinomas; altogether, 247 megabases of tumor DNA sequence were analyzed and 1,013 nonsynonymous somatic mutations in 163 of the 188 tumors were identified, including 915 point mutations, 12 dinucleotide mutations, 29 insertions, and 57 deletions (Ding et al. 2008a). Twenty-six significantly mutated genes were identified, including oncogenes and tumor suppressor genes commonly found to be mutated in lung cancer, such as TP53, KRAS, CDKN2A, STK11, and others. Mutations were most common in TP53 and KRAS.

More recently, a report on complete exome and genome sequences of 183 lung adenocarcinomas revealed a mean exonic somatic mutation rate of 12.0 events per megabase (Imielinski et al. 2012). Analysis of nucleotide context-specific mutation signatures grouped the sample set into distinct clusters that correlated with smoking history and alterations of reported lung adenocarcinoma genes. Elsewhere, Pleasance and colleagues (2010) sequenced a small-cell lung cancer cell line; these investigators identified 22,190 somatic substitutions, including 134 in coding exons. They found that G→T transversions were the most common (34%), followed by G→A transitions (21%) and A→G transitions (19%). These results are similar to data that have been obtained by analysis of the TP53 gene, which is discussed later in this overview. Elsewhere, a case report focused on a non-small-cell lung cancer (NSCLC) from a 51-year-old patient who had smoked 25 cigarettes per day for 15 years prior to excision of the tumor, which yielded a poorly differentiated sample with 95% tumor content, most likely an adenocarcinoma (Lee et al. 2010c). In this patient, single nucleotide variants were common, mostly at G→C base pairs, frequently G→T transversions; these were statistically distinct from germline mutations. More than 50,000 single nucleotide variants were observed, approximately 17.7 mutations per megabase. At least eight genes in the EGFR-RAS-RAF-MEK-ERK pathway were either mutated or amplified.

In another investigation, whole-exome sequencing and gene copy number analyses were used to study 32 primary head and neck squamous cell carcinomas (Agrawal et al. 2011). Tumors from patients with a history of tobacco use had more mutations than did tumors from patients who did not use tobacco, and tumors that were negative for human papilloma virus (HPV) had more mutations than did HPV-positive tumors. Six of the genes that were mutated in multiple tumors were assessed in up to 88 additional head and neck squamous cell carcinomas. In addition to previously described mutations in TP53, CDKN2A, PIK3CA, and HRAS, new frequent mutations were found in FBXW7 and NOTCH1. In all, 11 of the 28 mutations (39%) identified in NOTCH1 were predicted to truncate the gene product, suggesting that NOTCH1 may function as a tumor suppressor gene rather than as an oncogene in this tumor type. Moreover, a similar study of 78 additional tumors reported that 30% of the cases harbored mutations in genes that regulate squamous differentiation (including NOTCH1, IRF6, and TP63), implicating such dysregulation as a major driver of carcinogenesis in head and neck squamous cell carcinoma (Stransky et al. 2011).

The results of these studies are consistent with those reported in the 2010 Surgeon General's report and with information found in the COSMIC (Catalogue of Somatic Mutations in Cancer) database (Wellcome Trust anger Institute 2012), which stores and displays somatic mutations in genes associated with cancer, such as TP53 and KRAS. Collectively, the available results of late-generation sequencing studies, as well as the extensive databases on TP53 and KRAS mutations, are completely consistent with the induction of multiple mutations in critical growth control genes by metabolically activated carcinogens of cigarette smoke, although other processes downstream from exposure to carcinogens could also contribute.

Epigenetic changes, defined as nonsequence DNA changes, are also an integral part of cancer progression. Gene promoter hypermethylation is an epigenetic change, involving extensive methylation at the 5-position of C in CpG islands within the promoter region, and, often, extending into exon 1 of regulatory genes (Jones and Baylin 2002). In lung cancer, more than 750 genes are inactivated by gene promoter hypermethylation, and new genes are still being identified through genomewide screening approaches (Selemat 2012). The end result of this process can be the loss of gene transcription and, therefore, the silencing of gene function. Comparison of DNA methylation profiles between lung adenocarcinomas of current and never smokers, using a genome-wide platform, showed only modest differences between the groups, and it identified only LGALS4 as significantly hypermethylated and downregulated in smokers (Selamat et al. 2012). Analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation and was significantly associated with KRAS mutation and, to a lesser extent, with smoking. Promoter methylation of several genes, including P16, occurs early in tumor formation. One study of head and neck cancer found that P16 methylation was significantly and positively associated with pack-years¹ of smoking and was an independent risk factor for overall survival, being significantly associated with shorter survival in patients with early resectable adenocarcinomas (Ai et al. 2003). In that study, P16 promoter hypermethylation also correlated significantly with a history of alcohol consumption or tobacco use in head and neck cancer. Other genes, such as BRMS1 and RASSF1A, may be more frequently methylated in various tumor types from smokers. In a study by Tessema and colleagues (2009), the frequency of methylation of TNFRSF10C, BHLHB5, and BOLL was significantly higher in adenocarcinomas from never smokers than in those from smokers. Methylation of genes, such as MGMT and AGT promoter hypermethylation, may increase G→A transition mutations at CpG sites within the TP53 gene in NSCLC.

These data in aggregate support the pathways illustrated in Figure 6.4. The contribution of specific tobacco smoke carcinogens to lung cancer (and also to esophageal cancer) has been investigated in several nested case-control studies as well. In these studies, the carcinogens or their metabolites were quantified in stored urine samples that were collected from smokers years or decades before cancer developed. For example, using frozen urine samples collected during the 1980s from more than 18,000 smokers in Shanghai, China, scientists have found that specific metabolite levels were associated with an increased risk of lung or esophageal cancer, even after correction for the number of years of smoking and number of cigarettes smoked per day (Yuan et al. 2009, 2011a,b). Thus, significantly elevated risks for lung cancer were associated with increased levels of the NNK metabolites' total NNAL [4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides] and the PAH metabolite phenanthrene tetraol. The strongest elevated risk was for esophageal cancer in individuals with the highest levels of the tobacco-specific carcinogen N′-nitrosonornicotine and its glucuronides in their urine. This carcinogen induces a high incidence of esophageal tumors in rats (Yuan et al. 2009, 2011a,b).

Smokers experience proinflammatory changes in their lungs. Inflammation is intimately associated with activation of NF-κB and tumor promotion (Malkinson 2005; Smith et al. 2006; Lee et al. 2008), and many studies in laboratory animals demonstrate that anti-inflammatory agents can decrease tobacco carcinogen-induced lung tumorigenesis (Hecht et al. 2009). In addition, chronic obstructive pulmonary disease, particularly emphysema, is an independent risk factor for lung cancer in smokers. This association further implicates a strong role for inflammation in lung cancer (Turner et al. 2007). In one study, the tumor-promoting activity of cigarette smoke was examined in mouse models of lung tumorigenesis (Takahashi et al. 2010); here, exposure to smoke after treatment of A/J mice with NNK increased the multiplicity of lung tumors. Similar results were obtained in KRAS^LA2 mice harboring a mutation in KRAS codon 12 identical to that caused by NNK. IκB kinase β (IKKβ) was required for NF-κB activation and played a critical role in tumor promotion in this system, most likely through the induction of inflammation and related phenomena (Takahashi et al. 2010). These studies amplify and extend earlier observations demonstrating the tumor-promoting activity of cigarette smoke.

Changing Cigarettes and Risk for Lung Cancer Over Time

Cigarette smoking is the predominant cause of lung cancer in the United States, and lung cancer is the country's leading cause of cancer death (USDHHS 2004). Cigarette smoke, which contains multiple carcinogens (Hoffmann and Hoffmann 1997; IARC 2004; USDHHS 2004; Rodgman and Perfetti 2009), is composed of gases and particles with a distribution of size that result in substantial deposition in the lung when the smoke is inhaled (Stratton et al. 2001; Gower and Hammond 2007). The composition of tobacco smoke varies with cigarette type (e.g., filtered or unfiltered) and across brands of the same type (IARC 2004; Burns et al. 2008; World Health Organization [WHO] 2008b). Over past decades, multiple substantive changes in the design and composition of cigarettes have altered the chemistry of tobacco smoke raising the question as to whether lung cancer risks have changed in response (Hoffmann and Hoffmann 1997; Rodgman and Perfetti 2009). This section reviews evidence relevant to this question.

This section focuses on lung cancer because it is the cancer most related to cigarette smoking (USDHHS 2004). Substantial data are available, both over time and from many countries, on the occurrence of lung cancer, both generally and by histologic type. The topic of lung cancer in relation to smoking has been addressed in depth in several past reports of the Surgeon General. These reports have focused on levels of machine-measured tar and nicotine in relation to risk and have considered whether changes in design and characteristics that have lowered the tar yield of cigarettes have also reduced the risk of diseases caused by smoking (USDHHS 1981, 2004). The 2004 Surgeon General's report on the health consequences of smoking concluded that no substantive reduction in the risk of disease was associated with using cigarettes with low levels of tar, as measured by machine. This and earlier reports clearly document that machine-measured tar yields have little relationship to the doses actually received by smokers because of the phenomenon of compensation. This section focuses mainly on whether the changes in the design and composition of cigarettes over time that paralleled the reduction in tar yields (by machine measurement) may have altered—and possibly even increased—the risk of lung cancer associated with cigarette smoking. The analysis is limited to cigarette design issues and does not consider other issues, such as changing nicotine yields and the marketing of various types of cigarettes. This section does not explore the implications of these changes for diseases other than lung cancer.

Changes in Cigarettes Over the Past Several Decades

Since the 1950s, cigarettes have undergone changes in their design and composition (Hoffmann and Hoffmann 1997; NCI 2001). The most prominent changes have been the addition of filters and the use of ventilation holes in the filters to lower machine-measured tar and nicotine yields. Figure 6.5 shows the rapid rise in the use of filtered cigarettes that followed the heavy marketing of such cigarettes in the mid-1950s. The marketing effort promised a lower risk product to smokers who had become concerned about the disease risks of smoking (Brandt 2007). This shift to filters continued and today almost all manufactured cigarettes currently consumed in the United States are filtered (Hoffmann and Hoffmann 1997; NCI 2001). Figure 6.6 shows the move to cigarettes with lower tar yields, beginning with a shift from brands with more than 20 milligrams (mg) of machine-measured tar to lower tar-yielding brands in the late 1960s and early 1970s (NCI 2001). By 1990, about two-thirds of cigarettes sold had either medium (11–15 mg), low (6–10 mg), or very low (1–5 mg) yields of tar. The principal mechanism underlying the lower yields of machine-measured tar was the increase in the number and the size of ventilation holes in the filter, thereby diluting the smoke entering the machine (Hoffmann and Hoffmann 1997; NCI 2001). Although these changes reduced tar delivery as measured by the U.S. Federal Trade Commission's (FTC's) protocol, which did not reflect how smokers actually smoke; they did not reduce the risks of disease and premature mortality in smokers (NCI 2001; USDHHS 2004).

Figure 6.5

Market share and total annual cigarette sales of filtered and unfiltered cigarettes in the United States, 1925–1993. Source: National Cancer Institute 2001; data from Maxwell 1994.

Figure 6.6

Market share of total cigarettes sold per year, by tar yield (milligrams [mgs] of tar by Federal Trade Commission method), United States, 1967–1990. Source: National Cancer Institute 2001; data from Maxwell 1994. Note: Tar levels for given years (more...)

Epidemiologic evaluations of risk and assessments of smoke chemistry during the decades in which these substantial changes occurred tended to treat all cigarettes as if they were equivalent, both over time and across brands. The exception was that these evaluations did consider the machine-measured tar and nicotine yields and whether they were filtered. However, the design and composition of cigarettes changed substantially in other ways, even as they were continuously redesigned to deliver ever lower machine-measured yields of substances. Unfortunately, researchers in the past did not have access to information about the nature and extent of these and other changes in cigarettes because they were handled as trade secrets and, therefore, not disclosed by the industry.

Changes in Design, Curing, and Composition

Although smokers may perceive cigarettes as very simple devices: chopped-up tobacco rolled in paper, perhaps with a filter attached to the end, the reality, however, is that cigarettes are highly engineered products (Hoffmann and Hoffmann 1997; Rodgman and Perfetti 2009; Proctor 2011). The design features of cigarettes can have significant effects on the composition of the tobacco smoke and perhaps its toxicity. Over time, changes to cigarettes have become progressively more extensive and more complex, further complicating the efforts of researchers to understand their health implications (Hoffmann and Hoffmann 1997; NCI 2001; O'Connor et al. 2008; O'Connor and Hurley 2008; WHO 2008b). Many factors can influence the chemistry of tobacco smoke: (1) the geographic location where the tobacco is grown (which can alter the heavy metal content of smoke, for example) (IARC 2004, 2007); (2) agricultural practices (which can influence levels of nitrates and pesticides, but also polonium and heavy metal content as well) (Hoffmann and Hoffmann 1997; Rodgman and Perfetti 2009); (3) how the tobacco is cured and processed (which can influence tobacco-specific nitrosamine levels and other factors) (Hoffmann and Hoffmann 1997; NCI 2001; Peele et al. 2001; O'Connor et al. 2008; O'Connor and Hurley 2008); (4) the blend of tobacco used; (5) the use of reconstituted tobacco sheet and puffed tobacco (tobacco expanded through an industrial process) (Hoffmann and Hoffmann 1997; IARC 2004, 2007; O'Connor et al. 2008; O'Connor and Hurley 2008; Rodgman and Perfetti 2009); (6) the engineering characteristics of the manufacturing process (Hoffmann and Hoffmann 1997; O'Connor et al. 2008; O'Connor and Hurley 2008; Rodgman and Perfetti 2009); (7) the additives used in tobacco; and (8) the pattern of puffing the smoker uses to generate the smoke (which can alter the quantity of smoke generated and the relative composition of its constituents) (WHO 2007, 2008b; Burns et al. 2008).

Cigarettes in Australia, Canada, and the United Kingdom are made primarily of flue-cured tobacco, but most brands sold in the United States use a blend of air-cured tobaccos (Hoffmann and Hoffmann 1997; IARC 2004; WHO 2008b; Rodgman and Perfetti 2009). In addition, substantial amounts of reconstituted tobacco sheet and puffed tobacco are added to the blend. The soil in which the tobacco is grown, the agricultural practices used, and the methods of curing and processing the tobacco also differ across brands and have changed over time (Hoffmann and Hoffmann 1997; Peele et al. 2001; IARC 2004; Rodgman and Perfetti 2009). Flavoring agents; processing aids, such as humectants; chemicals intended to alter the pH of the smoke; and other agents are added to tobacco as part of the manufacturing process.

Approaches used to alter the processes of generating smoke may involve the cut size of the reconstituted tobacco sheet, filter ventilation, the density of the tobacco in the rod, the composition and design of the filter material, the porosity of the cigarette paper, and other factors (Hoffmann and Hoffmann 1997; O'Connor et al. 2008; O'Connor and Hurley 2008; Rodgman and Perfetti 2009). The pattern the smoker uses to puff the cigarette is superimposed on all of its intrinsic characteristics. This pattern varies among smokers and can change with different types of cigarettes smoked by the same smoker; it can also change systematically across smokers in response to certain design features, most notably filters and ventilation (NCI 2001; WHO 2008b). Rodgman and Perfetti (2009), O'Connor and colleagues (2008), and O'Connor and Hurley (2008) have reviewed the impact of many of these factors on the composition of tobacco smoke, but a detailed review of the extensive literature describing the effect of isolated changes on smoke composition is beyond the scope of this section.

Beyond the data held by the manufacturers, the details on differences in the design and composition of cigarettes across U.S. brands are not available in a systematic form. Complete and representative information is also not available over time on the composition of smoke generated by individual brands or on the changes in manufacturing practices for different brands. Longitudinal data on brands marketed in the United States are limited to data—using FTC's protocol—on machine-measured yields of the tar, nicotine, and carbon monoxide produced. Without this information, the research and public health communities have been unable to fully assess the potential effects of changes in the design and composition of cigarettes on smokers' exposures over time to toxicants in cigarette smoke. Nevertheless, the limited data that exist allow for some assessment of likely changes in smoke toxicity following changes that have been made in cigarettes.

Differences Across Brands in Toxicant Yields

Of the 7,000 or more constituents in tobacco and tobacco smoke, 69 have been identified as carcinogens (USDHHS 2010). The complexity and expense of measuring multiple constituents for all the different brands under multiple sets of machine parameters have led tobacco industry scientists to suggest that constituent yields can be benchmarked and reliably predicted from machine-measured tar yields (Counts et al. 2004, 2005, 2006; Morton and Laffoon 2008). This concept is based on the assumed relationship between the total mass of smoke and its nicotine content, as measured by a smoking machine. However, the mass of smoke generated by a smoking machine using any fixed protocol bears little relationship to the amount of smoke inhaled by a smoker or to the differences between brands in smoke exposure (Jarvis et al. 2001; NCI 2001). A more appropriate method for examining the variation in constituent yields across brands is to examine these yields after they have been normalized per mg of tar or per mg of nicotine to characterize the variation that might be experienced for a given level of nicotine intake.

Nicotine is the principal addictive constituent sought by the smoker and the ratio of tar to nicotine is relatively constant across brands. When the Massachusetts Benchmark Study data on yields for a 1999 sample of U.S. brands of cigarettes are normalized per mg of tar or per mg of nicotine, the ability of tar yields to predict the variation in yields of other constituents is poor (Harris 2001, 2004). In fact, the normalized yields of several constituents are higher for cigarettes with low machine-measured tar yields than for those whose machine-measured tar yields are high (Harris 2004).

Table 6.2 presents the variability in the yields of a variety of constituents across brands, normalized per mg of tar or per mg of nicotine, from the Massachusetts Benchmark Study sample of U.S. cigarettes in 1999. In this table, the coefficient of variation across brands (which represents the standard deviation of the measurements across brands normalized to the mean value of that constituent for all brands) is divided by the mean standard deviation of replicate measurements for that constituent. This formulation expresses the variation of constituents across brands in relation to the precision with which the constituent can be measured. Table 6.2 demonstrates that for many of the toxicants measured, the variation in constituents across brands, normalized per mg of tar or per mg of nicotine, is many times higher than can be explained by the variability of the measurement. Clearly, at least in terms of constituent yields from machine-generated cigarette smoke, smoke from all cigarettes is not uniform in composition. This variability is likely not limited to 1999, when the cigarettes were sampled, or to have remained constant over time. Furthermore, normalized constituent yields in Canadian and Australian cigarette brands and a sample of international blended cigarette brands manufactured by Philip Morris International have demonstrated similar variability (WHO 2008b). In addition, when biomarkers of exposure to specific toxicants are assessed, the data show considerable variability in their levels among smokers, particularly in heavy smokers (Joseph et al. 2005); this finding is consistent with variation in exposure due to differences in smoke composition across brands and to inherent variability among smokers.

Table 6.2

Ratio of brand coefficient of variation to replicate measurement coefficient of nicotine and tar variation per milligram (mg), per Massachusetts Machine Smoking Protocol, in rank order.

Changes in Tobacco-Specific Nitrosamine and Benzo[a]pyrene Levels Over Time

Because only limited longitudinal data are available for toxicant yields, changes in these yields over time are difficult to characterize accurately for all brands. However, for one major U.S. brand, some data are available for two of the major toxicants: benzo[a]pyrene (B[a]P) and the tobacco-specific nitrosamines (N′-nitrosonornicotine [NNN] and NNK).

B[a]P, one of the earliest identified carcinogens in cigarette smoke, is a typical carcinogenic PAH and is often used as a surrogate index for the PAHs as a group. Efforts to reduce the levels of this carcinogen in smoke have included increasing the proportion of tobacco in the cigarette rod that is made up of reconstituted sheet, changing the tobacco blend, increasing the porosity of the paper, and using other techniques (O'Connor et al. 2008; O'Connor and Hurley 2008; WHO 2008b; Rodgman and Perfetti 2009). Data are not available for all U.S. brands over time, but Hoffmann and Hoffmann (1997) published data for a prominent cigarette brand, measured repeatedly from 1959–1995, that showed a modest decline in B[a]P levels in smoke over that period.

In contrast to the decline in levels of B[a] P, levels of tobacco-specific nitrosamines, specifically NNK, increased dramatically in the previously referenced brand from 1978–1995 (Hoffmann and Hoffmann 1997). This increase was due in part to the increased nitrate levels in the tobacco used in cigarettes even before the curing (Hoffmann and Hoffmann 1997; Ding et al. 2008b; O'Connor et al. 2008; O'Connor and Hurley 2008; Rodgman and Perfetti 2009) and to changes in curing practices that have increased the presence of oxides of nitrogen and nitrate ion and the latter's reaction products during curing, with the resultant formation of tobacco-specific nitrosamines from the nicotine in the leaf (Hoffmann and Hoffmann 1997; NCI 2001; Peele et al. 2001; IARC 2004; Ding et al. 2008b; O'Connor et al. 2008; O'Connor and Hurley 2008).

Differences in Toxicant Yields Across Countries

Relatively more evidence is available for differences in toxicant yields from comparisons of international brands of cigarettes. Of particular note, the use of burley tobacco in U.S.-style blended cigarettes contributes substantially to the differences in tobacco-specific nitrosamines between U.S.-style cigarettes and those of Canada and Australia (Burns et al. 2008; Ding et al. 2008b; WHO 2008b), where most brands contain mainly unblended, flue-cured tobacco. Datasets are available for some smoke constituents that have been measured for major brands in the Canadian and Australian markets (WHO 2008b) and for a selection of international brands of blended cigarettes manufactured by Philip Morris (Counts et al. 2004, 2005).

Several other differences between Canadian and Australian brands were found, although cigarettes in both countries are made with unblended, flue-cured tobacco. Differences in the levels of cadmium and lead between the brands are notable. Figure 6.7 presents the mean yields of some toxic constituents for the major Canadian and Australian brands sampled in late 2000 to early 2001. The yields are normalized per mg of nicotine and expressed as a ratio to the mean yields for an international sample of brands manufactured by Philip Morris. The data for the Canadian brands are presented for all brands and for brands other than those with high NNN levels (U.S.-style and Gauloise cigarettes). The expected differences between flue-cured and blended cigarettes are evident (Ding et al. 2008b); the flue-cured cigarettes from Australia and Canada have much lower levels of tobacco-specific nitrosamines (notably NNN and NNK) and substantially higher levels of B[a]P. Australian and Canadian brands, however, differ markedly from blended cigarettes in a number of other toxicants, with lower levels of oxides of nitrogen; 1-ami-nonapthalene; 1,3-butadiene; and isoprene. Canadian, but not Australian, cigarettes have higher levels of catechol, phenol, and cresols. These differences may reflect the use of tobacco grown for use in cigarettes in different regions of Canada and Australia.

Figure 6.7

Ratio of the means of constituents in cigarette brands from Canada and Australia to the mean for an international sample of U.S.-style blended cigarettes manufactured by Philip Morris and sampled from late 2000 to early 2001. Source: Counts et al. 2005; (more...)

Figure 6.8 shows the differences in NNN and NNK between Australian brands and a blended version of the Marlboro brand designed for the Australian market (Burns et al. 2008; WHO 2008b). The levels of NNN and NNK in the blended-tobacco cigarette from Marlboro are much higher than those for even the highest level brand reported to the Australian regulatory authorities.

Figure 6.8

Mean and range of N′-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) yields per milligram [mg] of nicotine for brands reported to the Australian government, contrasted with the levels of NNN and NNK reported for (more...)

These differences in tobacco-specific nitrosamine levels in smoke translate to different exposures among smokers. Mouth-level exposures to NNN and NNK and urinary measures of NNAL—a metabolite of NNK—are higher among smokers in the United States than in smokers in Australia and Canada (Ashley et al. 2010), demonstrating that the observed differences in the composition of smoke result in substantive differences in exposure to tobacco-specific nitrosamines.

Low-Tar Cigarettes Do Not Reduce Risk of Lung Cancer

Early efforts to alter the risks of cigarettes focused on reducing the yields of tar and nicotine as measured by machine-smoking methods. As a result, machine-measured yields of tar and nicotine declined by more than 60% from the 1960s to 1990 (Hoffmann and Hoffmann 1997; NCI 2001). Much of that reduction was accomplished initially by adding filters and later by ventilating the filter to dilute the smoke coming through it, thus lowering the machine-measured yields of tar and nicotine so the newer products could be marketed as being less risky to health (NCI 2001). But to compensate for the reduced yields, smokers changed the way they smoked these cigarettes, resulting in no meaningful reduction in either the total dose of smoke received or in the risks of diseases caused by smoking (NCI 2001; USDHHS 2004). Changes in patterns included increasing the volume and velocity of puffs, increasing the duration of puffing, and shortening the intervals between puffs (NCI 2001). However, the protocol for smoking by machines was not changed.

Overall Death Rates for Lung Cancer Indicate Increased Risk of Smoking in Recent Decades

In the United States, the prevalence of smoking among males has declined since at least the 1950s, but age-adjusted death rates for lung cancer among men did not begin to decline until approximately 1990 (Wingo et al. 1999). Among women, the comparable death rates peaked around 2003 and significantly declined (Jemal et al. 2013), likely due to considerable success in reducing the prevalence of smoking among women. The long delay between decreases in the prevalence of smoking and changes in death rates for lung cancer raises the question as to whether there might have been an increasing risk of lung cancer over time from smoking cigarettes that could have contributed to this delay.

Epidemiologic studies are a key source of evidence for assessing whether the risk of lung cancer associated with smoking has changed over time. Particularly informative is the comparison by Thun and Heath (1997) of two prospective cohort studies of the risk of smoking conducted by the ACS. Each study, conducted more than 20 years apart, followed more than 1 million men and women. The Cancer Prevention Study I (CPS-I) began in 1959, and the Cancer Prevention Study II (CPS-II) began in 1982. The more than two decades between the studies saw substantial changes in the design and composition of cigarettes and in the brands of cigarettes that Americans smoked. The decline in machine-measured yields of toxicants in cigarettes between these two studies led to an expectation that the risk of lung cancer death for smokers would likely be lower in the CPS-II. The authors compared death rates from lung cancer in the first 6 years of follow-up for each study among the subsamples of never and current smokers at enrollment. The risks were found to be higher in CPS-II (Thun and Heath 1997). Figure 6.9 presents the results from these analyses for men and women current smokers and never smokers based on 786,387 CPS-I and 711,363 CPS-II participants. The risk for never smokers (as measured by the death rate from lung cancer) went essentially unchanged during the interval between the two studies, but the risk for smokers increased dramatically, with a proportionately greater increase among women smokers. The increase in risk of death from lung cancer remained after controlling for measured differences in duration and intensity (number of cigarettes smoked per day) between the smokers in the two studies.

Figure 6.9

Death rates from all lung cancers, by smoking status, Cancer Prevention Study I (CPS-I) and Cancer Prevention Study II (CPS-II), 1959–1965 and 1982–1988. Source: Thun and Heath 1997. Reprinted with permission from Elsevier, © 1997. (more...)

The 40-year follow-up of the British Doctors' Study from 1951–1991 presents similar evidence. During the second 20 years of follow-up, the risk of death from lung cancer was greater than during the first 20 years (Doll et al. 1994); this increase over time was limited to smokers and former smokers. Among never smokers, rates of lung cancer mortality were relatively constant across calendar years (Thun et al. 2006, 2008), suggesting that the changes observed in the relative risk (RR) of smoking were unlikely to have resulted from changes in population demographics or in other risk factors for lung cancer in the general population.

Models of risk based on smoking patterns have been applied to data on smoking prevalence for birth cohorts (i.e., sets of individuals born during specified calendar years and for whom rates can be examined as the cohorts advance in age and calendar year) to estimate the expected occurrence of death from lung cancer in the absence of any change in the risk imposed by smoking. Using birth-cohort-specific data on smoking developed by Harris (1983) and a multistage carcinogenesis model similar to that developed by Whittemore (1988), Swartz (1992) predicted overall age-adjusted trends in lung cancer mortality for White men from 1970–1985. The author estimated that a 12% decline in rates should have occurred during this interval, based on the assumption of a constant effect over time. However, this estimated decline contrasted sharply with the observed 26% increase in lung cancer death rates during the interval (Swartz 1992). To predict death rates for lung cancer over time by birth cohort, Tolley and colleagues (1991) used an updated set of birth-cohort-specific estimates for smoking prevalence and a risk model developed by Peto (1986) that was based on data from the British Doctors' Study (Doll et al. 1994). These authors estimated that overall lung cancer mortality should have started to decline in the early 1980s for White men and in the mid-1990s for White women. Instead, observed lung cancer mortality continued to rise throughout the 1980s, peaking in the early 1990s for White men (Wingo et al. 1999) and 2003 for women generally (Jemal et al. 2013). A similar approach, using risk models developed from the CPS-I data and birth-cohort-specific data on smoking prevalence from the National Health Interview Survey, demonstrated a systematic trend of increasing underestimation of observed death rates for lung cancer across all birth cohorts with advancing calendar years (NCI 2001).

Estimates of smoking behaviors for birth cohorts that incorporate changes in the number of cigarettes smoked per day were developed for NCI's Cancer Intervention and Surveillance Modeling Network (CISNET) (Anderson et al. 2012). These estimates are more detailed than previous data on smoking behaviors for birth cohorts and include estimates of the intensity and duration of smoking for 5-year birth cohorts from 1900–1984. For each calendar year, these estimates provide rates of smoking initiation; prevalence of current and former smoking; and distributions of the duration of smoking, the duration of abstinence, and the number of cigarettes smoked per day for current and former smokers. These estimates for smoking behavior were combined with risk models for current, former, and never smokers derived from 12-year follow-up data from the CPS-I (Knoke et al. 2004, 2008) to estimate birth-cohort-specific lung cancer death rates from 1960–2000 (Burns et al. 2011b). The resulting estimates were compared with observed U.S. national lung cancer death rates for the same birth cohorts. The comparison showed a progressively increasing underestimation of U.S. national lung cancer death rates across all birth cohorts as calendar years advanced from the 1960s to 2000 (Burns et al. 2011b). This underestimation was eliminated when a term that increased the risk of smoking, based on the estimated duration of smoking after 1972, was added to the risk model. These analyses suggest that estimates of smoking-related lung cancer deaths that are based on observations in the 1960s underestimate the current risks of smoking, implying that the risk of death from lung cancer associated with smoking may have increased over the past several decades—that is, during the same decades in which changes in the design of cigarettes were made.

Considering the increase in risk of death from lung cancer seen from CPS-I to CPS-II, Thun and Heath (1997) recognized the possibility that the risk of death from lung cancer observed in CPS-I might underestimate the contributions of (a) amount smoked and (b) duration of smoking due to overreporting in the CPS-I data of the duration of smoking and the number of cigarettes smoked early in life. Among White men, the transition from other forms of tobacco use (e.g., cigars and pipes) to cigarettes began largely after 1914, because cigarette smoking was uncommon before that year (Burns et al. 1997). Because lung cancer is a disease of older ages, much of the lung cancer mortality experience in CPS-I occurred among men who were well past their adolescence by 1914, and yet many of them reported initiating smoking at early ages. Some participants in CPS-I may have reported initiating cigarette smoking at the time at which they first used tobacco of any type, or they may have otherwise overestimated their duration of cigarette smoking, leading to a longer reported duration of cigarette smoking than actually occurred. The resulting misclassification, with a bias toward reporting a longer duration of smoking, could lead to a reduced magnitude of the estimated effect of duration of smoking on risk of lung cancer death in risk models based on CPS I data. Because a much larger fraction of those who developed lung cancer in CPS-II took up smoking after 1914, the effect of overreporting the duration of smoking would be lower in CPS-II, the magnitude of the estimated duration effect would increase, and the risk of smoking would appear to have increased between the two studies, with adjustment for differences in reported duration of smoking.

The study used the CISNET smoking rates and risk models based on CPS-I (Burns et al. 2011b) and attempted to minimize the contribution of overreporting of smoking duration and early smoking by eliminating birth cohorts born before 1915—the period during which overreporting was most likely. In addition, the potential for underestimation of the increase in duration over time to produce the observed progressive underestimation of the U.S. birth-cohort-specific death rates for lung cancer with advancing calendar time was examined by iteratively increasing the duration term and examining the fit of the resulting estimates to the observed U.S. death rates. Although increasing the duration term increased the estimated rates as anticipated, the pattern of a progressive change in risk remained even as calendar years advanced, with an overestimated actual risk giving way to an underestimated risk as calendar years advanced. Thus, an increasing effect of duration on risk of death from lung cancer did not explain the progressive underestimation of mortality from lung cancer, whereas a term increasing the risk of cigarette smoking over time did.

Overreporting in CPS-I also may have resulted in an overestimation of the number of cigarettes smoked early in life, but the contribution of cigarettes smoked per day to risk of lung cancer is much smaller than the contribution of duration (Flanders et al. 2003; Knoke et al. 2004), and the exponent for the cigarettes-per-day term in the CPS-I risk equations is close to one (Knoke et al. 2004, 2008). As a result, any underestimation of lifetime number of cigarettes smoked per day due to overreporting of smoking early in life is expected to be modest and could be approximated by a constant that would be incorporated in the risk equations when they are adjusted for the healthy population selection bias (Pinsky et al. 2007) required for such estimates (Tolley et al. 1991; Burns et al. 2011b).

To further assess changes in the risk of lung cancer from smoking over time, Thun and colleagues (2013) extended their analyses by comparing the lung cancer risk associated with smoking observed in five contemporary cohorts (2000–2010) with risks observed in CPS-I (1959–1965) and CPS-II (1982–1988). For never smokers, rates of death from lung cancer remained constant across time among men and increased only slightly among women. Among females 55 years of age and older at baseline, the RR for lung cancer comparing current smokers to never smokers progressively increased from 2.73 in CPS-I to 12.65 in CPS-II to 25.66 for the 2000–2010 cohorts. Corresponding RRs for current male smokers were 12.22, 23.81, and 24.97, respectively. Compared with their counterparts in CPS-I and CPS-II, both men and women in the contemporary cohorts were at greater risk for lung cancer despite smoking fewer cigarettes per day. Duration of smoking increased substantially across the study time periods for women. In comparison, duration of smoking changed only modestly for men across the studies and actually declined slightly between CPS-II and the 2000–2010 cohorts.

Thun and colleagues (2013) also stratified their analyses by smoking intensity (i.e., number of cigarettes smoked per day) and duration of smoking for all three study periods. Within each stratum of smoking intensity and duration of smoking, the RR estimates increased over time for women. For men, RR estimates increased over time within each stratum of smoking intensity, but a consistent pattern was not evident for each stratum of smoking duration. The authors concluded that the risk of lung cancer from smoking has continued to increase among women but among men has plateaued at the very high levels observed in the 1980s.

Trends in most other tobacco-related cancers have not been examined in detail, although Baris and colleagues (2009) reported an increase in the incidence of bladder cancer over the past several decades.

Changes Over Time in the Types of Lung Cancer Associated With Smoking

Adenocarcinoma of the lung has been increasing in the United States since the 1970s (Travis et al. 1996; Wingo et al. 1999), as manifested in rising incidence rates and an increasing proportion of all lung cancers that are adenocarcinomas (Wingo et al. 1999; Devesa et al. 2005). Theoretically, this increase could be due to changes over time in the classification of tumors, but an analysis by Charloux and colleagues (1997) found the increase to be real and not a consequence of changing diagnostic practices.

Notably, the increase in adenocarcinoma of the lung has been accompanied by an increase in the estimated RR for this type of lung cancer associated with cigarette smoking. Early in the investigation of the lung cancer epidemic, the most common histologic type of lung cancer in men was squamous cell carcinoma, and the RR of squamous cell carcinoma associated with smoking was substantially higher than that for adenocarcinoma (Wu-Williams and Samet 1994; USDHHS 2004). Kreyberg (1962) even debated whether adenocarcinoma was associated with cigarette smoking, because of the low RR and because adenocarcinoma is the most common type of lung cancer among women who have never smoked. As the incidence of lung adenocarcinoma increased over time, the RRs of this type of lung cancer associated with smoking also increased (USDHHS 2001), suggesting that a new, or at least a substantially enhanced, risk of developing adenocarcinoma of the lung occurred in smokers. In a comparison of data from CPS-I and CPS-II, Thun and colleagues (1997) found that the RR for adenocarcinoma increased in smokers from 4.6 for men and 1.5 for women (per data from CPS-I, conducted 1959–1965) to 19.0 for men and 8.1 for women (per data from CPS-II, conducted 1982–1988), but that the age-adjusted death rates for adenocarcinoma of the lung among never smokers were essentially unchanged over the period. Furthermore, risk for lung cancer of all tissue types among never smokers remained constant over the same interval (Thun et al. 2006, 2008).

Trends across calendar years in age-standardized incidence rates of lung cancer have also varied by tumor type. Figure 6.10 presents trends in age-standardized incidence rates in the United States from 1973–2010 for lung cancer by gender and histologic type using data from NCI's SEER Program. Among men, the decline in the incidence rate of squamous cell carcinoma started well ahead of the decline for incidence rates for adenocarcinoma; similar trends are seen for women. Rates of squamous cell and small cell carcinoma have been declining in men since the early- to mid-1980s, but rates of adenocarcinoma did not peak until the 1990s (Travis et al. 1996; Wingo et al. 1999; Devesa et al. 2005). Age-standardized rates in women reflect their later uptake of smoking, resulting in a later year of peak smoking-induced rates of lung cancer, and the patterns are more difficult to interpret. However, rates of squamous cell carcinoma leveled off among women around 1990, but their rates of adenocarcinoma continued to increase through the 1990s (Wingo et al. 1999; Devesa et al. 2005). The recent trends in rates for the NSCLCs have been affected by trends in diagnostic practice, reflecting treatment approaches that are targeted by histologic type. There has been a trend to avoid nonspecific classification and to designate lung cancers as adenocarcinoma and squamous cell carcinoma (Langer et al. 2010; Travis et al. 2011; Conde et al. 2013).

Figure 6.10

Standardized incidence of lung cancer, by gender and histology (age adjusted to 2000 U.S. population), 1973–2010. Source: Surveillance, Epidemiology, and End Results (SEER) Program, public use data. Note: Other non-small-cell-lung carcinoma (NSCLC) (more...)

Interpreting age-standardized rates of lung cancer is difficult because of variations in the prevalence of smoking, in the distribution of duration of smoking, and in the distribution of the duration of abstinence in the U.S. population over the past several decades. For that reason, rates of lung cancer by histologic type have also been examined by birth cohorts. This approach examines outcomes as the population born during the selected calendar years initiates and quits smoking over time (and ages, as well). These two smoking behaviors have been found to differ substantially across sequential birth cohorts for the U.S. population (Burns et al. 1997, 2011b).

Zheng and colleagues (1994) found that birth-cohort-specific rates of lung cancer by histologic type across calendar years in the Connecticut Tumor Registry data demonstrated a clear birth-cohort pattern for increased rates of adenocarcinoma; that is, there were identifiable differences in rates by cohort. These changes paralleled gender and generational changes in smoking rather than advances in diagnostic procedures (Thun et al. 1997a). In this Connecticut study, the birth-cohort trends for squamous cell carcinoma were consistent with changes in smoking prevalence by birth cohort over time, but rates of adenocarcinoma by birth cohort progressively increased for both men and women in a manner that was not consistent with changes in smoking prevalence by birth cohort (Zheng et al. 1994). This increase was consistent with an increase over time in the risk of adenocarcinoma associated with smoking due to changes in the design of cigarettes, including the introduction of filters and low-tar cigarettes (Zheng et al. 1994; Thun et al. 1997a).

Figures 6.11 and 6.12 present incidence rates for lung cancer by histologic type based on 5-year birth cohort data from the SEER Program. Although the proportion of lung cancer that is adenocarcinoma is somewhat higher for women across all birth cohorts, a trend is found in which adenocarcinoma represents an increasing proportion of lung cancer when sequential cohorts are examined for both men and women. Data in Figures 6.11 and 6.12 are combined in Figure 6.13 to present mean values for the proportions of all lung cancers with a designated histologic type that were adenocarcinoma for those cohorts with data available. The mean values demonstrate a substantial increase in the proportion of lung cancer that is adenocarcinoma when moving from the earliest to the more recent cohorts. An important caveat in interpreting these means is that the age range for each cohort is different, as it must be, with the earliest cohorts having only the older age ranges and the more recent cohorts only the younger age ranges.

Figure 6.11

Incidence of lung cancer among U.S. men from various birth cohorts, by histologic type (adenocarcinoma, squamous cell carcinoma, and small and large cell carcinoma) and year of diagnosis, 1975–2000. Source: Surveillance, Epidemiology, and End (more...)

Figure 6.12

Incidence of lung cancer among U.S. women from various birth cohorts, by histologic type (adenocarcinoma, squamous cell carcinoma, and small and large cell carcinoma) and year of diagnosis, 1975–2000. Source: Surveillance, Epidemiology, and End (more...)

Figure 6.13

Unweighted mean percentage of all lung cancers that were adenocarcinoma, by gender and birth cohort for the available calendar years, United States, 1890–1955. Source: Surveillance, Epidemiology, and End Results Program, public use data.

Data from the SEER Program do not contain information about smoking status at the individual level, but the birth-cohort rates for the different histologic types presented in the figures result from a steadily progressing mixture of current, former, and never-smoking behaviors that are specific for each cohort as it moves forward in time. Therefore, differences in the proportion of lung cancers due to a specific histologic type are not due to differences by histology in overall smoking behaviors, given that these behaviors are the same for all of the histologic types in any given calendar year. Differences by histologic type within a cohort can reflect differences in the relationship of age to histologic type, differences in the rate of decline in risk after smoking cessation for the different histologies, or variation in the exposures over time in the agents causing the different types of lung cancer.

Effects due to aging, such as those that might be manifested if the durations of smoking required to produce squamous cell carcinoma and adenocarcinoma are different, would likely reveal themselves in a similar fashion across all cohorts as those cohorts reach the appropriate ages, but Figures 6.11 and 6.12 do not indicate a consistent pattern with age.

The time course of reduction in excess risk of lung cancer after cessation of smoking likely differs for the different histologic types. For example, some data suggest that excess risks for squamous cell and small cell lung cancers may decline more rapidly after cessation than do excess risks for adenocarcinoma (Kenfield et al. 2008). As calendar years have advanced, the U.S. population in the age groups at substantial risk for lung cancer (i.e., those over 50 years of age) is composed of an increasing fraction of former smokers, and those former smokers have had longer durations of abstinence. The potential effect of a slower decline in risk for adenocarcinoma raises the possibility that the decline in squamous cell carcinoma and the increase in adenocarcinoma over time may be a result of a relatively more rapid decline in risk for squamous cell carcinoma, leaving an increasing fraction of lung cancer as adenocarcinoma. However, if the increasing proportion of lung cancer that is adenocarcinoma was in fact due to this effect (of a less rapid decline in the excess risk for adenocarcinoma following cessation), then the greatest shift would be in the earliest birth cohorts, among whom the effects of differences in risk with abstinence would be most evident. Figures 6.11 and 6.12 show the opposite pattern—the greatest increase in the proportion of lung cancer that is adenocarcinoma occurs in the more recent birth cohorts who are younger in age and have less cumulative abstinence.

The birth-cohort pattern observed in Figures 6.11 and 6.12 suggests that changes in the design and composition of cigarettes may be a factor that is driving the increase in rates of adenocarcinoma (Charloux et al. 1997; Thun et al. 1997a; NCI 2001). Risk of lung cancer reflects cumulative exposure to cigarette smoke, and if a change in the design or composition of cigarettes increases the risk of lung cancer from smoking, then the onset of increasing risk begins at the time when the change is made. Each succeeding cohort would have a larger fraction of its cumulative smoking exposure from the new cigarettes, as existing brands are refashioned and smokers switch to brands with greater risk characteristics. This increased risk becomes stronger in successive birth cohorts, particularly if use of the newer, more hazardous product is more common among younger than older smokers. Among older individuals from the earlier birth cohorts, rates of lung cancer will continue to be dominated by the substantial contribution of their past smoking, and an increase in risk resulting from a more recently changed cigarette product will make a relatively modest proportional contribution to the pre-existing and already substantial risk for these cohorts. As more recent birth cohorts are examined, the onset of increasing risk due to a change in product design will begin at an earlier age because members of the cohort will begin smoking the newer products at a younger age. The increment in risk with the use of the newer products reflects a larger proportion of the total risk for the cohort, simply because the duration of smoking preceding the shift to a more dangerous type of cigarette is shorter and thus the risk for that earlier period as a fraction of total risk is smaller. Such an effect could explain the progressive increase in the proportion of lung cancers that are adenocarcinomas across sequential cohorts, as shown in Figure 6.13.

Differences in the prevalence of current and former smoking and differences in the distribution of the duration of smoking and the duration of abstinence from smoking vary markedly across birth cohorts and contribute to differences in risks of lung cancer. To account for these differences in the examination of rising rates of adenocarcinoma, birth-cohort-specific smoking behaviors have been used to model changes in the rates of lung cancer of different histologic types (Burns et al. 2011a), as was done for overall lung cancer mortality and incidence rates. Risk models derived from CPS-I were applied to the smoking behaviors of birth cohorts. These behaviors include rates of smoking initiation, prevalence of current and former smoking, and distributions of the duration of smoking, duration of abstinence, and number of cigarettes smoked per day for current and former smokers (Burns et al. 2011a). The resulting rates were adjusted for a healthy population selection bias and differences between rates of incidence and mortality and then were scaled, based on the fraction of lung cancers of the appropriate histologic type in the SEER Program data for the first years available (1973–1975).

The predicted rates for squamous cell carcinoma and adenocarcinoma by 5-year birth cohort were compared with the rates observed in data from the SEER Program for the same cohorts during the calendar years 1973–2000. For squamous cell carcinoma, the predicted rates closely matched the rates from the SEER Program, suggesting that much of the variability in the incidence rates of squamous cell carcinoma over the past several decades can be explained by changes in the rates of smoking prevalence and cessation. In contrast, the predicted rates for adenocarcinoma did not match data in the SEER Program, and the differences between predicted rates and those of the SEER Program varied systematically by birth cohort. When a term increasing the risk for adenocarcinoma with duration of smoking after 1950 was added to the risk model for current and former smokers (to simulate an increasing risk over time associated with a change in the design of cigarettes), the predicted rates matched the rates from the SEER Program. Thus, these analyses suggest that increasing risk of lung cancer over time may be associated with changes in the design or composition of cigarettes. The analyses also raise the possibility that the increase in overall lung cancer mortality from smoking may reflect an increase in the risk of developing adenocarcinoma from smoking, with little change in the risk of developing squamous cell carcinoma.

Some researchers have suggested alternative explanations for the increase in lung adenocarcinoma. Based on birth-cohort analyses of data from the SEER Program and differences in the temporal trends in the incidence of squamous cell lung cancer and adenocarcinoma of the lung, Chen and colleagues (2007b,c, 2009) suggested an effect of air pollution, and specifically nitrogen oxides, as the cause for the trends in adenocarcinoma. However, because among never smokers both lung cancer mortality and the incidence of adenocarcinoma do not seem to have changed over time and because the risk of adenocarcinoma among smokers has increased, changes in cigarette smoking are a more likely cause of the temporal trends than air pollution.

Changes in the demographics of smokers are another potential explanation. Over time, the poorer and less-educated segments of the population have become a progressively greater fraction of U.S. smokers (see Chapter 13, “Patterns of Tobacco Use Among U.S. Youth, Young Adults, and Adults”). Within birth cohorts, an increasing proportion of smokers come from population groups characterized by less education and lower income and cessation rates are lower in these groups as well, compared with those having more education and higher incomes. Occupational and environmental exposures associated with increased lung cancer risk are also more common among those with less education and lower income. As a result, the effects of this demographic shift should be relatively uniform across cohorts, unlike the pattern observed in the figures. In addition, a demographic shift of this type in the characteristics of smokers would not affect rates of adenocarcinoma or squamous cell carcinoma or would not affect rates of adenocarcinoma or influence rates of squamous cell carcinoma.

In summary, adenocarcinoma has been increasing in the United States as a fraction of all lung cancers, becoming the most common histologic type of lung cancer. Despite decreases in smoking prevalence and concomitant decreases in squamous cell carcinoma, the incidence of lung adenocarcinoma among smokers has increased since the 1960s. Changes in the design and/or composition of cigarettes during the 1960s have increased the levels of tobacco-specific nitrosamines and other carcinogens in cigarette smoke. Evidence from birth-cohort models and epidemiologic studies are sufficient to conclude that the increased risk of lung adenocarcinoma among smokers is due to changes in the design and/or composition of cigarettes which increased the carcinogenicity of cigarette smoke.

Evidence for a Rising Risk of Adenocarcinoma of the Lung in the United States

Differences Across Time in Rates of Adenocarcinoma Within the United States and Across Countries

In a population, the principal determinants of risk for lung cancer are the prevalence of current smoking and the distribution of the duration of smoking among current and former smokers. As described previously, assessing the impact of differences in population-based smoking behaviors on rates of lung cancer is a complex undertaking. Even so, some understanding can be gained by comparing rates of lung cancer in countries where smokers have similar behaviors but smoke different types of cigarettes.

Incidence rates of adenocarcinoma of the lung and the proportions of adenocarcinoma in relation to all lung cancers increased in most countries through 1995–1997 (Devesa et al. 2005). These trends were particularly evident among women and reflected the higher risk of lung cancer accompanying their increasing smoking prevalence and a rising proportion of lung cancer that was adenocarcinoma (Devesa et al. 2005). When examined at the national level, however, the rates of increase of adenocarcinoma and the patterns of the shift to adenocarcinoma as the most common form of lung cancer varied among countries (Devesa et al. 2005). In many countries—such as European countries (Devesa et al. 2005), including Italy (Russo et al. 1997); Japan (Yoshimi et al. 2003); and Hong Kong (Tse et al. 2009)—the patterns among men have roughly mimicked those of U.S. men, with falling rates of squamous cell carcinoma and initially rising but then falling rates of adenocarcinoma. Among women, interpretations of changes in rates of cancer by histologic type need to consider the rising rates of smoking prevalence for women. Regardless, rates of adenocarcinoma rose faster than rates of squamous cell carcinoma in most countries for which data were available (Devesa et al. 2005).

As described previously, flue-cured cigarettes of the type preferred in Australia, Canada, and the United Kingdom have substantially lower levels of tobacco-specific nitrosamines than do U.S.-style blended cigarettes and have higher levels of B[a]P (WHO 2008b). Tobacco-specific nitrosamines, specifically NNN and NNK, are organ-specific carcinogens for adenocarcinoma of the lung in animal models (IARC 2007; USDHHS 2010); NNK selectively induces adenocarcinoma of the lung in rats, mice, and hamsters. The level of NNAL, a metabolite of NNK, in the urine of smokers has been shown to be an independent predictor of risk for lung cancer even when the analysis controls for intensity (by cotinine concentration) and duration of smoking (Church et al. 2009; Yuan et al. 2009).

In terms of PAHs, one prospective cohort study found that a biomarker (phenanthrene tetraol) for PAH exposure was not an independent predictor of risk for lung cancer (Church et al. 2009). When the risk for lung cancer was examined by histologic type in this study, however, a significant association was found between NNAL in the urine and adenocarcinoma of the lung. The relationship between NNAL and risk for lung cancer was not significant for all other types of lung cancer combined, and the odds ratios for adenocarcinoma and other lung cancers did not differ significantly from each other (Church et al. 2009).

Mouth-level exposure to tobacco-specific nitrosamines in smoke has been examined among smokers in countries with high use of blended cigarettes (United States) and flue-cured unblended cigarettes (Australia, Canada, and the United Kingdom) (Ashley et al. 2010). Levels of NNK exposure among Australian and Canadian smokers were approximately one-third that of U.S. smokers, and levels of NNN exposure were 85–90% lower than the U.S. experience. Among smokers in the United Kingdom, levels of NNK exposure were 20% lower than those of smokers in the United States, and levels of NNN were approximately 50% lower (Ashley et al. 2010).

In England and Scotland, flue-cured cigarettes remain popular, but measures of the level of exposure to tobacco-specific nitrosamines fall between those observed in smokers in the United States and in smokers in other countries where unblended cigarettes are common (Canada and Australia) (Ashley et al. 2010). In England (Bennett et al. 2008) and Scotland (Harkness et al. 2002), incidence rates of adenocarcinoma of the lung for men have increased only slightly, and squamous cell carcinoma remains the predominant lung cancer. Rates of squamous cell carcinoma among men in those countries are declining consistently as smoking prevalence drops.

In Canada, the incidence rate of adenocarcinoma among men in 1995–1997 remained lower than that of squamous cell carcinoma and well below the rate for White men in the United States (Figure 6.14) (Devesa et al. 2005). In contrast, rates for squamous cell carcinoma were similar for men in the United States and Canada in this period and in women as well (Devesa et al. 2005). Based on data up to 1997, the incidence of adenocarcinoma of the lung did not appear to be increasing over time in Canada. Instead, the data suggest that squamous cell carcinoma was decreasing so that adenocarcinoma represented an increasing fraction of lung cancers over time (Devesa et al. 2005).

Figure 6.14

Trends in incidence rates for lung cancer (age adjusted, world standard), by histologic type (squamous cell carcinoma, small cell carcinoma, and adenocarcinoma) and geographic area, 1980–1982 to 1995–1997. Source: Devesa et al. 2005. Reprinted (more...)

In Australia, where flue-cured, unblended cigarettes with low tobacco-specific nitrosamine levels are also prominent, the rate of adenocarcinoma among men rose across birth cohorts and over time and exceeded the rates of squamous cell carcinoma for the most recent cohorts (Blizzard and Dwyer 2002). In contrast, the rate of adenocarcinoma among men in New South Wales, Australia, remained essentially constant between 1985 and 1997 (Figure 6.14) (Devesa et al. 2005) or rose only slightly over time. However, the rate for squamous cell carcinoma in 1995–1997 among New South Wales men declined to a level approximating that of adenocarcinoma (Figure 6.14) (Joshua et al. 2005). Similarly in South Australia, the rate of adenocarcinoma among men through 2000 was also relatively consistent over time, and the rate of squamous cell carcinoma fell to the same level as adenocarcinoma (Nguyen et al. 2003). However, in South Australia, the rate of adenocarcinoma increased among younger age groups.

When comparing the United States and Australia, the different patterns of cigarettes smoked may contribute to different patterns of lung cancer. Figure 6.15 presents gender- and age-specific rates of lung cancer mortality for the United States and Australia for 2000 (Peto et al. 2006). Lung cancer death rates were lower in all age groups for men and women in Australia compared with the United States. Detailed comparisons of smoking behaviors similar to those used to model U.S. death rates are not available for Australia, but estimates of the prevalence of smoking show a general similarity for Australia and the United States, particularly during the 1990s (White et al. 2003).

Figure 6.15

Age-specific rates of lung cancer death, by gender and age group, in the United States and Australia, 2000. Source: Peto et al. 2006.

Figure 6.16 presents information on adenocarcinoma as a proportion of all lung cancers with a designated histologic type, by birth cohort and gender for the United States and Australia (Burns et al. 2011a). In Australia, a modest rise occurs in the proportion of lung cancers that are adenocarcinoma across the birth cohorts for both genders, but the fraction remains well below 50% for men and only slightly above 50% for women. Data for the United States show a much more dramatic increase in the proportion of lung cancer that is adenocarcinoma, with the proportion exceeding 60% in the most recent cohorts for White men and women. Notably, the earliest birth cohorts for the U.S. population, those born from 1880–1900, have proportions similar to those found in Australia.

Figure 6.16

Adenocarcinoma as a percentage of designated lung cancers in U.S. White men and women and Australian men and women, by various birth cohorts, 1890–1955. Source: Burns et al. 2011a. Reprinted with permission from Science & Business Media (more...)

In summary, rates for squamous cell carcinoma of the lung have been decreasing in most countries in which the prevalence of smoking has been declining. In contrast, the incidence rate of adenocarcinoma has been rising in the United States and has been level or increasing in other countries, with the general result that adenocarcinoma has increased as a proportion of lung cancer in most countries. The magnitude of that increase has differed between the United States, where the predominant type of cigarette is made of blended tobacco with relatively high levels of tobacco-specific nitrosamines, and Canada and Australia, where flue-cured cigarettes with lower levels of tobacco-specific nitrosamines predominate. Incidence rates of adenocarcinoma and the proportion of lung cancer that is adenocarcinoma are substantially higher in the United States than in Canada and Australia.

Effects of Filter Ventilation on Deposition of Smoke in the Lung and the Toxicity of This Smoke

One potential explanation for the rise in adenocarcinoma of the lung in the United States is a change in the pattern of smoking after ventilated filters were introduced to lower the machine-measured yields of tar and nicotine (Zheng et al. 1994; Thun et al. 1997a; Wingo et al. 1999). Smokers who shift to brands with nominally lower machine-measured yields with ventilated filters change their smoking pattern to restore their nicotine delivery to the level needed to sustain their addiction. As described previously, changes include increasing puff volume and velocity, greater duration of puffing, and shortening the intervals between puffs (NCI 2001). In addition, smokers may increase the depth of inhalation and hold the smoke in their lungs longer to increase nicotine uptake. Notably, there is little difference in markers of nicotine ingestion between smokers of brands of cigarettes with substantially different machine yields (Jarvis et al. 2001; NCI 2001). Increasing depth of inhalation and other more intense smoking patterns likely increase the deposition of smoke in the alveolar region of the lung.

Most physical models of particles disseminating in the lung incorporate the size-dependence of particle deposition in the lung, but do not fully reflect the complexity of smoking behavior. As a consequence, the models may underestimate the fraction of smoke particles retained in the lung (Stratton et al. 2001; Gower and Hammond 2007; Rostami 2009), raising questions about their validity in characterizing the distribution and deposition of particles in different regions of the lung with different tobacco products. An analysis by Gower and Hammond (2007) of models of cigarette smoke deposition that examined the effects of the changes in pattern of smoking after a shift to brands with lower machine-measured yields showed that puff time, inhalation depth, time holding one's breath, and exhalation time may affect total smoke deposition. While a shift in deposition to the alveolar level remains a possibility, the researchers could not determine whether the changes in patterns of smoking resulting from the use of more highly ventilated cigarettes could produce a large enough shift in the location of deposition to change the pattern of incidence of a specific histologic type of lung cancer. Although the magnitude of the potential change in regional deposition in the lung remains uncertain, existing evidence suggests that changes in the pattern of smoking, with a shift to lower tar-yield cigarettes, will likely increase the fraction of cigarette smoke particles deposited in the alveolar region of the lung. This shift may also have played a role in increasing the risk of adenocarcinoma of the lung over time.

The introduction of ventilated filters, or changes in the design and composition of cigarettes that accompanied their introduction, may have increased the carcinogenicity of cigarette smoke. Given the dilution of smoke by filter ventilation and the compensation for that dilution by smokers when these cigarettes are used, comparisons of the toxicity of cigarettes on a per-cigarette basis can be misleading, making comparisons on the basis of “per mg tar” or “per mg total particulate matter” more useful.

The level of filter ventilation alters the composition of tobacco smoke. In general, based on the International Organization for Standardization protocol and under more intense smoking parameters, higher levels of ventilation result in more complete combustion in flue-cured, unblended cigarettes smoked by a machine (Adam et al. 2010). When experimental (Rickert et al. 2007) or commercial (Roemer et al. 2004) U.S.-blended cigarettes were compared with experimental, unblended, flue-cured cigarettes (Monitor-7 Canadian reference cigarette) in mutagenicity testing, the level of revertants per mg (the indicator of mutational strength) of the total particulate matter was lower for the unblended Canadian reference cigarette. For Kentucky reference cigarettes, mutagenicity per mg of total particulate matter was 30–40% lower for unfiltered cigarettes than for the same cigarette with a filter added (Shin et al. 2009).

Tobacco industry documents show internal company research demonstrating that increasing filter ventilation increases the mutagenicity of the resultant tar on a per-mg of tar basis (Johnson et al. 2009). The published evidence produced by the industry is less clear. In a study from R.J. Reynolds, Chepiga and colleagues (2000) compared full-flavor, full-flavor low-tar, and ultralow-tar cigarettes and reported a nonsignificant trend of increased revertants per mg of tar in mutagenicity studies as the level of machine-measured tar decreased. In a study from Philip Morris, Roemer and colleagues (2004) reported that higher total yields of particulate matter were associated with a trend toward less mutagenic activity per mg of total particulate matter. In another study from Philip Morris, Patskan and colleagues (2008) compared the mutagenic activity of Marlboro full flavor, Marlboro Lights, and Marlboro Ultra Lights, finding that mutagenic activity was higher per mg of total particulate matter for Marlboro Ultra Lights, but this was for only some Salmonella strains used in the mutagenicity testing and for only some runs. Thus, the evidence supports a modest increase in the mutagenicity of tobacco tar as the level of machine-measured tar falls; this effect may result from increased ventilation.

These data should be interpreted with caution for several reasons. Mutagenicity is generally used as only a screening test, is often poorly associated with carcinogenicity in humans, and has not been quantitatively associated with differences in human risk. In addition, most of the studies described previously compared smoke generated under standardized machine-testing protocols. In actual use, smokers change their patterns of smoking, compensating for the design changes that result in lower yields of machine-measured tar and nicotine. This compensatory smoking behavior makes comparisons of cigarettes with very different machine-tested yields difficult to interpret relative to carcinogenicity in humans when the smoke for the different cigarettes is generated using a single, standardized, machine-smoking protocol.

Existing evidence about changes in the patterns of smoking cigarettes with low yields of tar and high ventilation supports a shift in the deposition of smoke in the lung toward the alveolar region; this shift likely contributes to the observed increase in adenocarcinoma of the lung. Research has not clarified whether the magnitude of this shift in lung deposition, by itself, is great enough to explain the dramatic increase in adenocarcinoma observed in the United States. The mutagenicity of tobacco tar from cigarettes with lower yields of machine-measured tar is trending upward. However, the trend is modest in size, and difficulties in extrapolating results from mutagenicity testing to risk for humans make it difficult to know whether these changes contribute to increasing the risk of lung cancer.

Liver Cancer

In many parts of the world, liver cancer remains a leading cause of cancer mortality. Primary liver cancer, the great majority of which is hepatocellular carcinoma (HCC), generally presents at an advanced stage with limited treatment options and a poor prognosis. Although worldwide liver cancer is the sixth most common cancer in terms of incidence, it represents the third most common cause of cancer-related death (Ferlay et al. 2010).

A number of strong risk factors for HCC have been identified, including infection with the hepatitis B or C viruses (HBV, HCV), exposure to aflatoxins, and alcohol-associated cirrhosis (London and McGlynn 2006). The incidence of liver cancer varies geographically worldwide, with rates generally consistent with the regional prevalence of the primary viral etiologic factors (Nordenstedt et al. 2010). Globally, Asia and sub-Saharan Africa—with endemic HBV infection and common dietary exposure to aflatoxins—have the highest incidence of HCC. Rates of HCC appear to have stabilized or started to decline in several Asian countries, where widespread vaccination against HBV and reduction of HBV cofactors have occurred during the past few decades (Yuen et al. 2009). HCV infection has been the primary etiologic agent for HCC in various countries having substantial incidence of HCC (London and McGlynn 2006).

Historically, the United States has had a low incidence of liver cancer and low death rates for the disease. However, rates of HCC have been increasing in the United States over the last two decades (Altekruse et al. 2009; El-Serag 2011). In recent years, Whites and Blacks, particularly those 50–59 years of age, have experienced the largest annual percentage increases in rates of HCC; rates of HCC among Asians/Pacific Islanders have been stable (O'Connor et al. 2010). The increased rates of HCC in the United States appear to be largely a consequence of chronic HCV infection (El-Serag 2004). However, obesity, diabetes, and associated nonalcoholic fatty liver disease, and the substantial burden of chronic HBV infection among foreign-born Asians may also be potential contributors to the increasing incidence of HCC (Larsson and Wolk 2007; Starley et al. 2010). In addition to viral hepatitis, cirrhosis from consumption of alcohol represents an important cause of HCC worldwide (London and McGlynn 2006). HCC is more common among men than women, which likely reflects gender differences in exposure to viral hepatitis and rates of progression of that disease, differences in smoking and in consumption of alcohol, and perhaps hormonal differences.

The association between smoking and HCC is complicated by the potential for confounding with the causal factors of consumption of alcohol and HBV and HCV infection. For example, people who drink alcohol are more likely to be smokers than people who do not drink alcohol (Dawson 2000). In addition, most HCV infections worldwide are acquired by injecting drugs, and the prevalence of smoking is very high among injection drug users (Marshall et al. 2011). In regions of the world with a high incidence of HCC, HBV infection is generally acquired perinatally or during early childhood. However, in other regions, HBV may be more commonly acquired through parenteral or sexual transmission; these behaviors may also be associated with smoking. Hence, the potential confounders must be examined carefully when assessing the association between smoking and HCC. However, considerable epidemiologic evidence, including data from studies in which measures have been taken to address potential confounding, indicates that smokers are at an increased risk for liver cancer (IARC 2004).

Epidemiologic Evidence

Since the 2004 report of the Surgeon General, 90 additional studies have been published or identified that report on the association between smoking and liver cancer. IARC (2004) concluded that there was sufficient evidence of a causal association between cigarette smoking and liver cancer. Subsequently, Lee and colleagues (2009) published a meta-analysis that was based on the studies considered in the 2004 IARC report.

Studies for the current review were compiled by searching the MEDLINE database (from January 1966 to December 2012) using the medical subject headings “tobacco,” “smoking,” “liver neoplasms,” or “hepatocellular carcinoma” and by examining references cited in the previous Surgeon General's reports, the IARC (2004) monograph on smoking and liver cancer, and the associated meta-analysis (Lee et al. 2009). The epidemiologic data came from a wide range of studies in both low- and high-incidence countries (Tables 6.3S and 6.4S). For many studies, the outcome was defined as HCC and was based on clinical, radiographic, laboratory (alpha-fetoprotein levels), or pathologic criteria. A minority of studies relied on linkage to cancer or mortality registries, often using primary liver cancer as the outcome defined by the coding of cancer diagnoses from the International Classification of Disease for Oncology or causes of death from the International Classification of Diseases. Some studies were unable to distinguish between HCC and intrahepatic cholangiocarcinoma; however, none of these studies were from geographic regions where intrahepatic cholangiocarcinoma would likely represent a substantial portion of primary liver cancers. Studies that did not explicitly differentiate between primary and secondary liver cancer (and therefore may have included cancers with a different primary site that had metastasized to the liver) were excluded from the analysis. Quantitative analyses included all studies that reported sufficient information to abstract or calculate an effect estimate and 95% confidence interval (CI); these analyses were stratified by study design (case-control or cohort).

This review focused on evaluations of the separate effects observed in current smokers, ever smokers, and former smokers in comparisons with never smokers or nonsmokers; studies with a reference group other than never smokers or nonsmokers were excluded (e.g., those comparing heavy smokers with light smokers). The quantitative analyses excluded all studies that compared liver cancer cases with controls who had chronic viral hepatitis, cirrhosis, or other chronic liver disease. Finally, the review separately examined the effects of smoking on HCC in studies that controlled for confounding by the main etiologic factors (HBV, HCV, and consumption of alcohol) for HCC in the region under study. Assessment of viral hepatitis status was considered adequate for inclusion in the quantitative analysis if the study reported on serological measurement of HBV surface antigen (HBsAg) or antibodies to HCV (anti-HCV) as indicators of chronic HBV or HCV infection, respectively.

Overall, 113 studies—including 59 case-control (Table 6.3S) and 54 cohort studies (Table 6.4S)—provided data on smoking and primary liver cancer. These studies, taken together, offered substantial heterogeneity in design, study population, assessment of smoking exposure, and the reporting of risk estimates. Many studies, however, were limited by having few HCC cases and reported nonsignificant increases in risk associated with various measures of smoking. Furthermore, many studies did not adequately control for potential confounding by major causal factors such as consumption of alcohol or HBV or HCV infection.

In an analysis combining data from 31 studies (12 case-control and 19 cohort) that reported sufficient information to estimate risk for HCC in current smokers compared with nonsmokers (Figure 6.17), the overall estimate for RR was 1.7 (95% CI, 1.5–1.9). The relationship between current smoking and HCC was similar in cohort studies (overall RR = 1.7; 95% CI, 1.5–1.9) and case-control studies (RR = 1.6; 95% CI, 1.2–2.1). When 11 studies (6 case-control and 5 cohort) that controlled for confounding by the primary etiologic factors (e.g., HBV, HCV, consumption of alcohol) were analyzed (Figure 6.18), the RR (1.6; 95% CI, 1.2–2.0) was similar to that in the overall analysis. Among these studies that directly addressed confounding, the relationship between current smoking and HCC was stronger in cohort studies (RR = 2.2; 95% CI, 1.4–3.3) than in case-control studies (odds ratio [OR] = 1.2; 95% CI, 0.9–1.5). Overall, these findings are similar to those in the meta-analysis performed by Lee and colleagues (2009) in association with the 2004 IARC report, which reported a 51% increased risk for liver cancer for current smokers compared with never smokers (meta-RR = 1.51; 95% CI, 1.37–1.67). The findings of the IARC (2004) review and the current review are similar, except that the present review includes a greater number of studies (31 vs. 20) and includes studies that reported results for only one gender. Both the present review and the IARC analysis defined current smoking as reported at entry into the cohort or at the time of diagnosis of liver cancer.

Figure 6.17

Estimated risk for liver cancer in current smokers compared with nonsmokers. Note: Weights are from random effects analysis. CI = confidence interval; ES = effect size; HBV = 675 hepatitis B virus; HCV = hepatitis C virus.

Figure 6.18

Estimated risk for hepatocellular carcinoma in current smokers compared with nonsmokers among studies that controlled for confounding by primary etiological factors (viral hepatitis, consumption of alcohol). Note: Weights are from random effects analysis. (more...)

Among 26 studies (18 case-control and 8 cohort) with evaluable comparisons between ever smokers and never smokers (Figure 6.19), the risk for HCC was increased among ever smokers (RR = 1.4; 95% CI, 1.3–1.6), with comparable estimates of the magnitude of effect observed in case-control studies (RR = 1.4; 95% CI, 1.1–1.7) and cohort studies (RR = 1.5; 95% CI, 1.3–1.7). In the 4 studies that adjusted for exposure to the primary etiologic agents (Figure 6.20), the magnitude of risk was notably higher among ever smokers (RR = 1.7; 95% CI, 1.4–2.2) compared to the magnitude of risk among ever smokers in studies (Figure 6.19).

Figure 6.19

Estimated risk for hepatocellular carcinoma in ever smokers compared with never smokers. Note: Weights are from random effects analysis. CI = confidence interval; ES = effect size; HBsAg = 690 hepatitis B surface antigen.

Figure 6.20

Estimated risk for hepatocellular carcinoma in ever smokers compared with never smokers among studies that controlled for confounding by primary etiological factors (viral hepatitis, consumption of alcohol). Note: Weights are from random effects analysis. (more...)

Among 33 case-control studies that evaluated dose-response relationships between smoking (e.g., increasing intensity, pack-years, or duration) and HCC, only 6 (18%) reported a statistically significant trend. Among 26 cohort studies that evaluated these relationships, 10 (38%) reported a significant dose-response effect of smoking intensity on increased risk for HCC, and 2 (8%) reported an inverse dose-response relationship. Many studies that evaluated dose response did not formally test for trends; however, a substantial proportion of these studies were not adequately powered to address such relationships. In their meta-analysis, Lee and colleagues (2009) summarized data from 7 studies with evaluable estimates and reported a significant dose-response trend showing increased risk for liver cancer with higher number of cigarettes smoked. However, this effect was notably less apparent among case-control studies that used hospital-based instead of population-based control groups.

Because of concern for residual confounding of smoking effects by coinfection with viral hepatitis, the association between smoking and HCC was evaluated in the present review among persons who did not have evidence for chronic viral hepatitis. In an analysis combining data from 13 studies (9 case-control and 4 cohort) that estimated risk among persons who were negative for markers of chronic HBV or HCV infection (Figure 6.21), the risk of HCC among current or ever smokers was significantly increased (RR = 1.8; 95% CI, 1.2–2.7) in a comparison with never smokers. After excluding a study that reported markedly increased risk among persons who were negative for HBV and HCV (Jeng et al. 2009), the estimated risk was attenuated but still significant (RR = 1.3; 95% CI, 1.0–1.8). Finally, when the analysis was restricted to the 3 studies that included only persons negative for both HBsAg and anti-HCV and also adjusted for consumption of alcohol (Kuper et al. 2000; Yuan et al. 2004; Koh et al. 2011), the RR was 1.7 (95% CI, 1.2–2.5).

Figure 6.21

Estimated risk for hepatocellular carcinoma among persons without evidence for chronic viral hepatitis infection for current or ever smokers compared with never smokers. Notes: Weights are from random effects analysis. CI = confidence interval; ES = effect (more...)

The present review did not identify any studies that directly evaluated the effects of interventions aimed at smoking cessation on subsequent risk for liver cancer. Among 23 studies with the requisite data available from the publication (11 case-control and 12 cohort) (Figure 6.22), the risk for liver cancer among persons identified as former smokers relative to never smokers was lower (RR = 1.4; 95% CI, 1.1–1.7) than for current smokers (RR = 1.7, 95% CI 1.5–1.9).

Figure 6.22

Estimated risk for hepatocellular carcinoma in former smokers compared with never smokers. Notes: Weights are from random effects analysis. CI = confidence interval; ES = effect size; HBV = hepatitis B virus; HCV = hepatitis C virus.

Despite substantial geographic variation in the incidence of HCC and the distribution of etiologic factors, smoking was consistently related to increased risk for HCC in all geographic regions, although the magnitude of the association was not as strong in studies conducted in European countries. Among 35 studies conducted in Asian countries (Table 6.3S), the RR for HCC among current or ever smokers was 1.5 (95% CI, 1.4–1.6).

In countries in sub-Saharan Africa, the present data analysis was limited to case-control studies that evaluated ever smoking. The number of cases of HCC in these studies ranged from 46–240, and all of them adjusted for HBV or HCV infection and consumption of alcohol. Each study suggested an association between smoking and HCC, but none of them were statistically significant—likely because of the limited number of cases. Overall, the RR from the three studies with data available (Kew et al. 1990; Olubuyide and Bamgboye 1990; Soliman et al. 2010) for countries in Africa was 1.7 (95% CI, 1.1–2.5).

Eight studies evaluated current or ever smoking and risk for HCC in the United States (Stemhagen et al. 1983; Austin and Cole 1986; Hsing et al. 1990; McLaughlin et al. 1995; Hassan et al. 2002, 2009; Marrero et al. 2005; Zhu et al. 2007). Veterans of the armed services were substantially overrepresented in these studies. The overall RR estimate in an analysis that combined current and ever smoking was 1.8 (95% CI, 1.3–2.5), and substantial heterogeneity in estimated risk was not found by study design.

Among the 14 studies reviewed from countries in Europe, 11 were case-control studies, largely from southern Europe, and 3 were cohort studies. Substantial heterogeneity was observed in these studies. In a series of case-control studies from Greece, smoking was consistently associated with HCC, but the associations were more pronounced (and statistically significant) among HBV-negative persons (Trichopoulos et al. 1980, 1987b; Tzonou et al. 1991; Goritsas et al. 1995). After adjusting for HBV and HCV infection, a study from Greece by Kuper and colleagues (2000) demonstrated a 1.5- and 1.6-fold nonsignificant increase in risk of HCC among persons smoking fewer than or at least 40 cigarettes per day, respectively. Elsewhere, 4 case-control studies from Italy reported null findings (Filippazzo et al. 1985; La Vecchia et al. 1988; Gelatti et al. 2005; Franceschi et al. 2006). In 2 cohort studies from Sweden, the risk estimate in 1 study among females was less than 1.0 (RR = 0.7; 95% CI, 0.2–2.0) (Nordlund et al. 1997). But, the other study observed increased rates of mortality from liver cancer among a cohort of men and a significant dose-response association with increased smoking (Carstensen et al. 1987). In a Europe-wide cohort study, Trichopoulos and colleagues (2011) rigorously characterized the smoking behavior, alcohol consumption, diet, and viral hepatitis status of a half-million people. Overall, the RR for HCC among current smokers compared to never smokers was 4.6 (95% CI, 1.9–10.9), and the RR was notably higher among males (5.4; 95% CI, 1.7–16.8) than among females (1.7; 95% CI, 0.3–8.5). In addition, the authors estimated that smoking contributed to nearly one-half of the number of cases of HCC, exceeding the proportion of HCC attributable to HBV, HCV, or consumption of alcohol. Finally, in a quantitative analysis for the present review from 5 evaluable studies in Europe, the RR for HCC among current or ever smokers (La Vecchia et al. 1988; Goritsas et al. 1995; Nordlund et al. 1997; Farker et al. 2003; Franceschi et al. 2006) was 1.4 (95% CI, 1.0–2.3).

Similar to the experience in Greece, several studies from other regions suggested a higher risk of liver cancer with smoking among HBV-negative persons than among those who were HBV positive (Lam et al. 1982; Yu et al. 1991a; Chen et al. 2008). Some other studies, however, failed to find any difference in this risk by HBV status (Kew et al. 1985; Mohamed et al. 1992; Evans et al. 2002). And yet, according to eight studies published in 2000 or later, smokers with chronic HBV or HCV infection have a substantially higher risk for HCC than those who do not have chronic hepatitis infection (Mori et al. 2000; Wang et al. 2003; Jee et al. 2004a; Franceschi et al. 2006; Fujita et al. 2006; Hassan et al. 2008; Jeng et al. 2009; Soliman et al. 2010). Formal evaluations of interactions between smoking and HBV or HCV infections have been reported infrequently from these studies.

Although the present review focuses on HCC, which represents a substantial majority of primary liver cancer, a meta-analysis by Wenbin and colleagues (2013) reported on the association between smoking with gallbladder cancer. In an analysis of data from 1,158 cases across 11 studies (all but 1 were case-control), smokers had a significantly increased risk for gallbladder cancer (RR = 1.5; 95% CI, 1.1–1.9) compared with nonsmokers.

Colorectal Cancer

Colorectal cancer—that is, cancer of the colon or rectum—is the third most common type of cancer in the United States and also ranks third as a cause of cancer deaths among men and women in the United States (Siegel et al. 2013). For 2013, the ACS projected 102,480 new cases of cancer of the colon and 40,340 new cases of cancer of the rectum as well as 51,710 deaths from the two cancers combined (Siegel et al. 2013). In the mid-1990s, the lifetime probability of developing colorectal cancer was estimated to be 5.6% in the United States (Howlader et al. 2013).

Worldwide, incidence and death rates for colorectal cancer vary more than 10-fold among countries. The highest rates occur in Australia/New Zealand, Japan, North America, and Western Europe, and the lowest rates are seen in countries with developing economies, particularly in Africa and Asia (Parkin et al. 1999). Studies show that among immigrants moving from low- to high-incidence countries, rates increase within one generation to the approximate rates of the new country, suggesting a strong role for environmental agents (Thomas and Karagas 1987). Risk also varies substantially even within countries. For example, in a study by Wei and colleagues (2009) of a middle-aged cohort of U.S. women, risk to age 70 varied up to 10-fold based on lifestyle factors.

An increased risk of colorectal cancer has been linked to a variety of risk factors, including physical inactivity (Wolin et al. 2009); obesity (Renehan et al. 2008); low calcium levels (Cho et al. 2004); and alcohol intake (Thun et al. 1997). Risk for colorectal cancer also increases for persons with a family history of colorectal cancer or polyps (Fuchs et al. 1994). Finally, a high-meat diet and a diet low in vegetables, fruits, or folate (World Cancer Research Fund/American Institute for Cancer Research 2007) have been implicated.

Conversely, several factors are consistently associated with a reduced risk of colorectal cancer, including the use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs). Aspirin use of 10–20 years is associated with a decreased risk of colorectal cancer mortality (Flossmann and Rothwell 2007), and short-term or current use of hormone replacement therapy (HRT) reduces risk in women (Rossouw et al. 2002). In addition, higher levels of vitamin D may protect against adenomatous polyps and incidence, recurrence, and death from colorectal cancer (Ng et al. 2009; Giovannucci 2010). Calcium supplementation reduces the risk of recurrent polyps (Baron et al. 1999).

The hypothesis that prolonged cigarette smoking may increase the risk of colorectal cancer gained support in the mid-1990s when epidemiologic studies, particularly cohort studies, showed a high incidence of adenomatous polyps and/or colorectal cancer in long-term smokers (Giovannucci et al. 1994a,b). Initially, there was concern that this observed association reflected uncontrolled confounding factors, such as lifestyle characteristics, as well as differences in risk between colon and rectal cancer, which are often combined in epidemiologic studies. Subsequent studies suggested a stronger relationship between smoking and rectal cancer than between smoking and colon cancer (Terry et al. 2002b; Wei et al. 2004). This difference was confirmed in two meta-analyses that were limited to prospective cohort studies (Liang et al. 2009; Tsoi et al. 2009) and one that included both case-control and cohort study data (Botteri et al. 2008a). In the latter systematic review, Botteri and colleagues searched the literature through May 2008 and evaluated data from six studies that compared the association of smoking and colon cancer separately from smoking and rectal cancer mortality. The RRs of ever smokers and current smokers were significantly higher for rectal cancer mortality than for colon cancer (rectal cancer: ever vs. never smoker, RR = 1.4 [1.2−1.7], current vs. never smoker, RR 1.6 = [1.3−1.8], colon cancer: ever vs. never smoker, RR = 1.2 [1.0−1.4], current vs. never smoker, RR = 1.2 [1.1−1.3]) (Botteri et al. 2008a).

Epidemiologic Evidence

Adenomatous Polyps

Botteri and colleagues (2008b) used rigorous search and data extraction techniques to synthesize the evidence for an association between smoking and the risk of adenomatous polyps. Among articles published from 1988–2007, they evaluated 125 in detail; these studies were conducted in countries around the world. Combined data from 33 studies found that current smokers had a significantly increased risk of adenomas (RR = 2.14; 95% CI, 1.86–2.46) (Figure 6.23). Among current smokers, the pooled RR estimates were somewhat greater (RR = 2.02; 95% CI, 0.62–6.56) for larger adenomas (≥10 millimeters [mm]) and those classified as high risk (RR = 2.04; 95% CI, 1.56–2.66). In addition, in a comparison with never smokers in 27 studies, former smokers had a significantly increased risk of adenomas (RR = 1.47; 95% CI, 1.29–1.67) (Figure 6.24). Finally, for every additional 10 pack-years of smoking, ever smokers had a 13% increase in risk of adenomatous polyps (95% CI, 9–18%). An evaluation for publication bias by Botteri and colleagues (2008b) showed no indication of such bias for the reporting of results about current smokers, but there was evidence for reports related to former and ever smokers.

Figure 6.23

Forest plot of relative risk for colorectal adenoma for current smokers versus never smokers. Source: Adapted from Botteri et al. 2008b, with permission from Elsevier, © 2008. Note: Partial endoscopy group is composed of studies in which some (more...)

Figure 6.24

Forest plot of relative risk for adenomatous polyps for former smokers versus never smokers. Source: Adapted from Botteri et al. 2008b, with permission from Elsevier, © 2008. Note: Partial endoscopy group is composed of studies in which some or (more...)

Colon and Rectal Cancer

Table 2.27 of the 2004 Surgeon General's report presented data from cohort studies of incidence and mortality for colon and rectal cancer among men and women in the United States (USDHHS 2004). Data published through 2000 and summarized in the 2004 Surgeon General's report consistently indicated that current smokers had an increased risk of colon cancer (the RRs ranged from 1.2–1.4) and of rectal cancer (RRs ranged from 1.4–2.0), regardless of the number or types of covariates for which there was adjustment.

Table 6.5S summarizes the 19 prospective cohort studies on smoking and the incidence of colorectal cancer that were published from 2002–2009. In the first study listed, Terry and colleagues (2002b) followed 89,835 Canadian women for a mean of 10.6 years and confirmed 363 cases of colon cancer and 164 of rectal cancer. The RR for rectal cancer for women with a smoking duration of 30–39 years was 1.52 (95% CI, 1.01–1.26)²; for women with duration of 40 or more years, the RR was 2.27 (95% CI, 1.06–4.87).

Tiemersma and colleagues (2002) followed 36,000 Dutch men and women who were 20–59 years of age at enrollment. At the end of follow-up (8.5 years), the investigators confirmed 102 cases of colorectal cancer. The relationship between smoking and risk for colorectal cancer was null among current smokers but significant among two groups of former smokers (durations of 16–30 and >30 years). In a U.S.-based study, Limburg and colleagues (2003) followed 34,467 women who were 55–69 years of age at baseline. The study confirmed 869 cases of colorectal cancer; duration of smoking was significantly related to risk of colorectal cancer incidence.

Per Table 6.5S, Otani and colleagues (2003) followed 90,004 Japanese men and women who were 40–69 years of age at enrollment. When the analysis was limited to invasive cases, there was a significant increase in risk among current smokers (RR = 1.6; 95% CI, 1.1–2.1) that was comparable to results when the analysis included all cases of invasive and noninvasive colon and rectal cancers.

In Japan, Shimizu and colleagues (2003), who followed 29,051 men and women for 8 years, confirmed 181 cases of colon cancer and 95 of rectal cancer. Among men, no trend was revealed between the risk of colon cancer and lifetime smoking (in pack-years), but for rectal cancer, the risk was significantly greater with more than 20 pack-years (RR = 2.44; 95% CI, 1.12–5.30) than it was for nonsmokers. In The Netherlands, a study by van der Hel and colleagues (2003a), which followed a cohort of 27,222 women, identified 249 cases of colorectal cancer. Ever smoking was similarly related (but not significantly) to colon cancer (RR = 1.36; 95% CI, 0.97–1.92) and to rectal cancer (RR = 1.31; 95% CI, 0.76–2.25).

Wakai and colleagues (2003), who followed a Japanese cohort of 25,260 men and 34,619 women for an average of 7.6 years, confirmed 408 cases of colon cancer and 204 cases of rectal cancer. Among both men and women, there was no relationship between years of smoking and risk of colon cancer or rectal cancer. In the United Kingdom, the Oxford Vegetarian Study, which followed a cohort of 11,140 vegetarians (Sanjoaquin et al. 2004), confirmed 95 cases of colorectal cancer and found that risk was elevated among both former and current smokers. In Europe, The Netherlands Cohort Study on Diet and Cancer followed 58,279 men and 62,573 women (Lüchtenborg et al. 2005); during the last 5.0 years of the 7.3-year follow-up, the study identified 661 cases of colorectal cancer. The risk of colorectal cancer was elevated among former smokers (RR = 1.30; 95% CI, 1.03–1.65) but not current smokers. In Asia, Yun and colleagues (2005) followed the Korean National Health Insurance Corporation cohort of 733,134 men and identified 417 cases of colon cancer and 453 cases of rectal cancer. The risk of colon cancer was elevated among former smokers but not current smokers, while there were no significant findings for rectal cancer. In the United States, Berndt and colleagues (2006) followed 22,887 participants in the Campaign Against Cancer and Heart Disease (CLUE II) cohort from Washington County, Maryland, and confirmed 250 cases of colorectal cancer. Compared with never smokers, ever smokers in the CLUE II cohort had an increased risk of colorectal cancer that failed to reach statistical significance (RR = 1.23; 95% CI, 0.91–1.66). This analysis adjusted for age and gender but not for other risk factors for colorectal cancer.

In Korea, Kim and colleagues (2006), who followed a cohort of 14,103 men and women, confirmed 100 cases of colorectal cancer. These investigators found that duration of smoking was significantly related to risk of colorectal cancer: for those who had smoked more than 45 years, the RR was 2.35 (95% CI, 1.16–4.74) in a comparison with never smokers. Also in Asia, Akhter and colleagues (2007) followed a cohort of 25,279 Japanese men (40–64 years of age at baseline) for a mean of 7 years and identified 188 cases of colorectal cancer. These researchers observed a significant increase in risk among former smokers and a statistically insignificant, modestly increased risk among current smokers. Both age at initiation and duration of smoking were related to risk. In the United States, Paskett and colleagues (2007) analyzed data from 146,877 participants in the Women's Health Initiative (WHI). After nearly 8 years of follow-up, the study confirmed 1,075 cases of colon cancer and 176 cases of rectal cancer. The study did not find a significant relationship between smoking and risk of colon cancer, but current smokers had a significantly elevated risk of rectal cancer (RR = 1.95; 95% CI, 1.10–3.47). Duration of smoking was associated with risk of colon cancer (p-trend = 0.03) and rectal cancer (p-trend = 0.05).

Among a cohort of Chinese men and women in Singapore, Tsong and colleagues (2007) confirmed 516 cases of colon cancer and 329 cases of rectal cancer during a mean follow-up of 11 years. In this cohort, both current and former smoking were related to risk of rectal cancer but not to risk of colon cancer. Similarly, age at initiation and duration of smoking were related to risk of rectal cancer but not to risk of colon cancer. In the United States, a study by Driver and colleagues (2007) reported on follow-up results for male physicians in the Physicians' Health Study; after 20 years of follow-up, there were 381 confirmed cases of colon cancer and 104 confirmed cases of rectal cancer. Overall, ever smoking was related to risk of colorectal cancer (RR = 1.42; 95% CI, 1.17–1.72). In addition, current smokers who smoked two packs per day had an increased risk of colon cancer (RR = 1.53; 95% CI, 1.02–2.29) and rectal cancer (RR = 1.92; 95% CI, 1.01–3.66). In Maryland, Hooker and colleagues (2008) evaluated incidence of rectal cancer in two cohorts of residents from that state's Washington County. In the cohort that was followed from 1963 to 1978, there was a significant increase in risk of rectal cancer among current male smokers but not among their female counterparts. The RR for rectal cancer in the cohort followed from 1975 to 1994 ranged from 1.57 to 1.92 for current and former smokers, but only the RR for former female smokers (1.87; 95% CI, 1.02–3.45) reached significance.

Also in the United States, Hannan and colleagues (2009) studied 184,187 men and women as part of the Nutrition cohort of the CPS-II. After 13 years of follow-up, the study confirmed 1,962 cases of colorectal cancer. Current smokers had an increased risk of colorectal cancer (RR = 1.27; 95% CI, 1.06–1.52), as did former smokers (RR = 1.23; 95% CI, 1.11–1.36). Among current smokers, the RR was greatest for those with a long duration of smoking. RR was comparable between men and women. Finally, a study by Gram and colleagues (2009) followed 68,160 women in Norway and confirmed 425 cases of colorectal cancer. Duration of smoking was significantly related to overall risk of colorectal cancer, but when individual sites were evaluated, sparse data limited the power to find significant associations. Increasing pack-years smoked was related to increased risk of colorectal cancer.

Table 6.6S summarizes 16 case-control studies published from 2001–2008; here the findings are mixed, with only a few studies reporting significant increases in risk associated with various measures of smoking. The studies were carried out in diverse locations, including Asia, North America, and Europe. Sample sizes ranged up to 2,000 cases and adjustments were made for a variety of risk factors.

Table 6.7S presents details on nine cohort studies that reported mortality data for either colorectal cancer overall or separately for colon and rectal cancer. The cohort studies of mortality also came from North America, Asia, and Europe. In several studies, risk for death from colorectal cancer was significantly increased; for example, in two studies among women in the United States—the Nurses' Health Study (NHS) (Kenfield et al. 2008) and the Iowa Women's Health Study (Limburg et al. 2003)—current smokers have an approximate 60% increased risk of colorectal cancer mortality. Several of these studies summarized in Table 6.7S also observed significant increases in risk based on number of cigarettes smoked per day or total pack-years.

Most of these studies were summarized in the three separate meta-analyses referenced earlier in this chapter (Botteri et al. 2008a; Liang et al. 2009; Tsoi et al. 2009). Notably, the meta-analysis by Botteri and colleagues (2008a) combined data from 53 studies (33 prospective cohort and 20 case-control) that were published from 1980–2008 and further characterized the association of smoking with colorectal cancer. Drawing on 47 of those studies, the authors found that former smokers had an increased risk of colorectal cancer (RR = 1.17; 95% CI, 1.11–1.22) in comparison with never smokers. In addition, based on 25 of the studies, ever smokers had an increased risk of colorectal cancer (RR = 1.18; 95% CI, 1.11–1.25) compared with never smokers. This meta-analysis also evaluated risk for colorectal cancer mortality; based on 14 and 12 studies, respectively, current smokers (RR = 1.28; 95% CI, 1.15–1.42) and former smokers (RR = 1.23; 95% CI, 1.14–1.32) had an increased risk of mortality from colorectal cancer in a comparison with never smokers (Botteri et al. 2008a). The increased mortality could reflect a higher incidence of colorectal cancer in smokers or an unfavorable effect on the disease's natural history.

Prostate Cancer

Among American men, prostate cancer is the most commonly diagnosed cancer and the second leading cause of cancer death. In 2013, 238,590 American men were expected to be diagnosed with prostate cancer and 29,720 were expected to die from this disease (Siegel et al. 2013). Since the mid-1990s, death rates for prostate cancer have been declining, but incidence rates have fluctuated (Siegel et al. 2013). The decline in death rates has been attributed to the combination of earlier detection and advances in the treatment of men who are in advanced stages of the disease (Etzioni et al. 2008); the fluctuation in incidence may be due to trends in prostate-specific antigen (PSA) testing.

To date, several risk factors for prostate cancer have been identified with certainty; these risk factors cannot be modified:

• Age. The risk of prostate cancer increases with age.
• Race. Prostate cancer incidence and death rates are highest among African American men and lowest among Asian men.
• Family history. Men who have a father or brother diagnosed with prostate cancer are twice as likely to be diagnosed with prostate cancer as those with unaffected fathers and brothers.

Unlike the case in breast and colon cancer, research has not yet identified the inherited mutations in genes that consistently explain the strong family associations found in prostate cancer, but some studies have discovered a small number of common variants across the genome that are associated with the risk for this disease (Eeles et al. 2008, 2009; Thomas et al. 2008).

Biologic pathways influencing prostate cancer involve hormones and growth factors. Androgens and their signaling pathways are necessary for the development of prostate cancer. Support for the role of these pathways is based on results of two trials showing that drugs inhibiting 5-α-reductases, the enzymes that convert testosterone to the more androgenic dihydrotestosterone, reduce the risk of prostate cancer (Thompson et al. 2003; Andriole et al. 2010). In epidemiologic studies, however, circulating levels of androgens have not been associated with the risk of prostate cancer (Roddam et al. 2008a). Growth factors are also important: for example, results from cohort studies have consistently associated circulating levels of insulin-like growth factor-1 with increased risk for prostate cancer (Roddam et al. 2008b). Research on pathways may provide insights into etiologic factors.

In terms of modifiable risk factors, obesity is associated with an increased risk of death from prostate cancer (Calle et al. 2003), but evidence for an association between risk for incident prostate cancer and physical inactivity is not consistent (Friedenreich and Thune 2001). Drinking alcohol does not appear to be an important factor for prostate cancer incidence or mortality (Velicer et al. 2006; Gong et al. 2009; Chao et al. 2010). Some studies have found a higher risk of prostate cancer or advanced disease with a higher intake of energy (calories), processed meat, dairy foods, and calcium, as well as lower intake of tomatoes and cruciferous vegetables (Giovannucci et al. 2007; World Cancer Research Fund 2007). Regarding prevention, two studies found reduced risk of prostate cancer as a secondary endpoint. In one study, persons who had skin cancer and lived in areas with low levels of selenium in the soil received selenium supplements (Clark et al. 1998); in the other study, men who were current or former smokers received vitamin E (Alpha-Tocopherol 1994). However, in a subsequent trial designed to test the hypothesis that supplementation with these agents would reduce the risk of prostate cancer, Lippman and colleagues (2009) found that supplementation with selenium or with vitamin E did not reduce risk in men who were not selected for exposure to selenium or smoking status.

Epidemiologic Evidence

Incidence and Mortality

More than 30 prospective studies have investigated the link between smoking and incidence of prostate cancer or death from that disease; Table 6.8S summarizes the findings from studies that reported rates, risks, or RRs of prostate cancer associated with cigarette smoking. Of note, Table 6.8S presents updated findings from 8 studies that have examined five cohorts over time (see notes a–f in Table 6.8S). Epidemiologic studies of the association between cigarette smoking and prostate cancer incidence and mortality have been reviewed previously (Colditz 1996; Lumey 1996; Hickey et al. 2001; Levi and La 2001; Zu and Giovannucci 2009), including an Australian consensus conference report (Colditz 1996). To date, the association between cigarette smoking and prostate cancer has not been found to be modified by polymorphisms of genes that are important in the detoxification of carcinogens found in tobacco smoke, including GSTM1, GSTT1, and NAT2 (Gertig et al. 1998; Slattery et al. 1998). However, some studies indicate association of xenobiotic metabolism gene SNPs with colorectal cancer and smoking (Nisa et al. 2010; Koh et al. 2011; Osawa et al. 2012; Fu et al. 2013). Meta-analyses of prospective studies (Huncharek et al. 2010) and case-control studies (Lumey 1996) have also been conducted. In the pooled analysis of data from 24 cohort studies, Huncharek and colleagues (2010) reported some evidence of increased risk for incident prostate cancer (RR = 1.04; 95% CI, 0.87–1.24) among current smokers. The elevated risk was significant in data stratified by amount smoked (cigarettes per day: RR = 1.22; 95% CI, 1.01–1.46; pack-years of smoking: RR = 1.11; 95% CI, 1.01–1.22). Increased risk of deaths from prostate cancer was also found among current smokers (RR = 1.14; 95% CI, 1.06–1.19) (Huncharek et al. 2010).

Twenty-one of the 35 prospective studies reviewed in Table 6.8S did not support a positive association between cigarette smoking and risk (incidence) of prostate cancer. Four of the 35 studies supported positive associations (Whittemore et al. 1984; Hiatt et al. 1994; Adami et al. 1996; Cerhan et al. 1997), and 10 produced either null associations or findings that appeared to indicate inverse associations. Beyond the studies summarized in Table 6.8S, a nested case-control study by Heikkilä and colleagues (1999) did not reveal a baseline difference in the prevalence of current smoking between incident prostate cancer cases and controls. In another study, in a comparison with the general population, Malila and colleagues (2006) found a higher than expected incidence rate of prostate cancer in the placebo arm of the Alpha-Tocopherol, Beta-Carotene Trial of male smokers (median level of smoking at randomization: 20 cigarettes/day for 36 years): the standardized incidence ratio here was 1.20 (95% CI, 1.06–1.35) (Malila et al. 2006).

In contrast to the lack of a consistent association described above between smoking and incidence of prostate cancer, 12 prospective studies (Hammond and Horn 1958; Akiba and Hirayama 1990; Hsing et al. 1991; Tverdal et al. 1993; Adami et al. 1996; Coughlin et al. 1996; Rodriguez et al. 1997; Giovannucci et al. 2007; Rohrmann et al. 2007; Batty et al. 2008; Watters et al. 2009; Weinmann et al. 2010) of the 20 such studies that evaluated prostate cancer mortality in Table 6.8S supported a modest-to-moderate positive association with smoking. In an investigation not included in Table 6.8S, a prospective cohort study by Eichholzer and colleagues (1999) that used non-smokers with normal levels of vitamin E as a comparison group reported a higher risk of prostate cancer death among men who smoked and had a low plasma concentration of vitamin E (RR = 3.26; 95% CI, 1.27–8.35). In contrast, no difference in risk was found among male smokers who had a normal level of vitamin E.

Unlike associations between smoking and other types of cancer such as neoplasms of the lung, the risk of prostate cancer death does not appear to rise with an increasing number of cigarettes smoked per day, duration of smoking, or total pack-years. However, current or recent smoking (Figure 6.25), rather than smoking in the distant past or a cumulative smoking history, may influence prostate cancer mortality. For example, among studies in Table 6.8S reporting a positive association for smoking, the RR was larger for current smokers than for former smokers (Hsing et al. 1991; Adami et al. 1996; Rodriguez et al. 1997; Giovannucci et al. 2007; Batty et al. 2008; Watters et al. 2009; Weinmann et al. 2010) or was stronger when considering smoking status closer to the time of death from prostate cancer (Hsing et al. 1991; Rohrmann et al. 2007).

Figure 6.25

Prospective cohort studies on the association between current cigarette smoking and prostate cancer mortality. Note: Includes studies reporting a relative risk and 95% confidence interval for current smoking or current number of cigarettes smoked per (more...)

Two reports from Giovannucci and colleagues (1999, 2007) provide further evidence for the importance of relatively recent smoking. In an earlier report from the Health Professionals Follow-up Study (not shown in Table 6.8S), Giovannucci and coworkers (1999) followed participants from 1986 to 1994 and noted 177 prostate cancer deaths in 351,261 person-years. Compared with never smokers, the RR was 1.58 for current smokers at baseline, 1.73 for men who had quit smoking within 10 years of baseline, and 1.04 for those who had quit 10 or more years before baseline. In a later report from the same study, Giovannucci and associates (1999) followed participants from 1986–2002 and noted 312 prostate cancer deaths in 673,706 person-years. Using simple updating of biennially assessed smoking status (rather than baseline smoking status, as in their 1999 report), the authors found that the RR among current smokers, in a comparison with smokers who had quit within 10 years, was 1.41 (95% CI, 1.04–1.91).

Data from some studies do not support the hypothesis that the association between prostate cancer mortality is stronger for current smoking than for former smoking (Doll et al. 2005). Their British Doctors Study, which followed physicians from 1951–2001, noted 878 prostate cancer deaths in 34,439 male physicians. The study recorded updated smoking status in 1957, 1966, 1971, 1978, and 1991. The prostate cancer mortality rate (indirectly standardized for age and study year) did not differ (Table 6.8S) between never smokers (89.4/100,000 men per year), former smokers (80.9), and current smokers (90.0). Despite the overall lack of association among smokers, however, the prostate cancer mortality rate (per 100,000 men per year) increased with the number of cigarettes smoked per day by current smokers (1–14/day = 66.7; 15–24 = 99.6; ≥25 = 113.3), but the p for trend was not significant (0.52) (Table 6.8S).

Ten of the studies in Table 6.8S were not cited in the 2004 Surgeon General's report (Lotufo et al. 2000; Lund Nilsen et al. 2000; Putnam et al. 2000; Allen et al. 2004; Doll et al. 2005; Giovannucci et al. 2007; Rohrmann et al. 2007; Batty et al. 2008; Watters et al. 2009; Weinmann et al. 2010). Of these, 7 reported on cigarette smoking and prostate cancer mortality (Lotufo et al. 2000; Doll et al. 2005; Giovannucci et al. 2007; Rohrmann et al. 2007; Batty et al. 2008; Watters et al. 2009; Weinmann et al. 2010); 4 of the 7 gave quantitative support for a positive association between smoking (3 implicated current smoking) and death from prostate cancer (Rohrmann et al. 2007; Batty et al. 2008; Watters et al. 2009; Weinmann et al. 2010). Two of the 10 studies not cited in the 2004 Surgeon General's report but shown in Table 6.8S (Doll et al. 2005; Giovannucci et al. 2007) were updates of studies included in the 2004 report (Doll et al. 1994; Giovannucci et al. 1999). The findings in the 2004 report of no association with prostate cancer mortality in the British Doctors Study (Doll et al. 1994) and of a positive association in the Health Professionals Study (Giovannucci et al. 1999) were unchanged with additional follow-up.

Stage and Histologic Grade

As shown in Table 6.9S, three studies (Hussain et al. 1992; Roberts et al. 2003; Moreira et al. 2010) investigated the association between smoking and both disease stage and histologic grade at the time of diagnosis or surgical treatment, while two (Daniell 1995; Kobrinsky et al. 2003) looked at smoking and disease stage but not histologic grade. Advanced stage (e.g., local invasion, metastasis to a regional lymph node, metastasis to bone) and high grade (e.g., a high sum of the two Gleason scores given by the pathologist or poorly differentiated cancer at pathologic examination) are indicators of a poor prognosis. Thus, studies about smoking and stage or grade of the cancer are relevant for interpreting the findings of higher mortality in the prospective studies. Cases were ascertained from a clinical setting in three studies (Hussain et al. 1992; Daniell 1995; Roberts et al. 2003), from a regional cancer registry in one (Kobrinsky et al. 2003), and from the SEARCH cohort in the fifth (Moreira et al. 2010). All five studies support the hypothesis that smokers diagnosed with prostate cancer are more likely to have advanced-stage disease or less-well-differentiated disease than men who have prostate cancer and do not smoke. In the only study that evaluated intensity of smoking, risk of extraprostatic disease or high-grade disease increased with number of pack-years of smoking (Roberts et al. 2003).

Progression, Case Fatality, and All-Cause Mortality

Nine studies have investigated the association between smoking and the progression of prostate cancer after diagnosis, death from the disease, or death from all causes in men who have prostate cancer (Table 6.10S). Eight of the studies used a retrospective cohort design, while one (Gong et al. 2008) was a prospective study. Five studies reported on progression, defined as biochemical recurrence/progression/failure, local recurrence/failure, distant failure, or development of hormone-refractory disease (Merrick et al. 2004; Oefelein and Resnick 2004; Pickles et al. 2004; Pantarotto et al. 2007; Moreira et al. 2010). Five studies reported on case fatality (Daniell 1995; Pickles et al. 2004; Jager et al. 2007; Pantarotto et al. 2007; Gong et al. 2008), and five reported on all-cause mortality (Yu et al. 1997; Oefelein and Resnick 2004; Pickles et al. 2004; Jager et al. 2007; Pantarotto et al. 2007). One study reported on death from all causes other than prostate cancer (Gong et al. 2008).

Of the nine studies reported in Table 6.10S, six suggest that in men who have prostate cancer, smoking is associated with a higher risk of progression or death from the disease; these findings were independent of smoking's possible influence on stage or grade. Among men diagnosed with prostate cancer, all-cause mortality appears to be higher in smokers than in nonsmokers. In some studies, many of these deaths were due to prostate cancer because the majority of men had advanced-stage disease (Oefelein and Resnick 2004). In other studies, deaths were more likely due to other causes because the majority of men had localized disease (Pickles et al. 2004; Gong et al. 2008).

Breast Cancer

Breast cancer is the most frequently diagnosed type of cancer, other than nonmelanoma skin cancers, and the second leading cause of cancer death among women (Siegel et al. 2013). Despite an approximate 2% decrease in incidence since 1999 and a 28% decline in breast cancer mortality since 1991 (Jemal et al. 2010a,b), about 211,000 new cases of invasive breast cancer were diagnosed and approximately 40,000 deaths resulted from breast cancer among U.S. women in 2009 (Howlader et al. 2013). The age adjusted incidence and death rates for 2004–2008 were 124/100,000 and 23.5/100,000 women per year, respectively, based on the 17 geographic areas covered by the SEER Program of NCI (Howlader et al. 2013). Average annual incidence rates per 100,000 women varied substantially by race/ethnicity in 2004–2008: 77.9 for American Indians/Alaska Natives, 92.1 for Hispanics, 93.7 for Asians/Native Hawaiian or Other Pacific Islanders, 119.9 for Blacks, and 127.3 for non-Hispanic Whites. Death rates per 100,000 women also varied by race/ethnicity during this period: 12.2 for Asians/Native Hawaiian or Other Pacific Islanders, 15.1 for Hispanics, 17.2 for American Indians/Alaska Natives, 22.8 for non-Hispanic Whites, and 32.0 for Blacks.

The burden of breast cancer morbidity and mortality is high. Thus, researchers have long sought to identify modifiable etiologic factors to prevent and control this disease. Active cigarette smoking and exposure to secondhand smoke have received increasing attention over the past two decades, as clinical studies have detected nicotine and its metabolite cotinine in the breast fluid of nonlactating women (Petrakis et al. 1978; Hill and Wynder 1979), and data from rodent studies have indicated that genotoxic carcinogens in cigarette smoke can induce mammary tumors (el-Bayoumy 1992). Sixty-nine known carcinogens are detectable among the myriad chemicals in tobacco smoke (USDHHS 2004). Adipose tissue of the breast can store lipophilic carcinogens, and these can be locally activated by breast epithelial cells to form DNA adducts (Phillips et al. 2002). The prevalence of carcinogen DNA adducts is reported to be increased in smokers and in women with breast cancer (see “DNA Adducts”). A recent report suggests that nicotine leads to overexpression of cyclin D3 and induces neoplastic transformation and proliferation of breast epithelial cells in vitro (Lee et al. 2010a). Thus, evidence is accumulating for several plausible mechanisms by which smoking may induce breast cancer; this evidence is reviewed in greater detail below.

Historically, the epidemiologic evidence for an association between breast cancer and active cigarette smoking and between breast cancer and exposure to secondhand smoke has been inconsistent, leading to conclusions in the past that smoking is not a risk factor for this type of cancer (Palmer and Rosenberg 1993; Terry and Rohan 2002). However, some recent reviews have concluded that both active and passive smoking may increase the risk of breast cancer, although there is continuing disagreement as to the magnitude of effect (California Environmental Protection Agency [Cal/EPA] 2005; Collishaw et al. 2009 for the Canadian Expert Panel on Tobacco Smoke and Breast Cancer Risk; Institute of Medicine 2012).

Biologic Basis—Evidence for Potential Etiologic Mechanisms

Breast cancer is the end result of a multistep process in which some epithelial cells in the breast undergo a series of mutations. In doing so, these cells escape from programmed cell death and then proliferate and invade surrounding tissue (Armitage and Doll 1957; Fisher 1958; Cairns 1975; Tomlinson et al. 1996). Genetic and epigenetic mutations in critical genes in cells—such as tumor suppressor genes, DNA replication and repair genes, and proto-oncogenes—can lead to the initiation of tumorigenesis. Clones from these mutated cells continue to expand and proliferate, rendering them susceptible to further cancer-causing mutations. For hereditary cancers, as proposed in the Knudson (1996) model, at least two allelic mutations are necessary, one of which might be inherited. Endogenous and exogenous exposures can potentially affect the development and proliferation of mutant cells in both inherited and sporadic breast cancer and thereby affect breast carcinogenesis.

The following section addresses biologic mechanisms by which tobacco smoke, an exogenous exposure, can potentially contribute to the causation of breast cancer. The review in this section addresses the plausibility of a causal association between risk of breast cancer and active or passive smoking. The studies were identified through literature searches using the following key words: smoking and breast cancer, carcinogenesis, DNA adducts, epigenetic, hormones (androgens, progesterones, and estrogens), anti-estrogen hypothesis, and ovarian function. Past Surgeon General's reports were also reviewed: those published in 2004 and 2006, which addressed active and passive smoking, respectively (USDHHS 2004, 2006), and the one in 2010, which focused on mechanisms by which tobacco smoke contributes to disease (USDHHS 2010).

DNA Adducts

Cigarette smoke contains thousands of compounds including 69 known to be carcinogens (USDHHS 2010). Some of these compounds have been shown to cause mammary tumors in rodents (Hecht 2002). Nicotine, one of the major constituents of tobacco smoke, has been measured in the nipple aspirate of female smokers (Petrakis et al. 1978) and smoking-related DNA adducts have been found in the DNA of epithelial cells within breast milk (Thompson et al. 2002), documenting that components of smoke reach breast tissue. Carcinogens in tobacco smoke cause cancer by damaging DNA; this is the initiating event in tumorigenesis (Figure 6.4). Many carcinogens from tobacco smoke are metabolically activated by the cytochrome P-450 (CYP) enzymes, including CYP1A1 and CYP1B1, and by NAT2, all of which are present in breast tissue. These activated metabolites bind to DNA to form DNA adducts that in turn can damage DNA (USDHHS 2010). Elevated levels of DNA adducts have been associated with certain types of cancer, supporting a positive association between increasing levels of DNA adducts and risk of cancer (Phillips 2005). The degree of activation of detoxification enzymes—such as glutathione S-transferases (GSTs), uridine-5′-disphosphate-glucuronosyltransferases (UGTs), epoxide hydrolases, and sulfatases, which are also present in the breast—is important because these enzymes catalyze the excretion of the toxic metabolites, thereby potentially decreasing the formation of DNA adducts. Smoking induces activity of some of these enzymes (USDHHS 2010).

As a biomarker, smoking-related DNA adducts are an integrated measure of exposure to tobacco smoke, metabolic activation, and delivery of the metabolite to DNA in the target tissue (Groopman et al. 1995). Smoking-related DNA adducts can be quantified in breast fluid, tissue, and peripheral blood cells. However, an increase in the levels of DNA adducts does not directly correspond to a similar increase in cancer risk because other processes are involved (Phillips 2005). To causally link the presence of smoking-related DNA adducts to risk of breast cancer, elevated levels ideally need to be detected in breast epithelial cells before the onset of the cancer and at higher levels in those individuals going on to develop cancer than in those who do not. Levels of DNA adducts measured at the time of diagnosis or after diagnosis (e.g., in case-control or cross-sectional studies) may not reflect the etiologically relevant time window of tumor initiation. Similarly, levels of DNA adducts in peripheral cells may not reflect what is happening locally at a specific target site: circulating levels of biomarkers have not always been correlated with levels at the tissue site.

Several studies have evaluated the relationship between smoking and the prevalence of smoking-related DNA adducts in breast tissue (Perera et al. 1995; Li et al. 1996; Rundle et al. 2000). These studies have confirmed the presence of smoking-related DNA adducts in breast tumor cells and adjacent normal epithelial cells in some, but not all, current and former smokers (Perera et al. 1995; Li et al. 1996; Rundle et al. 2000; Faraglia et al. 2003). Some case-control studies have reported high levels of DNA adducts in smokers compared with nonsmokers (Perera et al. 1995; Li et al. 1999; Conway et al. 2002; Li et al. 2002; Rundle et al. 2002). Faraglia and colleagues (2003) conducted a large, comprehensive case-control study that included 148 breast tumor tissues and adjacent normal samples from the Long Island Breast Cancer Study Project. The arylamine 4-aminobiphenyl (4-ABP) DNA adduct was measured using an immunoperoxidase method that had been validated by mass spectrometry. The study's authors observed a significant trend between levels of 4-ABP DNA adducts in normal breast tissue and smoking status, and they measured higher levels of DNA adducts in active and passive smokers than in never smokers. Interestingly, mean levels of DNA adducts were significantly lower in tumor tissue than in adjacent normal tissue.

Elsewhere, circulating levels of PAH-DNA adducts in peripheral blood mononuclear cells were assessed in two sample sets taken 4.5 years apart from the same case-control study (Gammon et al. 2004b). The authors observed a modest association in both sets of samples between the highest PAH-DNA adduct levels and the risk of breast cancer, but they did not observe a dose-response relationship with increasing adduct levels. Furthermore, the strength of the association did not differ between active and passive smokers. To date, no prospective cohort study has incorporated these markers.

Polymorphisms in genes encoding enzymes involved in the metabolic activation and detoxification of toxins, such as those from exposure to cigarette smoke, could also affect breast carcinogenesis by either promoting or preventing the formation of DNA adducts. Firozi and colleagues (2002) observed a significant interaction between levels of DNA adducts in breast tissue and CYP1A1, GSTM1, and NAT2 polymorphisms among ever smokers. These authors also observed higher levels of DNA adducts among smokers with combined CYP1A1*1/*2 or *2/*2 and GSTM1 null genotypes than among smokers with polymorphisms in either genes. In addition, the frequency of smoking-related DNA adducts was higher in those with slow acetylator alleles of the NAT2 gene than in those having rapid acetylator alleles.

Several studies have examined the association between smoking, p53 mutations, and/or protein expression in breast tumors; results have been mixed (Conway et al. 2002; Furberg et al. 2002; Gaudet et al. 2008; Van Emburgh et al. 2008a). Mordukhovich and colleagues (2010), who conducted a large case-control study of 859 cases and 1,556 controls from the Long Island Breast Cancer Study Project, found that women in the study with p53-positive tumors were less likely to have been exposed to cigarette smoke than women without p53 mutations. This finding suggests that smoking may not significantly affect the p53 pathway. In this study, p53 mutations were identified from DNA extracted from paraffin blocks and p53 protein expression was evaluated using immunohistochemistry.

Other Cellular Mechanisms

In addition to forming DNA adducts, constituents of tobacco smoke may contribute to carcinogenesis by promoting cell growth and proliferation through the activation of a number of receptors, such as cyclooxygenase II and prostaglandin E2, and signaling pathways, including Akt and epidermal growth factor receptor (Narayan et al. 2004; Miller et al. 2005; Kundu et al. 2007; Botlagunta et al. 2008; Guo et al. 2008; Connors et al. 2009; Dasgupta et al. 2009). Constituents of tobacco smoke may also cause cells to evade apoptosis after DNA damage by altering cellular response at the mRNA and protein levels (Connors et al. 2009). In addition, cigarette smoke can inactivate tumor suppressor genes via genetic and epigenetic changes (Liu et al. 2010a). Narayan and colleagues (2004) found that cigarette smoke condensate increases levels of GADD45—a gene whose expression is upregulated in response to DNA damage and/or growth arrest in a dose-dependent manner—to increase proliferation of epithelial cells and to induce cell cycle arrest at the synthesis/gap₂/mitosis (S/G₂/M) phase. Furthermore, Dasgupta and colleagues (2009) found that the exposure of human breast cancer cells to nicotine can contribute to epithelial-mesenchymal transition, a collection of changes seen in more advanced cancers that is characterized by loss of cell adhesion, increased cell mobility, and repression of E-cadherin. These mechanistic studies were conducted in cell culture experiments using normal and malignant breast epithelial cell lines, but they have yet to be replicated in an in vivo model.

Hormones

Estrogen's role in the initiation, promotion, and progression of breast cancer is well established through preclinical data, observational studies, and clinical trials (Yager and Davidson 2006). Studies in experimental animal models and cultured human cells demonstrate that estradiol (E2) and estrone (E1) are carcinogenic (Yager and Davidson 2006). Estrogen is thought to exert its carcinogenic effects primarily through two complementary pathways (Figure 6.26). The first pathway involves the activation of signaling pathways via the estrogen receptor (ER), which leads to altered gene expression and increased proliferation and, in turn, the opportunity for more mutations. The second pathway involves the oxidative metabolism of estrogen (E2/E1) to catechol estrogens and then to reactive quinone metabolites. The quinone metabolites have the ability to form depurinating DNA adducts or to form catechols through the oxidation-reduction cycle that produce reactive oxygen species causing oxidative damage to DNA (Lavigne et al. 2001). The catechols can be inactivated by methylation mediated by catechol-O-methyltransferase, glucuronidation, and sulfation. In women, blocking the action of the ER by such agents as tamoxifen, a selective estrogen receptor modulator, or by decreasing estrogen production (e.g., by removing the ovaries in premenopausal women) has been shown to decrease the incidence of breast cancer up to 50% (Fisher et al. 1998; Parker et al. 2009). Estrogen metabolism, which occurs in the liver, kidney, and other organs, including the breast, involves a complex set of pathways (Figure 6.27). Various CYP isoforms, which are often tissue specific, are responsible for the oxidation and conjugation of estrogen metabolites. One of the first steps in estrogen metabolism is the oxidation of the parent estrogens (E2/E1) at the 2, 4, and 16 positions of the carbon skeleton to the 2, 4, and 16 hydroxylated metabolites (Yager and Liehr 1996). Davis and colleagues (1993) showed that the 16 hydroxy estrogens exhibit strong estrogenic and mitogenic activities and hypothesized that higher levels of such activities increase the risk for breast cancer by uncontrolled cellular proliferation and by binding to the ER, thereby damaging DNA. The 2- and 4-hydroxy metabolites also exhibit estrogenic activity and can stimulate cellular proliferation. Despite being more abundant than the 4-hydroxy metabolite, the 2-hydroxy metabolite is much less potent and shorter acting. Both the 2- and 4-hydroxy estrogen metabolites can go on to form genotoxic reactive quinone metabolites.

Figure 6.26

Pathways to estrogen carcinogenesis. Source: Adapted from Yager and Davidson 2006, updated for Surgeon General's Report. Note: 4-OH E1 = 4-hydroxyestrone; 4-OH E2 = 4-hydroxyestradiol; 16α-OH E1 = 16α-hydroxyestrone; E1 = estrone; E2 = (more...)

Figure 6.27

Pathways involved in estrogen metabolism. Source: Adapted from Ziegler et al. 2010.

In cell culture studies of granulosa cells, choriocarcinoma cells, and placental microsomes, nicotine was shown to directly inhibit the aromatase enzyme, resulting in reduced conversion of androgens to estrogen in a dose-dependent manner (Barbieri et al. 1986a,b). This is an important pathway, particularly in postmenopausal women among whom estrogen is generated primarily in peripheral tissues. In animal studies, cigarette smoke reduced the number of oocytes, caused toxicity to ovarian follicles, and led to ovarian atresia (Mattison 1982; Blackburn et al. 1994; Miceli et al. 2005), which could affect estrogen production in premenopausal women.

Observational studies have linked cigarette smoking to earlier age at menopause (Baron et al. 1990; Bromberger et al. 1997) and reduced bone density in postmenopausal women (Daniell 1976; Baron et al. 2001); both conditions are associated with relative estrogen deficiency and a reduction in the risk for breast cancer. Smoking is also associated with decreased fertility (USDHHS 2004, 2010) and with earlier menarche in children whose mothers were heavy smokers during pregnancy (Windham et al. 2004); both conditions are known risk factors for breast cancer. However, as noted in the 2001, 2004, and 2010 Surgeon General's reports, the majority of epidemiologic studies comparing circulating endogenous estrogen levels in premenopausal (Table 6.11S) and postmenopausal women (Table 6.12S) have not found differences between smokers and nonsmokers. In several small studies, premenopausal women who smoked were found to have significantly elevated urinary levels of 2-hydroxy E1 or reduced levels of E1, E2, or estriol (E3) during the luteal phase of the menstrual cycle compared with non-smokers (MacMahon et al. 1982; Michnovicz et al. 1986, 1988; Westhoff et al. 1996). The clinical implications of these findings and any associated changes in breast tissue have not been investigated.

Studies that compared the effect of HRT, an exogenous hormonal exposure, in smokers and nonsmokers did observe differences by smoking status in circulating levels of estrogen and its metabolites, supporting the hypothesis that smoking increases hepatic metabolism of estrogens (Jensen et al. 1985; Jensen and Christiansen 1988; Cassidenti et al. 1990; Geisler et al. 1999). Among postmenopausal women who were using orally administered HRT, circulating estrogen metabolites—including E1, E2, and estrone sulfate—were 40–70% lower in smokers than in nonsmokers (Jensen et al. 1985; Jensen and Christiansen 1988; Cassidenti et al. 1990; Geisler et al. 1999). A dose-dependent, reciprocal increase in the binding capacity of sex-hormone-binding globulin was observed by Cassidenti and colleagues (1990) and, importantly, differences in levels of estrogen and its metabolites were not evident before treatment with HRT in these same women (Jensen et al. 1985; Cassidenti et al. 1990). Furthermore, significant changes in circulating hormone levels between smokers and nonsmokers were not observed after transdermal administration of HRT, a method that bypasses estrogen metabolism in the liver (Geisler et al. 1999; Mueck and Seeger 2005).

Alterations in estrogen metabolism pathways have also been observed in pregnant women who smoked (USDHHS 2001). Several studies have found that pregnant women who smoked had lower levels of circulating E2 and E3 than pregnant women who did not smoke (Targett et al. 1973; Mochizuki et al. 1984; Bernstein et al. 1989; Petridou et al. 1990; Kaijser et al. 2000). However, compared with their nonsmoking pregnant counterparts, rates of 4-hydroxylation were increased in pregnant smokers in samples of placental tissue (Chao et al. 1981; Juchau et al. 1982), and rates of 2-hydroxylation were nonsignificantly increased (Juchau et al. 1982). Smoking did not alter E2 metabolism or the formation of E1, 2-hydroxyestradiol, and other estrogen metabolites, but 15α-hydroxyestradiol, 4-hydroxyestradiol, and 7α-hydroxyestradiol were significantly elevated (Zhu et al. 2002). Finally, Piasek and colleagues (2001) found that levels of progesterone were lower in pregnant women who smoked than in those who did not smoke. If the rate of 4-hydroxylation continues to be higher after pregnancy in smokers than in nonsmokers, then smoking may increase risk for breast cancer rather than having a protective effect, as suggested by the anti-estrogenic hypothesis proposed by Michnovicz and colleagues (1986).

Several other circulating hormones have also been compared between smokers and nonsmokers. In premenopausal women, Cramer and colleagues (1994) and Windham and colleagues (2005) did observe higher levels of circulating follicle-stimulating hormone in smokers than in nonsmokers (Table 6.11S). Last, circulating levels of androgens (e.g., androstenedione, dihydroepiandrosterone sulfate, and testosterone), progesterone, and cortisol have been found to be higher in smokers than in nonsmokers. In postmenopausal women, these elevated levels may affect breast carcinogenesis. Missmer and colleagues (2004) associated increased levels of circulating androgens with increased risk for breast cancer among postmenopausal women. A meta-analysis by Law and colleagues (1997) found that levels of dihydroandroepiandosterone sulfate and androstenedione were significantly higher in postmenopausal smokers than in nonsmokers but that levels of estrogens did not differ. Finally, cigarette smoking has been shown to directly affect adrenal cortical hormone levels (Baron et al. 1995). The effects of these hormonal changes on breast tissue are not known.

Summary

The available evidence supports biologically plausible mechanisms, particularly for DNA adduct formation and unrepaired DNA mutations, by which exposure to tobacco smoke could cause breast cancer. However, data are limited and a detailed mechanistic model of how exposure to tobacco smoke may affect risk for breast cancer cannot yet be assembled.

Active Cigarette Smoking and Risk for Breast Cancer

Individual authors and various review panels have evaluated the evidence for an association between active and passive cigarette smoking and breast cancer. The first systematic review of such an association was included in IARC Monograph 38 (1986). Based on a review of 10 case-control and 8 cohort studies published between 1959 and 1983, the 1986 IARC monograph found “no consistent effect of smoking on the risk of breast cancer” (p. 298). The literature at the time was limited, however. Only 2 of the case-control studies (CDC 1983; Janerich et al. 1983) were population-based, rather than hospital-based, and few studies adjusted for potential confounders. All but 1 cohort study (Hiatt et al. 1982) mixed incident and decedent cases and few adequately adjusted for relevant confounders. Palmer and Rosenberg (1993) reviewed 5 cohort and 16 case-control studies (9 with population controls, 3 with participants in a screening program, and 4 with hospital controls), finding “little evidence to suggest that cigarette smoking materially increases risk” (p. 154). However, the authors noted that future investigations should consider age at initiation of smoking because of evidence that women were beginning to smoke at earlier ages. Terry and Rohan (2002) published a comprehensive literature review on cigarette smoking and breast cancer, concluding that “the association between cigarette smoking and breast cancer risk remains unclear” and that the observed “increased risk with smoking of long duration, smoking before a first full-term pregnancy, and passive smoking require (sic) confirmation in future epidemiological studies” (p. 965). They suggested that future studies and meta-analyses consider timing of exposure (e.g., age at initiation of smoking and smoking before first pregnancy), duration and dose (years of exposure and pack-years of smoking), sources of passive exposure, the overlap of active and passive exposures, potential confounders, and modification by menopausal status and genetic susceptibility.

IARC (2004) summarized results from 36 case-control studies, 8 cohort studies, and a large pooled analysis of data from 10 cohort and 43 case-control studies, the pooled analysis having been conducted by the Collaborative Group on Hormonal Factors in Breast Cancer and colleagues (2002) and based on studies having at least 100 women with incident invasive breast cancer. The pooled analysis was restricted to nondrinkers (38% of cases and 43% of controls), because alcohol was considered a potentially significant confounder of the effects of smoking. Sufficient data were available to consider a wide variety of other potential confounders, including age at diagnosis, parity, age at birth of first child, breastfeeding, race, country, education, family history, age at menarche, height, weight, body mass index (BMI), use of hormonal contraceptives, and menopausal status. Study site, age, parity, and age at first birth were included as covariates in the final analysis of the effect of smoking on risk of breast cancer among nondrinkers. However, the analysis did not consider duration or amount of smoking or exposure to secondhand smoke. Results indicated no association between active smoking and risk for breast cancer (RR = 1.03; 95% CI, 0.98–1.07) in women who did not drink alcohol. The Collaborative Group (2002) also contrasted this result with those for all women regardless of alcohol intake (RR = 1.09) and statistically adjusted for alcohol intake (RR = 1.05). The 2004 IARC report concluded that: (a) the majority of epidemiologic studies “found no association with active smoking, after controlling for established risk factors”; and (b) the Collaborative Group analysis of women who reported themselves to be nondrinkers “confirms the lack of an increased risk of breast cancer associated with smoking” (p. 1183). The Cal/EPA reviewed many of the same studies in 2005 and came to a different conclusion: “Considering the epidemiological studies, the biology of the breast and the toxicology of tobacco smoke constituents together, the data provide support for a causal association between active smoking and elevated breast cancer risk” (p. 7-79).

In April 2009, the Canadian Expert Panel on Tobacco Smoke and Breast Cancer Risk conducted an extensive descriptive evaluation of active cigarette smoking and exposure to secondhand smoke, paying particular attention to the timing of these exposures (age at initial exposure and before or during first full-term pregnancy), duration and dose (years of exposure and number of pack-years of smoking), modification by menopausal status, and genetic susceptibility (Collishaw et al. 2009). The panel's approach, to some extent, followed the suggestions of Terry and Rohan (2002) that future studies and meta-analyses focus more carefully on the issues of duration, timing, genetic susceptibility, source of passive exposure, the overlap of passive and active exposure, and potential confounders. The evaluation included summative reviews, meta-analyses, and the most recently published studies through November 2008. Pooled analyses and meta-analyses were not performed. The evaluation paid particular attention to results from the 2002 analysis by the Collaborative Group on Hormonal Factors in Breast Cancer, the 2005 Cal/EPA report, and the 2004 and 2006 Surgeon General's reports.

The Canadian Expert Panel evaluated results from more recent, updated analyses published for four of the cohort studies and nine of the case-control studies that were included in the 2002 Collaborative Report in which duration of smoking was reported. Unlike the 2002 report, which excluded women who consumed alcohol, the Canadian panel reported risk estimates adjusted for alcohol intake. The four cohort studies included the NHS-I (Egan et al. 2002), the Canadian National Breast Screening Study (Cui et al. 2006), the CPS-II (Calle et al. 1994), and the Iowa Women's Health Study (Olson et al. 2005). Three of these (Calle et al. 1994; Olson et al. 2005; Cui et al. 2006) reported significantly increased RRs, ranging from 1.18–1.50, for the longest duration of smoking (≥40 years). Among the nine case-control studies (Rohan and Baron 1989; Palmer et al. 1991; Smith et al. 1994; Baron et al. 1996; Johnson et al. 2000; Kropp and Chang-Claude 2002; Alberg et al. 2004; Magnusson et al. 2007; Prescott et al. 2007), five reported an increase in risk of greater than 45% for smoking durations ranging from 11 to more than 50 years and for high cumulative levels of pack-years or cigarette-years³ (Rohan and Baron 1989; Palmer et al. 1991; Johnson et al. 2000; Kropp and Chang-Claude 2002; Alberg et al. 2004). However, results were statistically significant only for postmenopausal women who reported more than 35 years of active smoking (OR = 1.7; 95% CI, 1.1–2.7) in one study (Johnson et al. 2000).

The Canadian Expert Panel also evaluated three cohort studies published after 2002 in which the risk of breast cancer was significantly increased for the longest durations of active smoking, ranging from 20 or more years to 31 or more years (Al-Delaimy et al. 2004; Reynolds et al. 2004b; Gram et al. 2005). According to the Canadian Expert Panel, when these studies were considered along with three of the four older cohort studies (Egan et al. 2002; Olson et al. 2005; Cui et al. 2006) (Calle et al. 1994 was excluded because it was a mortality study), five reported an increased risk for the highest duration category of smoking: two with borderline significance (RR = 1.15 [95% CI, 1.00–1.33]; 1.18 [95% CI, 1.00–1.38]) (Reynolds et al. 2004b; Olson et al. 2005, respectively) and three with statistical significance (RR = 1.21 [95% CI, 1.01–1.45]; 1.36 [95% CI, 1.1–1.7]; and 1.50 [95% CI, 1.19–1.89]) (Al-Delaimy et al. 2004; Gram et al. 2005; Cui et al. 2006, respectively). However, it should be noted that the result used for the Gram study is based on a subgroup of women who reported ever smoking for at least 20 years. The result for all current smokers with 25 or more years of smoking was increased but not statistically significant (RR = 1.26; 95% CI, 0.98–1.63). Although four of these five studies reported statistically significant trends across levels of duration (Olson did not calculate a p for trend), only three (Gram et al. 2005; Olson et al. 2005; Cui et al. 2006) actually showed unambiguous evidence of an increasing trend with duration of active smoking. The panel also reviewed four cohort studies published after 2002 that reported risk estimates by pack-years of smoking (Reynolds et al. 2004b; Gram et al. 2005; Olson et al. 2005; Cui et al. 2006). Among these studies, three had statistically significant RRs ranging from 1.17–1.48 for the highest category of pack-years (Reynolds et al. 2004b; Gram et al. 2005; Cui et al. 2006). Additionally, the panel reviewed 32 case-control studies in which ORs were reported for duration of active smoking and 27 in which estimates were reported for pack-years. The results from these case-control studies were found to be inconsistent, regardless of menopausal status. The Canadian Expert Panel concluded that the results from the cohort studies for increased risk with longer duration and higher pack-years were more “persuasive” than those from the case-control studies and “that the relationship between active smoking and breast cancer is consistent with causality” (Collishaw et al. 2009, p. 49). Johnson and colleagues (2011) summarized the results from the Canadian Expert Panel in a brief report.

In November 2009, IARC issued a special report on human carcinogens, including tobacco, that encompassed more than 150 epidemiologic studies about the association between tobacco smoke and breast cancer (Secretan et al. 2009). This report updated findings and conclusions from the 2004 IARC report and noted that two large cohort studies conducted after 2002 showed positive, but small, statistically significant associations. These studies included the California Teachers Study (hazard ratio [HR] = 1.13; 95% CI, 1.0–1.28) (Reynolds et al. 2004b), which was also reviewed in the 2006 Surgeon General's report, and the Canadian National Breast Screening Study (HR = 1.18; 95% CI, 1.09–1.27) (Cui et al. 2006). Based on these findings and those from previous reports, as well as evidence from studies of animal and human tissues, the IARC panel concluded that “there is limited evidence that tobacco smoking causes breast cancer” (Secretan et al. 2009, p. 1033) and added the female breast as a new cancerous tumor site associated with exposure to tobacco smoking.

In addition to these extensive reports, several reviews and meta-analyses have addressed active cigarette smoking alone (Khuder and Simon 2000; Khuder et al. 2001; Nagata et al. 2006; Ren et al. 2007), exposure to secondhand smoke but not active smoking (Lee and Hamling 2006; Pirie et al. 2008), both active and passive smoking (Morabia 2002b; Johnson 2005; Sadri and Mahjub 2007; Iwasaki and Tsugane 2011), smoking-genotype interactions (Vogl et al. 2004; Masson et al. 2005; Terry and Goodman 2006; Ochs-Balcom et al. 2007; Ambrosone et al. 2008; Zhang et al. 2010), smoking-DNA repair marker interactions (Neumann et al. 2005), timing in relation to first pregnancy or birth of first child (Lawlor et al. 2004; DeRoo et al. 2011b), and intrauterine exposure (Park et al. 2008).

Conclusions from Previous Surgeon General's Reports

The 2001 Surgeon General's report on women and smoking concluded that “active smoking does not appear to appreciably affect breast cancer risk overall,” but it suggested that future research address both age at initiation of smoking and potential susceptibility associated with specific genetic polymorphisms (p. 217). The 2004 Surgeon General's report on the health consequences of smoking evaluated: (a) the influence that cigarette smoking has on endogenous estrogen levels due to changes in metabolism and lowered body weight; (b) the effects of early age at smoking initiation, smoking-genotype interactions, and exposure to secondhand smoke; and (c) carcinogenic and anti-estrogenic effects of smoking on breast tissues.

The 2004 Surgeon General's report concluded that “evidence is suggestive of no causal relationship between active smoking and breast cancer,” that subgroups of women at high risk because of smoking could not be “reliably identified,” and that the previous finding of a lower risk for breast cancer among women with BRCA1 or BRCA2 mutations in one study (Brunet et al. 1998) “was not replicated” in a later study (Couch et al. 2001) and therefore not established (USDHHS 2004, p. 312).

The sections below review and quantitatively summarize studies of cigarette smoking by study design (cohort, case-control), and by geographic regions (North America, Europe, Asia) that differ for smoking prevalence, as well as breast cancer incidence and mortality. Table 6.13 shows selected estimates of the prevalence of smoking from the WHO Reports on the Global Tobacco Epidemic (2008a, 2011) for countries represented in these reports. Although there is considerable variation, the prevalence of smoking in women is generally similar in North America and Europe but substantially lower in Asia.

Table 6.13

Age-standardized estimates of the prevalence of current cigarette smoking for selected member states of the World Health Organization (WHO), 2009.

The following sections include reports on the association between smoking and breast cancer risk based on cohort studies published up to 2012 (Table 6.14S) and case-control studies published from 2000–2011 (Table 6.15S). A list of studies by category of exposure is provided in Table 6.16S. Studies based on incident cases that estimate risk of breast cancer are emphasized because studies that focus on mortality may include a different mix of correlates and etiologic pathways affecting survival that alter the association with smoking (Al-Delaimy et al. 2004). As a result, studies of smoking and breast cancer mortality are evaluated in a separate section (see “Exposure to Tobacco Smoke and Breast Cancer Mortality”). Some studies or reviews that mix prevalent with incident cases, however, are included (Lawlor et al. 2004; Hanaoka et al. 2005; Ha et al. 2007).

Cohort Studies

Table 6.14S presents an overview of 15 publications from the 12 cohort studies on breast cancer and active smoking published since 2000 (Manjer et al. 2000b, 2001; Egan et al. 2002; Terry et al. 2002a; Al-Delaimy et al. 2004; Lawlor et al. 2004; Reynolds et al. 2004b; Gram et al. 2005; Hanaoka et al. 2005; Olson et al. 2005; Cui et al. 2006; Ha et al. 2007; Lin et al. 2008; Xue et al. 2011; Luo et al. 2011b). The study by Lawlor and colleagues (2004) was restricted to parous women in the United Kingdom and combined prevalent and incident cases. The report by Manjer and colleagues (2001) was based on the same cohort as used in an earlier report by Manjer and colleagues (2000b), but was restricted to women with tumor tissue available for analysis. Consequently, Lawlor and colleagues (2004) and Manjer and colleagues (2001) are excluded from the meta-analyses and forest plots. Additionally, reports by Terry and colleagues (2002a) and Cui and colleagues (2006) were based on the same cohort, the Canadian National Breast Screening Study. Although Table 6.14S summarizes both studies, estimates only from Cui and colleagues (2006) are used in the meta-analyses to avoid duplication. Two reports stem from the NHS-I (baseline year 1976) (Egan et al. 2002; Xue et al. 2011); data from the more recent report are used in the majority of the meta-analyses. Data from the NHS-II are included because it is a separate premenopausal-women-only cohort with a different baseline year (1989) (Al-Delaimy et al. 2004). All three of these studies are summarized in Table 6.14S.

North American Studies

The U.S. Radiologic Technologists Study (USRTS) (Ha et al. 2007) reported nonsignificantly increased RRs for breast cancer among former smokers (RR = 1.17; 95% CI, 0.99–1.38) and current smokers (RR = 1.13; 95% CI, 0.96–1.32). Although the study adjusted for the first year in which an individual worked as a radiologic technician, either residual confounding or synergy may have occurred between smoking and exposure to radiation at work, because previous analyses showed an increased risk (RR = 2.41; 95% CI, 1.32–4.41) associated with exposure to radiation at work among women who were employed before 1940, when exposure to occupational radiation was potentially higher for this group (Mohan et al. 2003; Sigurdson et al. 2003). Eighty-six percent of cases (781 out of 906) were ascertained by self-report, of which 20% (155 out of 781) could not be verified against medical records (Ha et al. 2007), but a previous report from the USRTC indicated 99.4% agreement between self-report and breast cancers ascertained from medical records (Sigurdson et al. 2003). In addition, 14% (125) of the cases were ascertained from death certificates, and date of diagnosis was imputed using the average survival time between 1973 and 2000 based on SEER data.

Several reports from the NHS-I cohort on smoking and breast cancer are based on both premenopausal and postmenopausal women (Willett et al. 1987; London et al. 1989; Hunter et al. 1997; Collaborative Group on Hormonal Factors in Breast Cancer et al. 2002; Egan et al. 2002; Xue et al. 2011). All but two (Egan et al. 2002; Xue et al. 2011) have been incorporated in previous reviews or meta-analyses. The report by Xue and colleagues (2011) analyzed data for 8,772 incident cases of breast cancer in 111,440 women followed for up to 30 years (1976 baseline). The RR was 1.09 (95% CI, 1.02–1.17) for current smokers and 1.06 (95% CI, 1.01–1.11) for former smokers in comparisons with never smokers. These estimates were adjusted for age, family history of breast cancer, age at menarche, height, BMI at 18 years of age, use of oral contraceptives, history of benign breast disease, physical activity, alcohol use, age at first birth, parity, passive smoking at home and at work, current BMI, age at menopause, menopausal status, and use of HRT. Risk was significantly and positively associated with increasing pack-years of smoking (p trend = 0.001), number of cigarettes smoked per day (p trend = 0.02), and duration of smoking (p trend = 0.01), particularly in the intervals between menarche and menopause and menarche and first birth (RR = 1.11; 95% CI, 1.07–1.15; and 1.18; 95% CI, 1.10–1.27 for every 20 pack-years, respectively). The authors also reported a significant trend (p = 0.02) toward decreasing risk with increasing pack-years of smoking after menopause.

In an earlier report from the NHS-I, Egan and colleagues (2002) reported results for a subset of 78,206 premenopausal or postmenopausal women that was restricted to those for whom data on both active and passive smoking were collected and based on an average of 14 years of follow-up (1982 baseline). The RR, based on a reference group of women who reported no history of active or passive smoking, was 1.15 (95% CI, 0.98–1.34) and 1.17 (95% CI, 1.01–1.34) for current and former smokers, respectively. Last, Al-Delaimy and colleagues (2004) analyzed data from the NHS-II cohort (1989 baseline), based predominantly on premenopausal women, and reported a significantly increased risk for breast cancer among former smokers (RR = 1.18; 95% CI, 1.02–1.36) but not current smokers (RR = 1.12; 95% CI, 0.92–1.37) in comparisons with never smokers over an average of 10 years of follow-up. This study also reported a positive association between risk for breast cancer and increasing duration of smoking (p trend = 0.04) and a significantly increased risk for smoking 20 years or more (RR = 1.21; 95% CI, 1.01–1.45).

Cui and colleagues (2006), using data from the Canadian Breast Screening Study (1980–1985 baseline, 40–59 years of age) reported an increased risk for breast cancer among current smokers (RR = 1.18; 95% CI, 1.09–1.27) but not former smokers (RR = 1.00; 95% CI, 0.93–1.08). This report was an update of the same cohort from an analysis by Terry and colleagues (2002a), but at an average of 16 years of follow up for 4,445 cases rather than an average of 10.6 years for 2,552 cases. Overall, few differences can be found between these two reports. The 16-year follow-up study, using never smokers as the referent, found significant associations between risk for breast cancer and the highest categories of cigarettes smoked per day (RR = 1.20; 95% CI, 1.00–1.44), duration of smoking (RR = 1.50; 95% CI, 1.19–1.89), and pack-years of smoking (RR = 1.17; 95% CI, 1.02–1.34), as well as for smoking for more than 5 years before first pregnancy (RR = 1.13; 95% CI, 1.01–1.25) and for initiation of smoking between 16 and 19 years of age (RR = 1.10; 95% CI, 1.01–1.21). Effect modification by menopausal status was not found, but positive associations were stronger in women who did not report vigorous physical activity.

In the Iowa Women's Health Study cohort, Olson and colleagues (2005) reported a significantly increased risk (vs. never smokers) for breast cancer among postmenopausal current smokers (RR = 1.19; 95% CI, 1.03–1.37) but not former smokers. Increased risks were also detected for age at smoking initiation (older than 18 years of age: RR = 1.11; 95% CI, 1.00–1.24), smoking duration (≥40 years: RR = 1.18; 95% CI, 1.00–1.38), and smoking before first pregnancy (RR = 1.21; 95% CI, 1.07–1.37).

Reynolds and colleagues (2004b) used data from the California Teachers Study to evaluate the association of smoking with breast cancer. The authors detected significantly increased risks for breast cancer among current smokers in comparisons with two reference groups: never smokers (RR = 1.32; 95% CI, 1.10–1.57) and women who reported no active or passive exposure to smoking (RR = 1.25; 95% CI, 1.02–1.53). Results for former smokers, when compared with women who reported no active or passive exposure to smoking or with never smokers, were attenuated and not significant, regardless of reference group. This study reported significant trends toward increasing risk of breast cancer with longer duration and greater pack-years of smoking and more cigarettes smoked per day. In addition, risk of breast cancer was increased in women who initiated smoking before 20 years of age (RR = 1.17; 95% CI, 1.05–1.30) and who smoked for 5 or more years before first pregnancy (RR = 1.13; 95% CI, 1.00–1.28). In response to a letter by Johnson (2004), Reynolds and colleagues (2004a) conducted additional analyses to evaluate the associations for smoking duration, pack-years of smoking, and average number of cigarettes smoked per day with risk of breast cancer stratified by nulliparous women only, parous women who smoked less than 5 years prepartum, and parous women who smoked for 5 or more years prepartum. These analyses suggested a stronger risk effect among parous women who smoked for 5 or more years before first pregnancy for duration, pack-years, and cigarettes smoked per day (RR = 1.12, 1.28, and 1.25, respectively, for highest levels) than for women who smoked for less than 5 years before first pregnancy (RR = 1.18, 1.12, and 1.11, respectively, for highest levels) compared with their nonsmoking counterparts. Results were significant for the highest levels for pack-years and cigarettes smoked per day for parous women who had smoked for 5 or more years prior to pregnancy. Risk of breast cancer was increased among nulliparous women (RR = 1.13, 1.33, and 1.37, respectively, for highest levels), but significant for only those women who reported smoking 20 or more cigarettes per day compared with nonsmoking nulliparous women.

Luo and colleagues (2011b) reported results for 3,520 cases among 79,990 postmenopausal women followed for an average of 10.3 years in the Women's Health Initiative Observational Study cohort. The RRs for former and current smokers were 1.09 (95% CI, 1.02–1.17) and 1.16 (95% CI, 1.00–1.34), respectively, when based on a reference group of never smokers. These risks increased about 7–8% when based on a no active/no passive exposure reference group (RR = 1.16; 95% CI, 0.98–1.38 and RR = 1.24; 95% CI, 1.00–1.54, respectively). Risk was significantly (p <0.05) and inversely associated with age at initiation of smoking, and it was positively associated with cigarettes per day, duration, and pack-years of smoking. The RR for 50 or more pack-years of smoking was 1.18 (95% CI, 1.02–1.37), very similar to the estimate of 1.19 (95% CI, 1.07–1.33) for 51 or more pack-years reported by Xue and colleagues (2011) for the NHS-I. It is important to note, however, that the estimate for the Women's Health Initiative (Luo et al. 2011b) is for postmenopausal women only; the NHS-I (Xue et al. 2011) reported a significant (p = 0.02) inverse association with pack-years of smoking after menopause but a strong (p <0.001) positive association before menopause. Thus, these two large cohort studies provide contradictory results for the effect of smoking on risk of breast cancer in postmenopausal women.

Last, in a companion report from the Women's Health Initiative, Luo and colleagues (2011a) provided results suggesting that the risk of breast cancer is greater in nonobese women who smoke. The RR for current smoking was 1.25 (95% CI, 1.05–1.47) in nonobese women (BMI <30) versus 0.96 (95% CI, 0.69–1.34) in obese women. Significant trends in risk were found for age at initiation, duration and pack-years of smoking, and cigarettes per day in nonobese but not in obese women. The RR for 50 or more years of smoking was 1.62 (95% CI, 1.22–2.17) in nonobese women but only 0.62 (95% CI, 0.28–1.40) in obese women. This is one of three studies to date that have examined the interaction of smoking and body size on risk of breast cancer and the only one to formally test for statistical interaction; the other studies have been case-control. Gammon and colleagues (2004a) also reported an increased risk of breast cancer in lean women (BMI <22.3) exposed to both active and passive smoking (OR = 1.76; 95% CI, 1.06–2.92) but no association for obese women (BMI >29.2) in their case-control Long Island Breast Cancer Study Project. In contrast, Band and colleagues (2002) found a nonsignificant inverse association in ever smokers with a BMI less than 21 (RR = 0.75; 95% CI, 0.29–1.94) but an increased risk in those with a BMI 21 or greater (RR = 1.13; 95% CI, 0.63–2.04); however, the latter result is for lean, normal, overweight, and obese women combined and therefore cannot be compared with the other studies. Luo and colleagues (2011a) speculated as to whether this interaction could be associated with either an anti-estrogenic effect of smoking or with different distributions of genetic susceptibility polymorphisms in obese versus nonobese postmenopausal women.

European Studies

Since 2000, three European cohort reports have been published for findings on two studies of smoking and risk for breast cancer. Gram and colleagues (2005) studied the Norwegian-Swedish Cohort, a large population-based cohort (n = 102,098) in Scandinavia with up to 9 years of follow-up. Although the study detected nonsignificant increased risks for breast cancer among former smokers (RR = 1.15; 95% CI, 0.94–1.41) and current smokers (RR = 1.17; 95% CI, 0.95–1.40), it found some strong associations with timing of smoking initiation, duration of smoking, and smoking dose. Risk estimates for initiation of smoking before 15 years of age (RR = 1.48; 95% CI, 1.03–2.13), “before/around menarche” (RR = 1.39; 95% CI, 1.03–1.87), and before first pregnancy (RR = 1.27; 95% CI, 1.00–1.62) were all significantly associated with breast cancer among women who reported smoking for at least 20 years in comparisons with never smokers. Among women with 20 or more years of smoking, significant increased risks were also reported for smoking at least 10 cigarettes per day (RR = 1.34; 95% CI, 1.06–1.70), accumulating 20 or more pack-years of smoking (RR = 1.46; 95% CI, 1.11–1.93), and smoking for at least 25 years (RR = 1.36; 95% CI, 1.06–1.74) in comparison with never smokers. These results were attenuated on the order of 1–7% when analyzed for current smokers and were no longer significant except for pack-years of smoking and number of cigarettes smoked per day, as shown in Table 6.14S. Earlier, Manjer and colleagues (2000b) reported results from a smaller cohort study (n = 10,902) conducted in Malmö, Sweden. In premenopausal and postmenopausal women combined, former smoking—but not current smoking or number of cigarettes smoked per day—was significantly associated with risk for breast cancer (RR = 1.31; 95% CI, 1.02–1.69).

Asian Studies

Since 2000, studies published have included a systematic review of three cohort and eight hospital-based case-control studies by Nagata and colleagues (2006) and a single cohort study by Lin and colleagues (2008). The three cohort studies in the review by Nagata and colleagues (2006) included the study by Hanaoka and colleagues (2005) of middle-aged Japanese women, a study of atomic bomb survivors by Goodman and colleagues (1997), and a study of breast cancer mortality by Hirayama (1984, 1990). All eight case-control studies were conducted before 2000. In addition to multiple problems with the design of these studies, their results are difficult to interpret and have poor generalizability because of the low incidence of breast cancer and very low prevalence of smoking among Asian women (Table 6.13). Although the prevalence of smoking is very low among Chinese women (2%) and low among Japanese (12%) women, it is high among Chinese (50%) and Japanese (42%) men (Table 6.13, based on WHO 2011). Thus, women in Asia are exposed to secondhand smoke more so than to active cigarette smoking.

The study by Lin and colleagues (2008) included approximately 12 years of follow-up of 34,401 women (Table 6.14S). However, the study had limited power to detect an association between smoking and breast cancer because of a small number of cases (n = 208) and the low prevalence of current smoking (1.6%) and former smoking (5.3%). The RRs for breast cancer were 0.67 (95% CI, 0.32–1.38) for current smokers and 1.27 (95% CI, 0.46–3.48) for former smokers. However, when the analysis was restricted to postmenopausal women, current smokers had an elevated, albeit not significant, risk (RR = 1.20; 95% CI, 0.52–2.80). The study included too few premenopausal women to conduct a formal test of interaction, but the results suggest the possibility of effect modification by menopausal status. The Japan Public Health Center-based prospective cohort study by Hanaoka and colleagues (2005) also lacked statistical power, with only 180 incident cases among 21,805 women and a smoking prevalence of 5.7%. Moreover, the analyses appeared to mix incident morbidity data with mortality data. The RRs were 1.7 (95% CI, 1.0–3.1) for current smokers and 1.1 (95% CI, 0.4–3.5) for former smokers, using a no active/no passive reference group. Among premenopausal women, the RR was significantly increased, but imprecisely estimated for ever smokers (RR = 3.9; 95% CI, 1.5–9.9); the study found no increased risk among postmenopausal women (RR = 1.1; 95% CI, 0.5–2.5).

Case-Control Studies

Since 2000, there have been 34 reports based on 30 case-control studies on smoking and breast cancer (Table 6.15S). The reports provided by Metsola and colleagues (2005) and Sillanpaa and colleagues (2005a) were based on the same study group, and both used a no active/no passive exposure reference group. Because the report by Sillanpaa and colleagues (2005a) adjusted for a number of potential confounders and these adjustments made a difference in the reported estimates, this report is used in the meta-analyses and forest plots. Table 6.15S presents an overview of these studies. Seven studies are limited by either a small sample (<200 cases) with low statistical power (Delfino et al. 2000; Morabia et al. 2000; Alberg et al. 2004; Gibson et al. 2010; Kaushal et al. 2010) or by other design features that limit interpretation, such as clinic-based controls (Delfino et al. 2000; Kruk 2007; Cerne et al. 2011) or benign breast disease controls (Delfino et al. 2000). These studies vary considerably in reporting type and detail for measures of smoking and whether results are stratified by ethnicity, menopausal status, or genetic biomarkers.

North American Studies

Since 2000, findings on smoking and risk for breast cancer have been reported across seven large population-based case-control studies with at least 1,000 cases (Johnson et al. 2000; Innes and Byers 2001; Band et al. 2002; Gammon et al. 2004a; Mechanic et al. 2006; Prescott et al. 2007; Slattery et al. 2008; Young et al. 2009). The reports by Fink and Lash (2003) and DeRoo and colleagues (2011a) are not included in this section because they dealt exclusively with smoke exposure during pregnancy. Young and colleagues (2009) conducted the largest case-control study to date, with 6,235 cases and 6,533 controls (Table 6.15S). The study was based on pooled data from two case-control studies in Ontario, Canada: the Ontario Women's Health Study and the Ontario Women's Diet and Health Study. The designs of the two studies were similar, with cases ascertained through the provincial cancer registry and controls randomly selected from a population-based listing or by random-digit dialing. A risk estimate of 1.10 (95% CI, 0.98–1.23) was reported for current smokers versus women with no history of active or passive smoking. A significantly increased risk was found for older age at smoking initiation (≥26 years vs. a no active/no passive group) (OR = 1.26; 95% CI, 1.03–1.55), but there were no associations at younger ages of initiation (<12 years: OR = 0.88; 95% CI, 0.59–1.31; 12–15 years: OR = 1.02; 95% CI, 0.90–1.16; 16–20 years: OR = 1.12; 95% CI, 1.01–1.24). There was a significant risk of breast cancer for smoking initiated more than 5 years before first birth (OR = 1.16; 95% CI, 1.04–1.31), and for smoking initiated after first birth (OR = 1.24; 95% CI, 1.02–1.52). These results do not support the hypothesis that early initiation of smoking and smoking before first birth are more strongly associated with risk of breast cancer than are later initiation and initiation of smoking after first birth.

Johnson and colleagues (2000), in a study in eight Canadian provinces, ascertained 2,317 cases through the provincial tumor registries in the mid-1990s. Controls (2,438) were randomly sampled from health plan listings, a property assessment database, or by random-digit dialing. Extensive data were collected via a mailed questionnaire on active smoking and exposure to secondhand smoke. The analyses of cigarette smoking status used two reference groups: never smoker and no active/no passive exposure. Only the no active/no passive exposure referent was used for age at smoking initiation, number of cigarettes smoked per day, duration of smoking, pack-years of smoking, and number of years since quitting smoking. In general, risk estimates were higher when using the no active/no passive referent group than when using the never smoker referent group. Among premenopausal women, adjusted estimates (using no active/no passive as the referent) were higher for former smokers (OR = 2.6; 95% CI, 1.3–5.3) than for current smokers (OR = 1.9; 95% CI, 0.9–3.8); estimates for postmenopausal women were marginally higher for current smokers (OR = 1.6; 95% CI, 1.0–2.5) than for former smokers (OR = 1.4; 95% CI, 0.9–2.1). As for other measures of smoking (using no active/no passive exposure as the referent), premenopausal women had risk estimates at least 20% higher than postmenopausal women for current and former smoking status, age at smoking initiation, number of cigarettes smoked per day, duration of smoking, and number of years since quitting smoking. The study oversampled women younger than 55 years of age, so it is one of only a few with sufficient statistical power to detect associations among premenopausal women.

Only two studies reported results that were stratified by race/ethnicity. In one, Mechanic and colleagues (2006) provided data from Phases I and II of the Carolina Breast Cancer Study, a study that examined former and current smoking among 894 African American and 1,414 non-Hispanic White women. These cases were ascertained through the North Carolina Central Cancer Registry, and population-based controls (n = 2,022) were selected from motor vehicle and Health Care Financing Administration (now the Centers for Medicare & Medicaid Services) listings. This report serves as an update to the study by Marcus and colleagues (2000), which provided age and race-adjusted estimates from Phase I. In the study by Mechanic and colleagues (2006), risk for breast cancer was significantly increased in African American women who were former smokers (OR = 1.80; 95% CI, 1.30–2.50) or who had smoked more than 20 years (OR = 1.80; 95% CI, 1.20–2.60). In contrast, risk was not significantly elevated for White women who were former smokers (OR = 1.20; 95% CI, 0.90–1.50) or who had smoked for more than 20 years (OR = 1.10; 95% CI, 0.90–1.50). Increased risk was not significantly associated with current smoking for either racial group.

Slattery and colleagues (2008) conducted a population-based case-control study in Arizona, Colorado, New Mexico, and Utah. This study provided data on the risk of breast cancer associated with smoking status, pack-years of smoking, age at smoking initiation, and smoking before first pregnancy. Among women with a first primary breast cancer who had data for smoking, 798 were Hispanic/American Indian and 1,527 were non-Hispanic White. Cases were ascertained from state or national cancer registries (e.g., NCI's SEER Program). Population-based controls were randomly sampled, of which 924 Hispanics/American Indians and 1,601 non-Hispanic Whites had data for smoking. Among premenopausal non-Hispanic White women, risk for breast cancer was significantly increased among ever smokers (OR = 1.3; 95% CI, 1.0–1.7), those who smoked before first pregnancy (OR = 1.4; 95% CI, 1.0–1.9), and those who accumulated more than 15 pack-years of smoking (OR = 1.6; 95% CI, 1.1–2.4). The study did not find any significant associations with breast cancer in premenopausal Hispanic and American Indian women or in postmenopausal non-Hispanic White or Hispanic/American Indian women.

Results from the three remaining large case-control studies are inconsistent. Gammon and colleagues (2004a), who reported results from the Long Island Breast Cancer Study Project for 1,356 cases and 1,383 population-based controls, found that risk for breast cancer was not significantly increased among active/current smokers using a no active/no passive exposure referent regardless of the number of cigarettes per day, pack-years of smoking, age at smoking initiation, or smoking before first pregnancy. Significant associations were not found in a variety of subgroups, even after stratifying by menopausal status, BMI, alcohol use, use of HRT, use of oral contraceptives, family history, and age at reference date. In Los Angeles, Prescott and colleagues (2007), who conducted a case-control study of 1,728 cases and 441 controls, did not find significant associations between risk for breast cancer and smoking status, duration of smoking, age at smoking initiation, or smoking before first pregnancy. In contrast, Band and colleagues (2002) reported significant associations between risk for breast cancer and ever smoking (OR = 1.50; 95% CI, 1.09–2.07) and smoking for at least 20 years or more (OR = 1.60; 95% CI, 1.08–2.37) in premenopausal but not postmenopausal women based on responses from 1,018 cases and 1,025 controls who participated in a study conducted in British Columbia, Canada. There were no significant associations between risk and age at smoking initiation, but smoking before first pregnancy was significant for premenopausal women (OR = 1.51; 95% CI, 1.07–2.13) but not for postmenopausal women.

Six additional but smaller studies (<1,000 cases) that were conducted in the United States are notable for their findings (Lash and Aschengrau 2002; Egan et al. 2003; Li et al. 2005; Rollison et al. 2008; Ahern et al. 2009; Brown et al. 2010). In one, Li and colleagues (2005) examined a sample of 975 cases and 1,007 controls in Washington state and found a significantly increased risk (30% in each instance) for breast cancer among ever smokers, those who smoked, those 20–39 years of age, those who started smoking before age 20, and those who smoked before their first full-term birth. In addition, women who reported 20 or more pack-years of smoking and a history of HRT involving both estrogen and progestin had increased risk for breast cancer. The study by Lash and Aschengrau (2002) stands out because it found a significant inverse association for ever smoking (OR = 0.72; 95% CI, 0.55–0.95). That 2002 study conflicts, however, with a 1999 study (OR = 2.0; 95% CI, 1.1–3.6) in the same geographic area of Cape Cod, Massachusetts, carried out by the same team (Lash and Aschengrau 1999). Both studies included deceased cases and controls for which information about smoking was collected from proxies. However, the 2002 study, unlike the 1999 study, did not provide information about the fraction of data collected from proxy respondents. Thus, the results of the 2002 study could have been affected by information bias.

In a report from the Collaborative Breast Cancer Study, a population-based study conducted in Maine, Massachusetts, New Hampshire, and Wisconsin between 1988–1991 (Baron et al. 1996), Egan and colleagues (2003) analyzed data from the Massachusetts and Wisconsin sites (791 cases, 797 controls) for effect modification of smoking risk by NAT2 genotype. Not accounting for genotype, this study found a significantly increased risk for ever smokers (OR = 1.37; 95% CI, 1.12–1.69) and for women with more than 25 pack-years of smoking (OR = 1.54; 95% CI, 0.87–2.71). Results for the latter variable were OR = 1.54 (95% CI, 0.87–2.71) for premenopausal women and OR = 1.53 (95% CI, 1.10–2.13) for postmenopausal women. In a subsequent report, Ahern and colleagues (2009) analyzed data from only the Massachusetts site in the Collaborative Breast Cancer Study (557 cases, 432 controls) but did not find an association between pack-years of active smoking (OR = 0.90; 95% CI, 0.7–1.3 for >23 pack-years) and risk of breast cancer. However, this report was focused mainly on effects or associations with passive smoking.

The study by Rollison and colleagues (2008) reported an increased risk for breast cancer among ever smokers (OR = 1.43, 95% CI, 1.03–1.99). The authors attempted to compare results based on a no active/no passive to a no active-only reference group but the sample size was too small to provide sufficient statistical power to make an evaluation. Brown and colleagues (2010) conducted a case-control study of risk factors for breast cancer among Asians (Chinese, Filipino, Japanese) who immigrated to San Francisco-Oakland, California; Los Angeles, California; or Oahu, Hawaii. Just over one-half of the women in the study (54% of cases; 58% of controls) were born in Asia (China, Taiwan, Hong Kong, Macau, Japan, the Philippines, Southeast Asia, the Malaysian Peninsula, Singapore, or India) as opposed to Western or Western-style countries (such as those in North America or Europe or the nations of Australia and New Zealand). Women born in Asia and more recent migrants (<8 years) to the West had a lower risk of breast cancer regardless of smoking history than women born and raised in the West or a Western-style country. The overall OR for ever smoking was 1.2 (95% CI, 0.9–1.6). The only significant association between smoking and breast cancer was for age at initiation of younger than 16 years of age (OR = 2.92; 95% CI, 1.1–7.9), but this was based on a very small stratum (11 cases, 9 controls).

European Studies

Since 2000, three large (>1,000 cases) population based case-control studies have been conducted in Europe: one each in Germany (Andonova et al. 2010; Rabstein et al. 2010), Sweden (Magnusson et al. 2007), and Poland (Lissowska et al. 2006). Andonova and colleagues (2010) reported results from the Gene Environment Interaction and Breast Cancer in Germany (GENICA) study that included estimates of risk for breast cancer for former (OR = 0.95; 95% CI, 0.75–1.19) and current (OR = 0.84; 95% CI, 0.66–1.06) smoking using data for 1,021 cases and 1,015 controls in the greater Bonn region. This report and a companion report (Rabstein et al. 2010) were restricted to those subjects with available DNA and are further described in the section on genetic susceptibility.

Magnusson and colleagues (2007) ascertained 3,345 cases from six regional cancer registries and randomly selected 3,454 controls from a governmental population listing in Sweden. The study found few significant or consistent associations between risk of breast cancer and current or former smoking, duration of smoking, pack-years of smoking, age at initiation of smoking, or smoking before first full-term birth. However, data were missing for nearly 17% of cases and 25% of controls. The higher rate of missing data in controls was due partly to the use of a telephone interview that did not collect data for alcohol consumption and perhaps other covariates. In the Polish study, Lissowska and colleagues (2006) did not obtain significant results for all women considered together. However, among women younger than 45 years of age (n = 511), significantly increased risks were observed for current smoking (OR = 2.03; 95% CI, 1.40–2.95), the highest level of duration of smoking (>20 years: OR = 2.33; 95% CI, 1.32–4.13), smoking before first pregnancy (OR = 2.03; 95% CI, 1.40–2.94), and ever smoking approached statistical significance (OR = 2.40; 95% CI, 1.00–5.72). It is difficult to interpret some of these associations due to conflicting findings across levels of these exposures; for example, risks were also increased for duration of smoking less than 10 years (OR = 1.57; 95% CI, 1.01–2.44) and for smoking after first pregnancy (OR = 2.40; 95% CI, 1.27–4.53). Kruk (2007) also conducted a clinic-based study in Poland in which the control group was characterized by a higher prevalence of smoking than those in the general population. This study found some of the highest significant risks to date among women who smoked 10 or more cigarettes per day. Here, the ORs were 2.55 (95% CI, 1.81–3.60) for premenopausal women and 1.78 (95% CI, 1.33–2.37) for postmenopausal women.

In England, Roddam and colleagues (2007) conducted a population-based study of 639 cases, 36–45 years of age, with 640 age-matched controls. Significant associations were not detected for former smokers (OR = 1.15; 95% CI, 0.87–1.53) or current smokers (OR = 1.04; 95% CI, 0.79–1.36), age at initiation of smoking, duration of smoking, or number of cigarettes smoked per day. Data for duration of smoking and age at smoking initiation were analyzed as continuous variables. Thus, the results were not combined with those from other studies in generating summary estimates. The OR for former smokers, when calculated using a no passive/no active exposure reference group, was slightly lower (OR = 1.12; 95% CI, 0.72–1.73) for women with no passive exposure, and it decreased a bit more for women reporting passive exposure (OR = 1.09; 95% CI, 0.75–1.56). Interpreting the importance of the differences among the various estimates is difficult because none are statistically significant and the CIs overlap. Kropp and Chang-Claude (2002) evaluated the same smoking measures with a no active/no passive reference group. Their estimate for former smokers was comparable to that of Roddam and colleagues (2007) but was considerably higher for current smokers (OR = 1.47; 95% CI, 0.99–2.20). Last, Cerne and colleagues (2011) reported results from a clinic-based case-control study of breast cancer among 784 cases and 709 controls among post-menopausal Slovenian women. This report was focused on the effects of HRT, but an estimate was provided for smoking at least 10 cigarettes per day, adjusting for age and education only (OR = 1.70; 95% CI, 1.20–2.43). Notably, the reference group of nonsmokers included former smokers.

Asian Studies

Two small case-control studies from Asia were published between 2000 and 2011. For ever smoking, the study conducted in Manila, the Philippines (Gibson et al. 2010), reported an RR of 1.3 (95% CI, 0.6–2.9), and a study in northeast India (Kaushal et al. 2010) reported an RR of 1.15 (95% CI, 0.62–2.13).

Adjustment for Selected Covariates

Breast cancer is recognized as a heterogeneous disease with many associated risk factors (Hankinson and Hunter 2001; Brinton et al. 2002; Spicer and Pike 2005; Hortobagyi et al. 2006). Some of these risk factors have complex relationships with cancer of the breast, and the direction of their associations may differ according to characteristics such as breast cancer phenotype, age, menopausal status, and race/ethnicity. Established risk factors include:

• increasing age;
• family history of breast cancer in first-degree relatives;
• increased levels of endogenous estrogen;
• history of benign breast disease;
• mammographically dense breasts;
• less frequent screening;
• history of ionizing radiation exposure to the chest;
• various reproduction-related factors—increased risk with younger age at menarche (<12 years of age), older age at menopause (>54 years of age), older age at first pregnancy or live birth (>30 years of age), no history of breast feeding or a short duration of lactation, nulliparity, and decreased risk with increased number of pregnancies;
• higher socioeconomic status (e.g. higher level of education and/or family income);
• use of exogenous hormones (HRT, combined estrogen/progesterone oral contraceptives); and
• increased body size among postmenopausal women (as determined by height, weight, BMI, waist circumference, waist/hip ratio).

Studies have also demonstrated a modestly increased risk for breast cancer, on the order of 25–30%, associated with low level of physical activity (Friedenreich and Cust 2008) and on the order of nearly 50% with intake of 45 or more grams of alcohol per day (Collaborative Group on Hormonal Factors in Breast Cancer et al. 2002; Baan et al. 2007). IARC (2002) has concluded that alcohol consumption is a causal risk factor for breast cancer; additionally, Volume 6 of the IARC Handbook on Cancer Prevention concluded that regular physical activity reduces the risk of breast cancer. Many of these factors show a complex pattern of association that depends on timing in relation to other exposures, specifically increased estrogen levels, duration of exposure, and menopause. Differences in the distributions of these factors between women who smoke and those with no history of active smoking are likely to vary across populations; to the extent possible, the potential for confounding has been considered in individual studies and in the meta-analyses.

The great majority of cohort and case-control studies published since 2000 and described in this report (Tables 6.14S and 6.15S) either adjusted for, or evaluated the need for adjustment of, relevant confounders. Reproductive factors and family history are well-established, strong risk factors for breast cancer (Spicer and Pike 2005). In addition, since 2000 an increasing number of studies have demonstrated that alcohol use and obesity are important risk factors for breast cancer (Collaborative Group on Hormonal Factors in Breast Cancer et al. 2002). In a review by Kendall and colleagues (2007), the authors found that higher BMI is associated with increased endogenous estradiol levels among postmenopausal women. Although they did not find a clear relationship between alcohol use and estrogen levels, there was an apparent positive trend with increasing alcohol consumption (Kendall et al. 2007). All cohort studies described in this report adjusted for at least one reproductive factor and BMI; most of them either adjusted for or stratified on menopausal status; and all but one adjusted for alcohol consumption (Lawlor et al. 2004). Three cohort studies (Table 6.14S) did not adjust for family history (Manjer et al. 2000b; Lawlor et al. 2004; Gram et al. 2005).

The selection of covariates for adjustment varied across case-control studies (Table 6.15S). Some studies did not adjust for reproductive factors (Delfino et al. 2000; Alberg et al. 2004; Li et al. 2005; Metsola et al. 2005), alcohol intake (Delfino et al. 2000; Zheng et al. 2002b; van der Hel et al. 2003b; Alberg et al. 2004; Metsola et al. 2005), body size (Delfino et al. 2000; van der Hel et al. 2003b; Alberg et al. 2004; Metsola et al. 2005; Mechanic et al. 2006; Prescott et al. 2007), or family history (Johnson et al. 2000; van der Hel et al. 2003b; Alberg et al. 2004; Li et al. 2005; Metsola et al. 2005; Slattery et al. 2008). Five case-control studies did not adjust, stratify, or match on menopausal status, but in these studies the age range included both premenopausal and postmenopausal women (Marcus et al. 2000; Lash and Aschengrau 2002; Alberg et al. 2004; Metsola et al. 2005; Magnusson et al. 2007). Several studies explored models that adjusted for multiple covariates but reported results for only the most parsimonious models, adjusting for covariates that changed point estimates on the order of 5–15% (Marcus et al. 2000; van der Hel et al. 2003b; Gammon et al. 2004a; Li et al. 2005; Lissowska et al. 2006; Mechanic et al. 2006; Kruk 2007; Magnusson et al. 2007; Rollison et al. 2008; Young et al. 2009). Most studies with findings that were considered for inclusion in the meta-analyses made an effort to statistically detect and adjust for confounders within the data. However, the methods for considering potential confounders varied across studies and the basis for selecting the final, adjusted model was not always explicit.

Meta-Analysis of Breast Cancer Risk Associated with Measures of Active Smoking

All available non-overlapping cohort study reports published prior to 2012 and case-control study reports published from 2000–2011 were included in meta-analyses for this report. These timeframes were selected to identify the most recent evidence that was specifically relevant to associations between risk for breast cancer and active and passive smoking. The older literature has been repeatedly reviewed; the majority of studies published before 2000 were either cross-sectional or case-control in design and were not considered for inclusion in the meta-analysis. Reports from cohort studies published prior to 2000 were evaluated for inclusion; most of these have been superseded by subsequent reports. Table 6.16S provides a listing of the 65 reports from case-control and cohort studies. Twenty-six reports overlapped with results on the same study population, and of these, 11 were included in the meta-analyses because they were either the most recent or complete reports from their study. In the case of 1 cohort study (NHS-I) and 1 case-control study (Collaborative Breast Cancer Study), 2 reports contributed to separate meta-analyses because they offered different measures (NHS-I: Egan et al. 2002 and Xue et al. 2011; Collaborative Breast Cancer Study: Egan et al. 2003 and Ahern et al. 2009). Three cohort studies (Mills et al. 1989b; Land et al. 1994; Thomas et al. 1997), which were included in the report by the Collaborative Group on Hormonal Factors in Breast Cancer and colleagues (2002), were excluded from the present report because the individual estimates were not published in the original reports and they were combined into an ‘other’ category for the Collaborative Report. Four studies (1 cohort, 3 case-control) were included in only the meta-analysis of smoking before a first full-term pregnancy or first birth (Innes and Byers 2001; Fink and Lash 2003; Lawlor et al. 2004; DeRoo et al. 2011a). Thus, a total of 46 separate reports were included in the initial analysis of ever smoking. The total number included in each subsequent meta-analysis depended on whether a risk estimate was reported in a study for the measure of smoking. RR estimates were pooled across categories of exposure to fit common definitions of ever smoking, smoking status (former or current), duration of smoking, cigarettes smoked per day, pack-years of smoking, age at smoking initiation, and smoking before first pregnancy. Data are provided in Table 6.16S on studies affected by design and analysis issues, including small sample size, a mixed reference group (former smokers and nonsmokers combined), inadequate covariate adjustment, use of proxy subject reports, issues associated with exposure category cutpoints, and the presence of extreme outliers.

The DerSimonian and Laird (1986) procedure for random-effects meta-analysis was used to calculate summary estimates. The random-effects model was selected because the studies included in the meta-analysis showed substantial variation in type and quality of design, time period, geographic setting, composition of population, ascertainment of cases, selection of controls for case-control studies, and definition and measurement of smoking exposure. Whereas a fixed-effects model assumes that all studies are estimating the same true effect and that differences between studies are the result of random variation (precision) within studies, a random-effects model assumes that between-study variation is partly due to factors that influence the magnitude of the true effect within each study, resulting in a distribution of true effects across studies. The fixed-effects model gives greater weight to larger, more precise studies, whereas the random-effects model dampens to some degree the influence of these larger studies relative to smaller ones. Additionally, the summary estimates from random-effects models generally have broader CIs than those from fixed-effects models, making the former method intrinsically more conservative (Borenstein et al. 2009). The random-effects model accounts for heterogeneity among studies, which can be quantified, for example, in the Q-test statistic. When heterogeneity is low, the random-effects model converges with the fixed-effects model.

Meta-analyses were conducted in STATA 11.0 (STATA Corp., College Station, TX, USA) using the meta STATA command (Sterne 2009). The meta-funnel STATA command was used to create funnel plots for visual assessment of publication bias and outliers. Between-study heterogeneity was assessed with Cochran's χ2 test, reported as the Q-test statistic, and bias was assessed formally using Egger's statistical test (Egger et al. 1997) and Begg's rank correlation test (Begg and Mazumdar 1994), with the latter calculated via the metabias STATA command. The Begg test is reported to have low power when the number of studies is small. The Egger test is more powerful but also biased and can produce false-positive results (Deeks et al. 2005). Sensitivity analyses considered study design, prevalence of exposure, sample size, and measurement of exposure effect. Results for the Begg and Egger tests are included as a note in figures as appropriate. Summary estimates from random effects models are reported for all meta-analyses.

Ever Smoking

If not reported, a measure for ever smoking was calculated for all 46 studies by pooling available data on smoking status, smoking duration, cigarettes smoked per day, or pack-years of smoking, with the exception of four studies that provided data only for exposure before or during first pregnancy (Table 6.16S). A meta-analysis was conducted of nonoverlapping reports from all cohort studies through 2011, as well as case-control studies published from 2000–2011, for ever smoking, resulting in a summary estimate with significant heterogeneity (p_h <0.001): RR = 1.12 (95% CI, 1.07–1.17; n = 46) (Table 6.17S, Figure 6.28). From visual inspection, the funnel plot in Figure 6.29 shows no sign of skewness, indicating that publication bias was not a significant issue. This finding was further confirmed by Begg's rank correlation test (z = 0.48, p = 0.63) and the Egger test (bias = 0.44, p = 0.25). Stratification by study design revealed that the heterogeneity was due primarily to variation among the 27 case-control studies (RR = 1.15; 95% CI, 1.06–1.25; p_h <0.001) than to variation among the 19 cohort studies (RR = 1.10; 95% CI, 1.07–1.13; p_h = 0.793).

Figure 6.28

Forest plot showing association between ever smoking and risk for breast cancer, based on cohort studies published before 2012 and case-control studies published from 2000 to 2011 (n = 46). Note: * = cohort study; ^ = case-control study. Meta-analysis (more...)

Figure 6.29

Funnel plot for estimates in meta-analysis of ever smoking with risk for breast cancer, based on cohort studies published before 2012 and case-control studies published from 2000 to 2011 (n = 46). Note: l = cohort study; p = case-control study. Includes (more...)

Thirteen studies were excluded in the following sequence (some studies fell into more than one category).

1. Six cohort studies reported in the pooled analysis restricted to nondrinkers conducted by the Collaborative Group on Hormonal Factors in Breast Cancer and colleagues (2002) and for which there were no data available on smoking in the original report (van den Brandt et al. 1995; Engeland et al. 1996; Million Women Study Collaborative Group 1999).
2. Eight additional studies, three cohort (Schatzkin et al. 1989; Hanaoka et al. 2005; Lin et al. 2008) and five case-control (Delfino et al. 2000; Morabia et al. 2000; Alberg et al. 2004; Gibson et al. 2010; Kaushal et al. 2010), with less than 210 cases.
3. Two additional studies, one cohort (Vatten and Kvinnsland 1990) and one case-control (Cerne et al. 2011), with an estimate reported for only current smokers and for which the reference group appeared to mix never smokers with former smokers.

The summary estimate for the 12 cohort studies remaining (Table 6.17S) after the exclusion of the 7 studies that were restricted to nondrinkers had a small sample, or a mixed reference group did not change meaningfully from the overall estimate (RR = 1.10; 95% CI, 1.07–1.13; p_h = 0.717). For case-control studies, the RR was attenuated slightly (RR = 1.13; 95% CI, 1.04–1.23; p_h <0.001) when 6 were excluded that were either small (<210 cases), from Asia, or had a mixed reference group (Table 6.16S). The additional exclusion of a cohort study (Nordlund et al. 1997) that adjusted only for age and place of residence did not alter the summary RR for cohort studies. The funnel plot in Figure 6.29 indicates that the studies by Kruk (2007) and Lash and Aschengrau (2002) are outliers. The case-control study by Lash and Aschengrau (2002) relied on proxy interviews for deceased cases. Kruk (2007), which was conducted in Poland, used clinic-based controls that were reported to have a higher percentage of smoking (33%) than in the general population (23%). However, a comparison of self-reported prevalence of cigarette smoking and cotinine saliva samples (cutpoint for active smoking—1.5 nanogram [ng]/milliliter [mL]) indicated that true prevalence may be underestimated in Poland by 4.4% (West et al. 2007). The removal of Kruk (2007) and Lash and Aschengrau (2002) resulted in a summary risk estimate of 1.08 (95% CI, 1.03–1.13) and decreased heterogeneity (p_h = 0.340) for case-control studies, without adding significant bias according to the Begg (z = 0.73, p = 0.46) and Egger (bias = 0.43, p = 0.19) tests (see notes for Figure 6.30). The RR for the combined case-control and cohort studies (n = 30) decreased to 1.09 (95% CI, 1.06–1.12; p_h = 0.500). In summary, the significant heterogeneity among studies for the association between ever smoking and breast cancer is attributable mainly to the study by Kruk (2007), which is the more extreme of the two outliers. Excluding this study substantially reduces heterogeneity and results in an attenuated summary estimate. When taken together, these 30 studies suggest that ever smoking increases the RR for breast cancer by a statistically significant average of 9% (Table 6.17S, Figure 6.30). These 30 reports remained as the baseline to be considered for the remaining meta-analyses.

Figure 6.30

Forest plot showing association between ever smoking and risk for breast cancer, based on cohort studies published before 2012 and case-control studies published from 2000 to 2011, excluding studies with design or analysis issues (n = 30). Note: * = cohort (more...)

No Active-Only Versus No Active/No Passive Exposure Referent Group

Wells (1991) first suggested that the most appropriate reference group would exclude women who were exposed to passive smoke because their inclusion would attenuate the association with active smoking. Morabia and colleagues (1996) first used this criterion in an analysis of data from a case-control study in Switzerland. Since then, other investigators have narrowed the definition of the reference group to women who report no active or passive smoking exposure. In this report, 5 cohort studies (Egan et al. 2002; Reynolds et al. 2004b; Gram et al. 2005; Hanaoka et al. 2005; Luo et al. 2011b) and 14 case-control studies (Morabia et al. 2000; Delfino et al. 2000; Johnson et al. 2000; Kropp and Chang-Claude 2002; Lash and Aschengrau 2002; Alberg et al. 2004; Gammon et al. 2004a; Sillanpaa et al. 2005a; Lissowska et al. 2006; Mechanic et al. 2006; Roddam et al. 2007; Rollison et al. 2008; Ahern et al. 2009; Young et al. 2009) included results based on a no active/no passive exposure reference group. Ten studies reported results for both reference groups that can be compared for ever smoking (Johnson et al. 2000; Egan et al. 2002; Reynolds et al. 2004b; Gram et al. 2005; Hanaoka et al. 2005; Lissowska et al. 2006; Roddam et al. 2007; Rollison et al. 2008; Ahern et al. 2009; Luo et al. 2011b). Six compared estimates using the two referent groups by smoking status (Johnson et al. 2000; Egan et al. 2002; Reynolds et al. 2004b; Hanaoka et al. 2005; Roddam et al. 2007; Luo et al. 2011b), 1 did so by pack-years (Ahern et al. 2009), and 2 provided comparisons by duration, dose, and timing (Rollison et al. 2008; Luo et al. 2011b). Nine studies used only a no active/no passive reference group (Delfino et al. 2000; Morabia et al. 2000; Kropp and Chang-Claude 2002; Lash and Aschengrau 2002; Alberg et al. 2004; Gammon et al. 2004a; Sillanpaa et al. 2005a; Mechanic et al. 2006; Young et al. 2009). As noted previously, estimates for ever smoking were derived for some studies by pooling other exposure measures, such as former and current smoking. Additionally, the terminology for defining these reference groups (no active-only, no active/no passive) varies among studies, although the definitions are common.

The size of the reference group is greatly decreased when restricted to no active/no passive exposure because of the high prevalence of passive smoking exposure: most studies indicate that only about 10–20% of never smokers report no passive exposure. In a study by Arheart and colleagues (2008), an estimated 28% of people who reported no passive exposure were actually exposed based on serum cotinine levels, suggesting that the true no active/no passive group may be even smaller, particularly if considered in a lifetime context. No systematic analyses have been conducted to determine whether using only a small no active/no passive referent produces selection bias or sparse data bias (Greenland et al. 2000) as well as loss of statistical power, or whether statistical adjustment for passive smoking exposure in assessing active smoking is as efficient as having a no active/no passive referent. One exception may be Ahern and colleagues (2009), who estimated associations of active smoking with breast cancer using a restricted no active/no passive exposure reference group while also employing statistical adjustment for passive smoking exposure. Unfortunately, it is difficult to interpret the differences between the two approaches because only 30% of participants in that study had data for both active and passive smoking.

In the California Teachers Study cohort (Table 6.14S), the RRs for breast cancer in current smokers overall were both significant and quite similar with the two reference groups used: no active-only (“never”) (RR = 1.32; 95% CI, 1.10–1.57) and no active/no passive (RR = 1.25; 95% CI, 1.02–1.53) (Reynolds et al. 2004b). In contrast, ORs for ever smokers (i.e., former or current) in Johnson and colleagues' (2000) population-based Canadian case-control study were 1.0 (95% CI, 0.8–1.3) for premenopausal women and 1.2 (95% CI, 1.0–1.4) for postmenopausal women when based on the no active-only (“never”) reference group, versus 2.3 (95% CI, 1.2–4.5) for premenopausal women and 1.5 (95% CI, 1.0–2.3) for postmenopausal women when based on the no active/no passive exposure reference group. Although these results seem to suggest a strong effect when using a no active/no passive exposure reference group, the estimates were based on a restricted subgroup of women (62% of the reference group) who were able to account for and report data for more than 90% of their lifetime residential passive smoking exposure. In addition, the no active/no passive reference group consisted of only 193 women (49 premenopausal and 144 postmenopausal women), compared with 2,292 women in the no active-only reference group.

Only two case-control studies have compared results for measures of smoking other than ever smoking or smoking status, but the results are difficult to interpret because of small samples and low statistical power (Rollison et al. 2008; Ahern et al. 2009). For cohort studies, Lin and colleagues (2008) compared results using the two different definitions of reference groups (no active/no passive, no active-only) in the Japan Collaborative Cohort Study for Evaluation of Cancer Risk and stated there was no difference in the estimates, but they did not provide numerical evidence. Luo and colleagues (2011b) reported findings for the only cohort study to date with parallel, multivariable adjusted analyses contrasting no active/no passive exposure with no active-only reference groups for multiple measures. The use of a no active/no passive exposure reference group resulted in a small but consistent increase in RR ranging from 2–10% for most measures of active smoking (ever, status, age at initiation, duration, cigarettes smoked per day, pack-years). The strongest effect of active smoking was for duration greater than 50 years, where the RR was 1.45 (95% CI, 1.06–1.98) using a no active/no passive exposure group compared with 1.35 (95% CI, 1.03–1.77) using a no active-only (“never”) reference group. The analysis suggests that the use of a no active/no passive exposure reference group may provide a small benefit in control for confounding between active and passive smoking effects. However, this small gain in control of confounding is at the cost of statistical power. It has not been established whether statistical adjustment for passive exposure of estimates for the risk of active smoking adequately controls for this confounding. Additionally, the small, restricted subgroup with no active/no passive exposure could differ systematically for other confounders or modifiers that are not measured or adequately controlled. Luo and colleagues (2011b) did not systematically compare the subgroup of no active/no passive smokers with the rest of the study population to determine whether there were any differences for other potential confounders such as race/ethnicity, education, alcohol consumption, or reproductive variables. This comparison, in fact, was not made in any of the studies that used a no active/no passive exposure reference group.

Meta-analyses were conducted to compare 27 studies reporting results based on a no active-only reference group with 15 studies reporting estimates based on a no active/no passive exposure reference group (Table 6.16S), after the 13 exclusions cited previously. The number of studies was further reduced to 25 for the no active-only and 14 for the no active/no passive exposure analyses with the exclusion of 3 studies (Nordlund et al. 1997; Lash and Aschengrau 2002; Kruk 2007) for reasons given above. The report by Egan and colleagues (2002) was used because the more recent report by Xue and colleagues (2011) did not report results using a no active/no passive exposure reference group. The RR for the no active-only exposure reference group was 1.09 (95% CI, 1.06–1.13; p_h = 0.308) (Table 6.17S, Figure 6.31). This estimate is slightly lower than that calculated for 14 studies using a no active/no passive exposure reference group (RR = 1.15; 95% CI, 1.09–1.21; p_h = 0.572) (Table 6.17S, Figure 6.32). Nine of the studies—4 of which were large cohort studies (Egan et al. 2002; Reynolds et al. 2004b; Gram et al. 2005; Luo et al. 2011b)—calculated estimates using both reference groups. These 9 studies were included in the two meta-analyses. Neither of these analyses was significantly affected by publication or small-study bias, according to Begg or Egger statistics (see notes for Figures 6.31 and 6.32; funnel plots not shown). These analyses suggest that the use of a restricted no active/no passive exposure reference group results in a small increase in estimates of the association between ever smoking and breast cancer.

Figure 6.31

Forest plot showing association between ever smoking and risk for breast cancer, based on the subset of cohort studies published before 2012 and case-control studies published from 2000 to 2011 with a no active-only referent group (n = 25). Note: * = (more...)

Figure 6.32

Forest plot showing association between ever smoking and risk for breast cancer, based on the subset of cohort studies published before 2012 and case-control studies published from 2000 to 2011 with a no active/no passive exposure referent group (n = (more...)

Cigarette Smoking Status

A total of 25 studies reported estimates for current and former smoking; 20 used a no active-only and 5 a no active/no passive exposure reference group (Table 6.16S, Figures 6.33 and 6.34). The summary estimates were similar for current smokers (RR = 1.12; 95% CI, 1.08–1.16; p_h = 0.347) and former smokers (RR = 1.09; 95% CI, 1.05–1.13; p_h = 0.062) (Table 6.17S). Results for former smokers were virtually identical for the two study designs: cohort (RR = 1.09; 95% CI, 1.03–1.14; p_h = 0.021) and case-control (RR = 1.09; 95% CI, 1.03–1.16; p_h = 0.354). The summary estimate for current smokers in the 11 cohort studies (OR = 1.14; 95% CI, 1.10–1.18; p_h = 0.746) was higher than the estimate for those in the 14 case-control studies (OR = 1.07; 95% CI, 1.00–1.16; p_h = 0.209). Sensitivity analyses were conducted that excluded the 4 case-control studies (Kropp and Chang-Claude 2002; Gammon et al. 2004a; Sillanpaa et al. 2005a; Mechanic et al. 2006) and 1 cohort study (Gram et al. 2005) with estimates based on only a no active/no passive exposure reference group. Excluding these studies did not meaningfully alter the overall results for either current smokers (RR = 1.11; 95% CI, 1.07–1.16) or former smokers (RR = 1.09; 95% CI, 1.04–1.13). There was significant heterogeneity among the cohort studies for the association with former smoking because of 1 study (Hiatt et al. 1988) with an outlying estimate (RR = 0.65; 95% CI, 0.47–0.89). The exclusion of this study, as well as the other 5 that were excluded, eliminated the heterogeneity (p_h = 0.220) but did not change the point estimate. The association between risk for breast cancer and former smoking may be attenuated relative to current smoking because the former association includes women with variable lengths of time since cessation. These results suggest that current smoking is associated with an increase in the RR for breast cancer by an average of 12%, and former smoking with an increase by an average of 9%. These results are similar to those for ever smoking. Neither of these analyses was significantly affected by publication or small-study bias according to Begg or Egger statistics (see notes for Figures 6.33 and 6.34).

Figure 6.33

Forest plot showing association between current smoking and risk for breast cancer, based on the subset of cohort studies published before 2012 and case-control studies published from 2000 to 2011 (n = 25). Note: * = cohort study; ^ = case-control study. (more...)

Figure 6.34

Forest plot showing association between former smoking and risk for breast cancer, based on the subset of cohort studies published before 2012 and case-control studies published from 2000 to 2011 (n = 25). Note: * = cohort study; ^ = case-control study. (more...)

Duration of Cigarette Smoking

Several cohort studies support an association between risk for breast cancer and long duration of smoking exposure (Table 6.14S). The Canadian National Breast Screening Study (RR = 1.50; 95% CI, 1.19–1.89; p trend = 0.0003 for ≥40 years) (Cui et al. 2006) and the NHS-II (RR = 1.21; 95% CI, 1.01–1.45; p trend = 0.04 for ≥20 years) (Al-Delaimy et al. 2004) both showed increased risks that were significant at approximately 16 and 10 years of follow-up, respectively. An earlier analysis of the Canadian cohort by Terry and colleagues (2002a) showed risk to be approximately 7% higher for 40 or more years of smoking (RR = 1.61; 95% CI, 1.19–2.19; p for trend = 0.009), but the 2002 report was based on 1,893 fewer cases than that of the report by Cui and colleagues (2006). The two analyses adjusted for the same covariates. Although Egan and colleagues (2002) did not observe a significant trend for the association between risk for breast cancer and duration of smoking (p for trend = 0.18) in the NHS-I, the recent updated analysis by Xue and colleagues (2011) with 30 years of follow-up found a significant trend (p = 0.01). The RRs were 1.04, 1.07, and 1.15 for <20, 20–39, and 40 or more years of smoking, respectively. Luo and colleagues (2011b) reported a highly significant (p trend = 0.0002) increased risk with duration of smoking in the Women's Health Initiative, with an RR of 1.35 (95% CI, 1.03–1.77) at the highest level (≥50 years). Because all of these studies adjusted for age, it is difficult to attribute these trends to confounding by that variable.

In response to comments posed by Johnson (2004) about analyses of the California Teachers Study data (Reynolds et al. 2004b), Reynolds and colleagues (2004a) presented essentially the same results for all women (RR = 1.15; 95% CI, 1.00–1.33; p trend = 0.009 at ≥31 years of smoking duration) and for nulliparous women only (RR = 1.13; 95% CI, 0.84–1.52; p trend = 0.081, also at ≥31 years of duration). Two other cohort studies showed increased risks of 26% (Gram et al. 2005) and 18% (Olson et al. 2005), respectively, for the highest categories of smoking duration.

A total of 21 studies reported estimates for duration of smoking, after the 13 exclusions cited above (Table 6.16SA and B) (Roddam et al. 2007) not included because only continuous result reported. Nineteen studies with data for smoking duration of 20 or more years have examined the associated risk for breast cancer and were included in the meta-analysis: 7 cohort (Al-Delaimy et al. 2004; Reynolds et al. 2004b; Gram et al. 2005; Olson et al. 2005; Cui et al. 2006; Luo et al. 2011b; Xue et al. 2011) and 12 case-control studies (Johnson et al. 2000; Band et al. 2002; Kropp and Chang-Claude 2002; Zheng et al. 2002a; van der Hel et al. 2003b; Li et al. 2005; Lissowska et al. 2006; Mechanic et al. 2006; Magnusson et al. 2007; Prescott et al. 2007; Rollison et al. 2008; Brown et al. 2010) (Table 6.16S, Figure 6.35). The summary estimate (RR) for these studies was 1.16 (95% CI, 1.12–1.21; p_h = 0.318) (Table 6.17S). The Egger test was significant, but the Begg test was not, and thus this result may be influenced by publication or small-study bias (see note for Figure 6.35). The summary estimate (RR) was 1.15 (95% CI, 1.10–1.19; p_h = 0.819) for the 7 cohort studies and 1.23 (95% CI, 1.12–1.36; p_h = 0.146) for the 12 case-control studies (Table 6.17S). Three case-control studies had cutpoints that were greater than 20 years (Zheng et al. 2002a; van der Hel et al. 2003b; Magnusson et al. 2007), and the reference group in 1 cohort (Gram et al. 2005) and 3 case-control studies was based on no active/no passive exposure (Johnson et al. 2000; Kropp and Chang-Claude 2002; Mechanic et al. 2006). A sensitivity analysis that excluded these 7 studies resulted in similar overall summary estimates for all studies (RR = 1.15; 95% CI, 1.11–1.19; p_h = 0.43), case-control (RR = 1.21; 95% CI, 1.05–1.40), and cohort studies (RR = 1.14; 95% CI, 1.10–1.19).

Figure 6.35

Forest plot showing association between 20 or more years of smoking duration and risk for breast cancer, based on the subset of cohort studies before 2012 and case-control studies published from 2000 to 2011 (n = 19). Note: * = cohort study; ^ = case-control (more...)

The same analyses were conducted to estimate the summary RR for less than 20 years of smoking duration to compare it with the result for 20 years or more. The summary estimate for the 19 studies was 1.04 (95% CI, 1.01–1.07) (Table 6.17S). There was no evidence of publication or small-study bias according to Begg's or Egger's statistics (p >0.05). There was no difference in the RR between case-control and cohort studies, and the estimate was not attenuated with the exclusion of studies using a no active/no passive reference group or those that had a cutpoint that differed by more than 2 years from the 20 years of duration used in the meta-analyses. This indicates an increasing trend in risk with longer duration of smoking or a dose-response relationship. These results suggest that active smoking of long duration (i.e., 20 or more years) increases risk for breast cancer by a significant average of 15%. This estimate may be conservative, as some studies indicate that risk continues to increase with smoking over longer periods (Cui et al. 2006; Luo et al. 2011b).

Cigarettes Smoked Per Day

The number of cigarettes smoked per day provides a measure of smoking intensity. In most studies, it represents the intensity of current smoking unless data are available for multiple time points that can be used to interpret the measure as the usual intensity of smoking, or intensity over time, the latter often expressed as pack-years of smoking. A recent study (Lubin et al. 2007) suggests that smoking intensity, measured as cigarettes per day, may have complex interactions with duration of smoking on risk of disease: high-intensity effects may diminish over time, while low-intensity effects increase. In contrast, associations of duration or pack-years of smoking with risk may involve residual confounding with age, as older women will have smoked longer but will also have increased risk for breast cancer regardless of smoking. While all studies included in the present meta-analyses of duration and pack-years of smoking adjusted for age, residual confounding may remain that could inflate estimates for longer duration or higher pack-years of smoking. Consequently, meta-analyses were conducted for studies that quantified risk of breast cancer with cigarettes per day, as well as duration of smoking and pack-years of smoking, to provide an alternative measure of dose-response.

A total of 20 studies (9 cohort, 11 case-control) provided a report on cigarettes per day as a measure of the intensity of smoking (Table 6.16SA and B) (Roddam et al. 2007 not included because only a continuous result was reported). Higher level of intensity was categorized at 20 cigarettes for 9 studies, at 21 for 6 studies, and at 25 for 3 studies. The cutpoint at 20 is consistent with smoking one pack of cigarettes or more per day. Two of the 20 eligible studies were excluded from the meta-analysis because in 1 (Gram et al. 2005) the highest category was 10 or more cigarettes per day and in the second (Lissowska et al. 2006) it was more than 14. Because the focus of the meta-analysis was on maximum dose, studies that have a maximum-dose category less than 20 have the potential to have subjects with substantially higher levels of smoking included with individuals who smoke considerably less.

Results for low-level compared with high-level smoking intensity differed on the order of 2.7% for all studies, 4.7% for cohort studies, and 3.4% for case-control studies. The summary estimate for the 18 studies was 1.10 (95% CI, 1.06–1.16; p_h = 0.031) for fewer than 20 cigarettes smoked per day. Although there was no evidence of publication bias according to the Begg's statistic (p = 0.103), the Egger statistic (p = 0.025) suggested bias was present. The summary estimate was 1.13 (95% CI, 1.09–1.17; p_h = 0.903) for 20 or more cigarettes per day and there was no evidence of publication or small study bias according to Begg's or Egger statistics (Table 6.17S, Figure 6.36). These results appear to be more heavily weighted by the 8 cohort studies. There was significant heterogeneity for the 10 case-control studies for estimates involving 20 or fewer cigarettes per day (p_h = 0.033). When 3 case-control studies that used a no active/no passive reference group were excluded, the overall summary estimate was reduced to 1.08 (95% CI, 1.05–1.12, p_h = 0.179).

Figure 6.36

Forest plot showing association between 20 or more cigarettes/day and risk for breast cancer, based on the subset of cohort studies published before 2012 and case-control studies published from 2000 to 2011 (n = 18). Note: * = cohort study; ^ = case-control (more...)

Pack-Years of Cigarette Smoking

The number of pack-years of smoking is calculated as the product of intensity (i.e., cigarettes smoked per day) and duration of smoking, and thus this indicator provides an index of lifetime dose of cigarette smoking. Some investigators prefer this measure, noting that it provides greater analytic power than duration alone (Ha et al. 2007). However, in their modeling of lung cancer and cigarette smoking, Lubin and Caporaso (2006) noted that the measure of pack-years mixes low-intensity smoking over long durations with high-intensity smoking over short periods. Low-dose smoking over a long duration results in increasing trends for risk estimates, termed exposure enhancement, and high-dose smoking over short periods produces the reverse trend, termed reduced potency (Lubin and Caporaso 2006). In addition, estimates of the usual number of cigarettes smoked per day lose validity over longer durations if smoking is punctuated by intermittent attempts at cessation.

Sixteen studies (6 cohort and 10 case-control) have examined the association between risk for breast cancer and pack-years of smoking and were included in the meta-analysis (Table 6.16SA and B). The summary estimate (RR) for the 16 studies was 1.16 (95% CI, 1.11–1.21; p_h = 0.304) for 20 or more pack-years of smoking (Table 6.17S, Figure 6.37). The Begg and Egger tests did not reveal any bias (see notes for Figure 6.37). Estimates for 20 or more pack-years did not differ meaningfully between study types: cohort (RR = 1.15; 95% CI, 1.10–1.19; p_h = 0.346) and case-control (RR = 1.21; 95% CI, 1.09–1.34; p_h = 0.314) (Table 6.17S). After excluding 1 cohort (Gram et al. 2005) and 3 case-control studies with estimates based on only a no active/no passive exposure reference group (Johnson et al. 2000; Kropp and Chang-Claude 2002; Gammon et al. 2004a), the overall summary estimate for 20 or more pack-years was slightly attenuated (RR = 1.14; 95% CI, 1.10–1.18; p_h = 0.829), and the RR for case-control studies was reduced by 6%. The overall summary estimate was 1.14 (95% CI, 1.10–1.18; p_h = 0.900) with the exclusion of the case-control study by Li and colleagues (2005), which had a higher cutpoint (more than 52 pack-years) and included only postmenopausal women. The exclusion of this study sharply reduced the RR for the case-control studies to 1.09 (95% CI, 0.96–1.24; p_h = 0.795).

Figure 6.37

Forest plot showing association between 20 or more pack-years of smoking and risk for breast cancer, based on the subset of cohort studies published before 2012 and case-control studies published from 2000 to 2011 (n = 16). Note: * = cohort study; ^ = (more...)

The summary estimate for less than 20 pack-years of smoking was 1.09 (95% CI, 1.03–1.15; p_h = 0.099), which was below the summary estimate of 1.16 (95% CI, 1.11–1.21) for 20 or more pack-years (Table 6.17S). This result was primarily due to the cohort studies, for which the summary estimate for fewer than 20 pack-years was 1.04 (95% CI, 1.00–1.09; p_h = 0.872). The result for fewer than 20 pack-years of smoking for case-control studies was substantially higher (RR = 1.20; 95% CI, 1.05–1.37) but the heterogeneity was significant (p_h = 0.023). The summary estimate and the extent of heterogeneity for these case-control studies were substantially decreased when the three studies (Johnson et al. 2000; Kropp and Chang-Claude 2002; Gammon et al. 2004a) using a no active/no passive exposure reference group were excluded (RR = 1.10; 95% CI, 0.97–1.24; p_h = 0.154). Overall, accumulating 20 or more pack-years increased risk for breast cancer by a significant average of 16%, while smoking for less than 20 pack-years was associated with a smaller increased risk of 9%. The estimate for 20 or more pack-years of smoking may be conservative, because some studies indicate that risk continues to rise with more pack-years (Xue et al. 2011).

Thirteen of the 16 studies with estimates for pack-years of smoking also provided risk by duration (Table 6.16S). Estimates across levels of duration and pack-years of smoking were not necessarily consistent for the two measures within a study; the Spearman correlation across studies was 0.62 (p = 0.02). Nonetheless, the summary estimates suggest that long duration of smoking and higher numbers of pack-years of smoking significantly increase risk for breast cancer by a similar amount, approximately 11–21% based on the CIs, depending on study design and sensitivity analysis restrictions (Table 6.17S). The summary estimate from case-control studies tended to be higher for both duration and pack-years of smoking than for cohort studies but also less stable. Taken together, the meta-analyses for duration, cigarettes smoked per day, and pack-years provide similar evidence for a dose-response relationship between smoking and breast cancer.

Timing of Exposure to Tobacco Smoke

The timing of smoking relative to critical periods of change in the size and morphology of breast tissue—time-frames such as menarche, during adolescence, or before first pregnancy—may be important. Based on in vitro studies, Russo (2002) hypothesized that smoking is more likely to induce neoplastic changes during these periods, when the susceptibility of the breast to carcinogens is increased. Breast cancer also is more likely to develop in undifferentiated tissues that may be susceptible to tobacco-related and other carcinogens. Results of epidemiologic studies substantiate that nulliparous women have a higher risk than parous women of breast cancer. The lower risk for parous women is attributed to having an early full-term pregnancy and the subsequent increased differentiation in the terminal ducts of the breast (Russo et al. 1992, 2000; Russo and Russo 1995, 2008).

Age at Smoking Initiation

Twenty-two studies with data for age at smoking initiation were evaluated: 8 cohort studies and 14 case-control studies (Table 6.16S, see notes for Figure 6.38 for exclusions). The cutpoints for age varied among these studies. Therefore, estimates were allocated into the closest of the following categories: younger than 16 years of age, 16–19 years of age, and 20 years of age and older. The first two categories were combined so that all 22 studies had estimates for those younger than 20 years of age at smoking initiation. Sensitivity analyses stratified the studies by design and excluded studies with large differences in cutpoints or those that used only a no active/no passive exposure reference group.

Figure 6.38

Forest plot showing association between less than 20 years of age at smoking initiation and risk for breast cancer, based on the subset of cohort studies published before 2012 and case-control studies published from 2000 to 2011 (n = 22). Note: * = cohort (more...)

Figure 6.38 shows results from all 22 studies for those younger than 20 years of age at smoking initiation. The RR summary estimate was 1.11 (95% CI, 1.07–1.16; p_h = 0.088) (Table 6.17S). The Begg and Egger tests were not significant (see notes to Figure 6.38; funnel plot not shown). The estimate for the 8 cohort studies (RR = 1.09; 95% CI, 1.06–1.13; p_h = 0.541) was similar to that for the 14 case-control studies (RR = 1.12; 95% CI, 1.02–1.22; p_h = 0.029) (Table 6.17S). One cohort study (Gram et al. 2005) and 5 case-control studies (Johnson et al. 2000; Kropp and Chang-Claude 2002; Gammon et al. 2004a; Mechanic et al. 2006; Young et al. 2009) were excluded from the analysis because estimates were based on a no active/no passive exposure reference group. One study was excluded because the age cutpoint was 16 years of age or younger (Egan et al. 2003). These exclusions did not meaningfully alter the summary estimate (RR = 1.09; 95% CI, 1.06–1.13; p_h = 0.597). Nineteen studies (7 cohort, 12 case-control) estimated risk when smoking was initiated at 16 or fewer years of age (RR = 1.08; 95% CI, 1.00–1.15; p_h = 0.065).

Only 13 studies (6 cohort, 7 case-control) reported estimates of risk when smoking initiation occurred from 16–19 years of age (RR = 1.11; 95% CI, 1.07–1.15; p_h = 0.757). Additionally, results for the meta-analysis of the 19 studies that reported estimates for smoking initiation at 20 years of age and older showed a significant summary estimate (RR = 1.08; 95% CI, 1.05–1.12; p_h = 0.672) (Table 6.17S). This estimate was only slightly lower than that for those younger than 20 years of age. Thus, these studies did not reveal a clear trend for a change in summary estimates across categories for age at initiation. Few studies tested for trends across age categories and estimates for most studies included in the meta-analyses were similar for those 16 years of age and younger and those 20 years of age or younger (Spearman rank-order correlation = 0.81, p <0.0001). Of note, the estimates in the tails of the distribution of the RRs across studies with either significant protective or increased estimates are from studies that used a no active/no passive exposure reference group. Taken together, the meta-analyses of these studies did not provide clear evidence that initiating smoking during adolescence or young adulthood confers any greater risk than initiation at older ages.

Smoking Before or During First Full-Term Pregnancy

The effects of smoking before versus after a first full-term pregnancy may be confounded by effects associated with early age at smoking initiation and age at first pregnancy (Cui et al. 2006). Few studies have examined the risk of smoking during pregnancy, for which the results may differ for women who stop smoking when pregnant than for those who continue to smoke during pregnancy. Lawlor and colleagues (2004) conducted a meta-analysis of 11 studies, 2 of which were based on smoking during pregnancy (Innes and Byers 2001; Fink and Lash 2003), to assess the effect of smoking before a first full-term pregnancy. The analysis included estimates from their own cohort, the British Women's Heart and Health Study, 2 earlier cohort studies (Egan et al. 2002; Reynolds et al. 2004b), and 8 case-control studies (Adami et al. 1988; Hunter et al. 1997; Lash and Aschengrau 1999, 2002; Innes and Byers 2001; Band et al. 2002; Kropp and Chang-Claude 2002; Fink and Lash 2003). Based on 6,528 cases, the RR summary estimate was 1.07 (95% CI, 0.94–1.22). The risk was attenuated when 2 influential studies with wide CIs (Lash and Aschengrau 1999; Innes and Byers 2001) were removed (RR = 1.03; 95% CI, 0.93–1.14), which also reduced heterogeneity. These 2 studies and an earlier one based on the NHS-I (Hunter et al. 1997) were 3 of the 11 that reported statistically significant results.

DeRoo and colleagues (2011b) published a meta-analysis on a larger number of studies than the earlier review by Lawlor and colleagues (2004). These authors included an additional 15 reports (Morabia et al. 1996; Egan et al. 2003; Al-Delaimy et al. 2004; Gammon et al. 2004a; Gram et al. 2005; Li et al. 2005; Olson et al. 2005; Cui et al. 2006; Lissowska et al. 2006; Ha et al. 2007; Magnusson et al. 2007; Prescott et al. 2007; Rollison et al. 2008; Slattery et al. 2008; Young et al. 2009). They excluded 2 studies of smoking during first pregnancy based on linked birth and cancer registry data (Innes and Byers 2001; Fink and Lash 2003) and 1 study (Hunter et al. 1997) that overlapped with a subsequent report (Egan et al. 2002); these 3 (i.e., all but Egan et al. 2002) were included in Lawlor and colleagues' (2004) meta-analysis. DeRoo and colleagues' (2011b) summary estimate was 1.11 (95% CI, 1.06–1.16). This higher estimate than that of Lawlor and colleagues (2004) was influenced by several large cohort and case-control studies published between January 2004 and 2009.

Twenty-two studies included in this report provided RR estimates for smoking before or during first full-term pregnancy for the meta-analysis: 9 cohort studies (Al-Delaimy et al. 2004; Lawlor et al. 2004; Reynolds et al. 2004b; Gram et al. 2005; Olson et al. 2005; Cui et al. 2006; Ha et al. 2007; Luo et al. 2011b; Xue et al. 2011) and 13 case-control studies (Innes and Byers 2001; Band et al. 2002; Kropp and Chang-Claude 2002; Fink and Lash 2003; Gammon et al. 2004a; Li et al. 2005; Lissowska et al. 2006; Magnusson et al. 2007; Prescott et al. 2007; Rollison et al. 2008; Slattery et al. 2008; Young et al. 2009; DeRoo et al. 2011a) (Table 6.16S, see notes for Figure 6.39 for exclusions). For these 22 studies, the RR summary estimate was 1.10 (95% CI, 1.04–1.17; p_h = <0.001) (Table 6.17S). This summary result is higher and statistically significant compared with that of Lawlor and colleagues (2004), primarily because it included 5 recent, large cohort studies that reported significant estimates (Al-Delaimy et al. 2004; Gram et al. 2005; Olson et al. 2005; Luo et al. 2011b; Xue et al. 2011). The RR summary estimate was 1.16 (95% CI, 1.12–1.20; p_h = 0.746) for the 9 cohort studies and 1.05 (95% CI, 0.94–1.18; p_h = 0.001) for the 13 case-control studies (Table 6.17S). After excluding 1 cohort study (Gram et al. 2005) and 3 case-control studies (Gammon et al. 2004a; Kropp and Chang-Claude 2002; Young et al. 2009) that were based on estimates using only a no active/no passive exposure reference group, the overall summary estimate increased slightly (RR = 1.11; 95% CI, 1.05–1.18; p_h ≤0.001) due to the increase for case-control studies (RR = 1.09; 95% CI, 0.96–1.23; p_h ≤0.001). The additional exclusion of the 3 case-control studies, which reported estimates for smoking only during pregnancy (Innes and Byers 2001; Fink and Lash 2003; DeRoo et al. 2011a), further increased the RR for case-control studies to 1.13 (95% CI, 1.05–1.23), eliminating the significant heterogeneity (p_h = 0.727). In addition, the overall summary estimate was increased to 1.16 (95% CI, 1.12–1.20; p_h = 0.830). Thus, the 3 case-control studies with risk estimates for smoking only during pregnancy produced heterogeneity and attenuated summary estimates, but those that used a no active/no passive exposure reference group had little or no effect on the summary estimates.

Figure 6.39

Forest plot showing association between smoking before or during first full-term pregnancy and risk for breast cancer, based on the subset of cohort studies before 2012 and case-control studies published from 2000 to 2011 (n = 22). Note: * = cohort study; (more...)

These summary estimates for smoking before or during first pregnancy are only slightly higher than those for ever smoking, and they are quite similar to those for duration of 20 or more years and 20 or more pack-years of smoking. Overall, the studies conducted since 2000 do not provide clear evidence that smoking before first pregnancy confers a greater risk than smoking at any other time in a woman's life. Taken together, the results for earlier age at smoking initiation and smoking before first pregnancy do not support the hypothesis that smoking has greater carcinogenic effects during periods in which breast tissue is less differentiated and theoretically more susceptible.

Menopausal Status

Risk for breast cancer is associated with duration and level of estrogen exposure and evidence suggests that the phenotypic heterogeneity of breast cancer is linked to menopausal status (Lipton 2005). Spicer and Pike (2005) hypothesized that because menopause is associated with a decreased rate of breast cell proliferation compared with that in the premenopausal period, it modifies susceptibility to exposures such as obesity, hormone therapy, and alcohol. It is plausible that if smoking affects hormone metabolism, the risk of breast cancer due to smoking is similarly modified by menopause.

For some risk factors, such as obesity, risk estimates differ when analyses are stratified by menopausal status (van den Brandt et al. 2000). Menopause could modify the risk of breast cancer associated with smoking by altering hormone metabolism and the sensitivity of breast tissue to tobacco carcinogens (Kendall et al. 2007). Women who smoke—primarily current, heavy smokers—experience menopause at an earlier age than those who do not smoke (Baron et al. 1990; Midgette and Baron 1990; Kato et al. 1998; Mikkelsen et al. 2007; Sun et al. 2012) and have a higher risk for osteoporosis even when on estrogen therapy (North American Menopause Society 2010), which may be due to altered estrogen metabolism and lower estrogen levels (Kiel et al. 1992). These observations support an anti-estrogenic effect of smoking (Kendall et al. 2007). However, smokers also tend to be leaner, drink more alcohol, and have poorer diets than nonsmokers; all of these factors are also associated with early menopause (Sampson 2002). Moreover, results from several studies have not provided sufficient evidence that estradiol levels in current smokers differ from those in former or never smokers (Longcope and Johnston 1988; Baron et al. 1990; Key et al. 1991; Cassidenti et al. 1992; Kendall et al. 2007; Arslan et al. 2009). Even so, in a recent cross-sectional analysis of the association between endogenous hormones and several risk factors for breast cancer, the levels of all sex hormones were reported to be higher for women who smoked 15 or more cigarettes per day than for never smokers. Hormonal levels, particularly for estrogen, were attenuated with adjustment for BMI, whereas further adjustment for alcohol did not result in any meaningful change (Endogenous Hormones and Breast Cancer Collaborative Group 2011).

Previous reviews did not find evidence to suggest that menopause modifies the risk of breast cancer from smoking (Egan et al. 2002; Terry and Rohan 2002). The Collaborative Group on Hormonal Factors in Breast Cancer and colleagues (2002) reported an RR of 1.07 (standard error = 0.05) for premenopausal women and an RR of 1.12 (standard error = 0.06) for women 50 years of age and older who experienced natural menopause.

Several studies have examined menopausal status specifically, and several have conducted formal tests for interaction with smoking. Table 6.18S shows results for ever smoking from 14 studies stratified by menopausal status and 6 studies in which the entire study sample included only one menopausal group. Of the 20 studies listed, 7 reported data for pack-years of smoking for both menopausal groups and 3 reported results for postmenopausal women only. Overall, results for ever smoking were highly variable for both premenopausal and post-menopausal risks. Menopause can be difficult to define in observational studies, however, which can result in misclassification bias, particularly when age is the only criterion for menopause. Furthermore, not all studies in Table 6.18S accounted for residual confounding by hormonal status or use of HRT. A sensitivity analysis (Table 6.18S) provides the RR for case-control studies, with the study by Kruk (2007) excluded because of its extreme estimates.

Menopausal Status—Ever Smoking

Among 17 studies, 3 cohort (Hiatt and Fireman 1986; Manjer et al. 2000b; Xue et al. 2011) and 3 case-control (Band et al. 2002; Lissowska et al. 2006; Kruk 2007) studies reported a significantly increased risk for premenopausal women associated with ever smoking. All but 6 studies had an RR greater than 1.10, and no significant inverse associations were reported. The summary estimate (RR) associated with premenopausal smoking for all studies combined was 1.26 (95% CI, 1.11–1.43; p_h ≤0.001) (Table 6.18S). This RR was reduced to 1.18 (95% CI, 1.08–1.29; p_h = 0.005) when the single outlying estimate for a case-control study (RR = 2.34) (Kruk 2007) was excluded (Table 6.18S). The summary estimate for the case-control studies was reduced from 1.30 (95% CI, 1.04–1.62; p_h = 0.001) to 1.20 (95% CI, 1.02–1.42; p_h = 0.075) when the outlier was excluded, a value that is quite similar to the RR for the 4 cohort studies (RR = 1.16; 95% CI, 1.08–1.24; p_h = 0.628) (Table 6.18S).

A total of 17 studies reported results for smoking by postmenopausal women. Four out of 6 cohort studies reported positive associations of 1.10 or greater, of which 2 were significant (Olson et al. 2005; Luo et al. 2011b). One cohort study (Xue et al. 2011), however, reported a significant inverse association (RR = 0.91; 95% CI, 0.86–0.96). Three of the 11 case-control studies that included postmenopausal women reported significant positive associations for this group (Johnson et al. 2000; Li et al. 2005; Kruk 2007). Five studies reported an RR greater than 1.10, and none reported a significant inverse association. The summary estimate associated with postmenopausal women for all studies combined was 1.10 (95% CI, 1.02–1.19; p_h = 0.001) (Table 6.18S). This RR was reduced to 1.07 (95% CI, 1.00–1.14; p_h = 0.001) when the outlying estimate (RR = 1.76) (Kruk 2007) was removed. The summary estimate for the case-control studies was reduced from 1.13 (95% CI, 1.01–1.27; p_h = 0.001) to 1.07 (95% CI, 0.98–1.16; p_h = 0.147) when the outlier was removed, an estimate virtually identical to the estimate based on the 6 cohort studies (RR = 1.07; 95% CI, 0.97–1.19; p_h = 0.001) (Table 6.18S).

Several issues should be considered when evaluating these results for ever smoking in premenopausal versus postmenopausal women. First, the estimates reported by Kruk (2007) are outliers for both menopausal groups and, when these estimates are included, the summary estimates (RRs) are positively biased. The significant inverse association in postmenopausal women reported by Xue and colleagues (2011) for the NHS-I contrasts with the significant positive associations reported by two other large cohort studies, Women's Health Initiative (Luo et al. 2011b) and the Iowa Women's Health Study (Olson et al. 2005). Previous reports from NHS-I (London et al. 1989; Egan et al. 2002) have indicated a null association and no meaningful difference between menopausal groups, but they were based on fewer cases and less follow-up time than the recent report by Xue and colleagues (2011).

Among the case-control studies, the study by Johnson and colleagues (2000) also provided estimates for smoking by menopausal status that used a small no active/no passive exposure reference group: for premenopausal women, OR = 2.3 (95% CI, 1.2–4.5), and for post-menopausal women, OR = 1.5 (95% CI, 1.0–2.3). These estimates contrast strongly with their results when using a no active-only reference group (Table 6.18S): premenopausal women (OR = 1.0; 95% CI, 0.80–1.3); postmenopausal women (OR = 1.2; 95% CI, 1.0–1.4). No other study has contrasted estimates using these two reference groups by menopausal status. It is important to note that Johnson and colleagues (2000) restricted their analysis using a no active/no passive exposure reference group to the approximate 60% of women who reported their residential exposure to passive smoke for at least 90% of their lifetime. This makes a direct comparison of their results difficult.

Menopausal Status—Pack-Years of Smoking

Several studies have reported results for pack-years by menopausal status: 7 for premenopausal and 10 for postmenopausal (Table 6.18S). The results across these studies are variable and inconsistent. Two cohort studies that reported results for premenopausal women (Reynolds et al. 2004b; Xue et al. 2011) found significantly increased risks for the highest category of pack-years of smoking (≥30) (RR = 2.05; 95% CI, 1.20–3.49 and RR = 1.27; 95% CI, 1.16–1.38, respectively). Among 5 case-control studies offering estimates for premenopausal women, 2 reported statistically significant positive associations for the highest level of pack-years of smoking (Band et al. 2002: RR = 1.69; 95% CI, 1.10–2.61 for ≥20 pack-years; Slattery et al. 2008: RR = 1.6; 95% CI, 1.1–2.4 for >15 pack-years) in non-Hispanic Whites, while 1 (Johnson et al. 2000) found significant increased risks for fewer pack-years of exposure (RR = 2.30; 95% CI, 1.10–4.70 for 11–20, and RR = 2.40; 95% CI, 1.20–4.70 for 1–10 pack-years). The other 2 studies (Zheng et al. 2002a; Ahern et al. 2009) were essentially null for the association between breast cancer and pack-years of smoking in premenopausal women.

Four cohort and six case-control studies reported estimates for the association of pack-years of smoking with breast cancer in postmenopausal women. The pooled estimate for 20 or more pack-years was statistically significant in Reynolds and colleagues (2004b) (pooled RR = 1.17; 95% CI, 1.01–1.35), Olson and colleagues (2005) (pooled RR = 1.17; 95% CI, 1.04–1.31), and Luo and colleagues (2011b) (pooled RR = 1.12; 95% CI, 1.03–1.21). Luo and colleagues (2011b) also found a statistically significant increased risk for smoking more than 50 pack-years (RR = 1.18; 95% CI, 1.02–1.22). In contrast, there was a trend toward lower risk with more pack-years of smoking in Xue and colleagues (2011), which reached statistical significance for the highest level of more than 15 pack-years (RR = 0.88; 95% CI, 0.79–0.99). In contrast, only two (Johnson et al. 2000; Li et al. 2005) of the six case-control studies reported statistically significant associations for the highest level of pack-years of smoking in postmenopausal women (RR = 1.60; 95% CI, 1.00–2.60, and RR = 1.30; 95% CI, 1.00–2.60, respectively). It should be noted that the estimates reported by Johnson and colleagues (2000) were based on a no active/no passive exposure reference group.

Only one cohort study (Reynolds et al. 2004b) formally tested for interaction between menopause and smoking across multiple measures. This study found no significant results by the likelihood ratio test for duration of smoking (p = 0.80); cigarettes/per day (p = 0.42); pack-years of smoking (p = 0.07); and years since cessation (p = 0.76).

Menopausal Status—Summary

The results in Table 6.18S indicate that considerable heterogeneity exists among studies that report estimates for the association of smoking with breast cancer by menopausal status, although none of the summary estimates was associated with statistically significant publication bias. Although the results of the meta-analysis suggest that risk is greater in premenopausal than in postmenopausal women, it remains uncertain whether the association of smoking with breast cancer differs by menopausal status.

Hormone Receptor Status

ERs and progesterone receptors (PRs) mediate the effects of estrogen and progesterone on the growth, proliferation, and differentiation of breast tumors; response to hormonal treatment; recurrence; and survival. Palmer and Rosenberg (1993) postulated that the expression status of ERs could modulate the anti-estrogenic effects of smoking, and Meek and Finch (1999) reported that smoking alters the expression of ERs. The presence (+) or absence (−) of ER expression in breast tumors is increasingly recognized as a potential biomarker of etiologically distinct subtypes (Anders et al. 2008; Bertucci et al. 2009; Onitilo et al. 2009). Consequently, some of the more recent studies stratify analyses on ER expression. The information added by cross-classification with the status of PRs remains controversial. In addition to reporting the expression status of ERs and PRs, studies have begun to cross-classify cases by the status of human epidermal growth factor receptor 2 (HER2) because the so-called triple negative phenotype (i.e., the combination of negative ER, PR, and HER2 status) is increasingly recognized as distinct and having a poor prognosis (Bauer et al. 2007; ReisFilho and Tutt 2008; Gluz et al. 2009).

Many studies have assessed the risk of breast cancer based on the status of ER expression. In 2 early, small hospital-based studies, Daniell (1980) and Ranocchia and colleagues (1991) observed that the prevalence of smoking was higher among breast cancer cases with ER− tumors than in cases with ER+ tumors, but these studies were underpowered and the data were not rigorously analyzed. Table 6.19S summarizes data from 17 studies that assessed whether the risk for breast cancer differs by ER expression status for ever smoking or by the highest category of cigarettes smoked per day. Althuis and colleagues (2004) reviewed 10 of the studies shown in Table 6.19S (McTiernan et al. 1986; Stanford et al. 1987; Cooper et al. 1989; London et al. 1989; Yoo et al. 1997; Morabia et al. 1998; Huang et al. 2000a; Manjer et al. 2001; Britton et al. 2002; Cotterchio et al. 2003) with hormone receptor-defined breast cancer and found no evidence for a differential association between breast cancer and smoking by hormonal phenotype, but they did not provide a numerical analysis. Four of these studies (Cooper et al. 1989; London et al. 1989; Yoo et al. 1997; Morabia et al. 1998) were reviewed in the 2006 Surgeon General's report.

Hormone Receptor Status—Ever Smoking

Findings from the 17 studies on the association of ever smoking with breast cancer defined by ER status are highly inconsistent (Table 6.19S). Four studies reported significantly increased risks for ER+ breast cancer with ever smoking, with RRs ranging from 1.15–1.42 (Yoo et al. 1997; Al-Delaimy et al. 2004; Li et al. 2005; Luo et al. 2011b). Two studies reported significantly increased risks for ER−breast cancer (Cooper et al. 1989; Manjer et al. 2001), with RRs ranging from 1.63–2.41. One study (Morabia et al. 1998) reported significantly increased risks for both ER+ and ER− breast cancer, with a somewhat stronger association with ER− (RR = 4.01; 95% CI, 1.90–8.46) than ER+ (RR = 2.28; 95% CI, 1.56–3.35) tumors. This study is the only one that used a no active/no passive exposure reference group (Morabia et al. 1998). The recent case-control study by Rabstein and colleagues (2010) found a significant inverse association with ER+ breast cancer (RR = 0.79; 95% CI, 0.65–0.95), but no association with ER− breast cancer. The remaining studies reported null results (McTiernan et al. 1986; Stanford et al. 1987; London et al. 1989; Huang et al. 2000a; Britton et al. 2002; Cotterchio et al. 2003; Gammon et al. 2004a; Lissowska et al. 2006; Trivers et al. 2009).

Hormone Receptor Status—Cigarettes Smoked Per Day

Only six studies have reported results on the association between cigarettes smoked per day and breast cancer defined by ER status, and these are also very inconsistent (Table 6.19S). One study (London et al. 1989) reported a significantly increased risk for ER+ breast cancer with 25 or more cigarettes smoked per day (RR = 1.38; 95% CI, 1.04–1.84), and another (Al-Delaimy et al. 2004) reported significantly increased risks for ER+ breast cancer with fewer cigarettes smoked per day: RR = 1.46; 95% CI, 1.14–1.87 for 5–14 cigarettes smoked per day; and RR = 1.45; 95% CI, 1.09–1.93 for 1–4 cigarettes smoked per day. Manjer and colleagues (2001) found significantly increased risks for ER− breast cancer regardless of number of cigarettes smoked per day, and Morabia and colleagues (1998) reported significantly increased risks for both ER+ and ER− breast cancer regardless of level, although the association was somewhat stronger in women with ER− tumors. The remaining two studies reported essentially null results (Li et al. 2005; Lissowska et al. 2006).

Hormone Receptor Status—Methodologic Issues

Some issues affect the interpretation of published results for smoking and breast cancer by hormone receptor status. First, all but two studies (London et al. 1989; Al-Delaimy et al. 2004) in Table 6.19S used case-control designs, which are more subject to bias than other study designs. Second, methods for detecting ER expression have changed over time, and some older studies were based on a mix of methods (Ross and Hortobagyi 2005). Many studies rely on incomplete or inaccurate pathology and medical records and ER status is generally not obtained on in situ tumors. The completeness of data for ER status in the studies in Table 6.19S ranged from 40–100%. Third, few studies have identified consistent risk factors for the ER−phenotype other than race and younger age (Althuis et al. 2004), and thus potential confounders for this type of breast cancer are not yet well characterized. Last, researchers are not sure whether ER status should be cross-classified with PR status. The most recent studies have characterized breast cancer phenotypes by the combination of ER, PR, and HER2 status or by gene expression phenotypes (luminal A, B, basal-like) (Kwan et al. 2009; Trivers et al. 2009). Kabat and colleagues (2011) recently published an analysis from the Women's Health Initiative on risk of the triple negative phenotype compared with risk for ER+ breast cancer in relation to smoking. RRs (not shown in Table 6.19S) were significantly increased in women with ER+ breast cancer for former smoking (1.14; 95% CI, 1.05–1.24), duration of 30 or more years (1.14; 95% CI, 1.01–1.28), 40 or more pack-years of smoking (1.25; 95% CI, 1.06–1.44), and younger than 20 years of age at initiation (1.16; 95% CI, 1.05–1.28). In contrast, there were no significant associations in women with triple negative breast cancer. These results are quite similar to those reported by Luo and colleagues (2011b), who also analyzed tumors by ER/PR status only (not HER2) data from the Women's Health Initiative cohort.

Hormone Receptor Status—Summary

In summary, results from studies conducted to date are inconsistent on the association of smoking with different phenotypes of breast cancer defined on the basis of hormone receptor status.

Meta-Analysis of Breast Cancer Risk Associated with Measures of Secondhand Smoke

A total of 19 new published reports (7 cohort, 12 case-control) were reviewed together with the 20 reports (5 cohort, 15 case-control) that were previously abstracted and analyzed for the 2006 Surgeon General's report. Three of these update previous reports from the same studies and one overlaps with a current report (Table 6.24S). RR and OR estimates were based on either single estimates or were pooled across exposure strata and classified similarly to the eight categories reported in the 2006 Surgeon General's report. The same statistical procedures used in the meta-analyses for active cigarette smoking were used for the analyses of exposure to secondhand smoke. Sensitivity analyses considered study design, sample size, and magnitude of exposure effect.

Table 6.24S provides a listing of the 39 reports for 34 studies, of which 9 overlap with results on the same study population. Of these, 7 are included in the meta-analyses because they are either the most recent or complete reports from their study. In the case of 1 cohort study (California Teachers Study) and 1 case-control study (Carolina Breast Cancer Study), the best exposure estimates for specific categories were selected for inclusion in the meta-analyses: California Teachers Study (Reynolds et al. 2004b, 2009) and Carolina Breast Cancer Study (Millikan et al. 1998; Marcus et al. 2000; Mechanic et al. 2006). A total of 34 separate reports were included in the broadest category of exposure for the meta-analyses: Most comprehensive. RR and OR estimates were pooled across exposure levels to fit into one of the meta-analysis categories when necessary.

Measures of Exposure to Secondhand Smoke

This meta-analysis used eight categories of measures of exposure to secondhand smoke. These categories are not mutually exclusive, and assignments are presented in Table 6.24S.

1. Spouse/partner: This category was based on exposure during adulthood from a spouse or partner who was a smoker.
2. Adult—home: This category was based on exposure during adulthood from any smoker in the home. The category Spouse/partner is a subset of Adult—home because the location of exposure was assumed to be in the home.
3. Adult—workplace: Based on exposure during adulthood from smokers at the workplace, an estimate from this category could be used for any adult. However, most studies with a measure for exposure at the workplace had a measure for exposure at home that took precedence.
4. Childhood: This category was based on exposure during childhood to any smoker in the home. Among the 15 studies that provided a childhood estimate, the age definition of childhood varied. Sixteen, 18, or 21 years of age defined the end of childhood exposure in 7 studies (Smith et al. 1994; Marcus et al. 2000; Gammon et al. 2004a; Bonner et al. 2005; Rollison et al. 2008; Chuang et al. 2011; Luo et al. 2011b), and the remaining studies did not define a specific cutoff for age (Johnson et al. 2000; Liu et al. 2000; Kropp and Chang-Claude 2002; Lin et al. 2008; Pirie et al. 2008; Ahern et al. 2009; Reynolds et al. 2009; Xue et al. 2011).
5. Adulthood and childhood (or lifelong): This category was based on lifelong exposure during childhood and adulthood from any individual in any setting. Only seven studies defined exposure in this manner (Smith et al. 1994; Johnson et al. 2000; Kropp and Chang-Claude 2002; Reynolds et al. 2004b; Pirie et al. 2008; Ahern et al. 2009; Luo et al. 2011b).
6. Adult—any source: This category was based on the broadest, most inclusive measure available for exposure during adulthood from any source in the following priority: a general estimate for all sources of exposure if available, a comprehensive home exposure, spouse/partner exposure, and workplace exposure. Twenty-six non-overlapping reports included measures that were coded for this category based on a number of descriptive measures, including a general report for overall and nonspecific exposure to passive smoke as an adult (Johnson et al. 2000; Kropp and Chang-Claude 2002; Ahern et al. 2009); exposure specifically noted as from a spouse or partner (Sandler et al. 1985a; Hirose et al. 1995; Morabia et al. 1996; Jee et al. 1999; Nishino et al. 2001; Alberg et al. 2004; Gammon et al. 2004a; Kruk 2007; Roddam et al. 2007; Pirie et al. 2008); cohabitants in general (Smith et al. 1994; Delfino et al. 2000; Liu et al. 2000; Mechanic et al. 2006; Lin et al. 2008; Reynolds et al. 2009; Xue et al. 2011); coworkers (Bonner et al. 2005; Hanaoka et al. 2005); or a combination of cohabitants and coworkers (Shrubsole et al. 2004; Sillanpaa et al. 2005a; Lissowska et al. 2006; Luo et al. 2011b).
7. Ever in lifetime: Based on a report of exposure to passive smoke during either childhood or adulthood in studies that assessed exposure across the lifetime, this category can include, for example, an estimate based on exposure during adulthood if exposure during childhood was also assessed and included in the risk estimate. The category Adulthood and childhood is a subset of Ever in lifetime. Twenty nonoverlapping reports had measures that were coded for this category based on definitions that ranged from very general to specific. One study estimate was based on exposure during childhood and adulthood (Ahern et al. 2009); 5 were based on lifetime exposure in the home (Lash and Aschengrau 1999, 2002; Zhao et al. 1999; Bonner et al. 2005; Slattery et al. 2008); 4 were based on any exposure from a spouse or a parent during the lifetime (Gammon et al. 2004a; Gram et al. 2005; Pirie et al. 2008; Chuang et al. 2011); 1 was based on having lived with a smoker or been exposed to a smoker outside of the home (Hanaoka et al. 2005); 5 were based on having lived with a smoker or been exposed at the workplace (Smith et al. 1994; Morabia et al. 1996; Johnson et al. 2000; Kropp and Chang-Claude 2002; Rollison et al. 2008); and 4 were based on any exposure during childhood or adulthood without information about location or source of exposure (Reynolds et al. 2009; Young et al. 2009; Xue et al. 2011). The broadest measure for Ever in lifetime was selected in those studies that reported more than one category of exposure during childhood and adulthood. The home was the most frequently defined location for exposure; outside the home and/or at the workplace were identified less frequently. Studies varied widely in specificity and rigor of the definition of lifetime exposure.
8. Most comprehensive: This category was based on the broadest, most inclusive estimate of exposure available from each study. In the meta-analysis, this was always either Adult—any source or Ever in lifetime, with preference for the latter when both estimates were reported. A careful evaluation was made of the independent contributions of each category to the summary estimate for the Most comprehensive (see Comparison of Adult—Any Source with Ever in Lifetime for Most Comprehensive).

This meta-analysis applied some changes to the studies reviewed in the 2006 Surgeon General's report, including the exclusion of two mortality studies (Hirayama 1984; Wartenberg et al. 2000), the inclusion of a study conducted in China and published prior to 2005 (Hirose et al. 1995), and changes to several estimates for five studies (Smith et al. 1994; Millikan et al. 1998; Jee et al. 1999; Nishino et al. 2001; Gammon et al. 2004a). These changes are detailed in the notes for Table 6.24S. Risk estimates were abstracted for each study, classified into the eight categories described previously, and tabulated together with information on adjusted covariates, including reproductive risk factors, alcohol use, BMI, family history, and menopausal status. The most fully adjusted estimates were selected when available, and a random effects model was used to pool estimates across strata (e.g., race/ethnicity, menopausal status, or dose levels) when necessary.

Adjustment for Selected Covariates

The majority of studies that evaluated exposure to passive smoke adjusted for covariates, most often referencing those that were related to reproduction or estrogen, but also family history, use of alcohol, and BMI. Of the 34 separate studies, only 4 did not adjust for any covariate or adjusted for age only (Sandler et al. 1985a; Jee et al. 1999; Alberg et al. 2004; Metsola et al. 2005).

Most Comprehensive Measures of Passive Smoking

Among the 34 studies included in the meta-analysis of passive smoking and risk for breast cancer, only 7 did not report estimates for measures of active smoking (Jee et al. 1999; Liu et al. 2000; Nishino et al. 2001; Bonner et al. 2005; Pirie et al. 2008; Reynolds et al. 2009; Chuang et al. 2011). Eight of the 34 studies were based on Asian populations (Hirose et al. 1995; Jee et al. 1999; Zhao et al. 1999; Liu et al. 2000; Nishino et al. 2001; Shrubsole et al. 2004; Hanaoka et al. 2005; Lin et al. 2008), and 8 studies included data on the interaction between genotype and smoking for risk for breast cancer (Delfino et al. 2000; Alberg et al. 2004; Gammon et al. 2004a; Metsola et al. 2005; Sillanpaa et al. 2005a; Lissowska et al. 2006; Mechanic et al. 2006; Slattery et al. 2008). Figure 6.40 presents the 34 studies (10 cohort and 24 case-control) that were based on the Most comprehensive category, which was derived from either the Adult—any source (n = 14) or Ever in lifetime (n = 20) measures. Meta-analysis provided an overall summary RR of 1.14 (95% CI, 1.06–1.23), but with significant heterogeneity (p_h <0.001) (Table 6.25S). The funnel plot in Figure 6.41 shows evidence of significant skewness, suggesting the presence of publication bias, as indicated by the lack of smaller negative studies. This was further confirmed (Figure 6.40) by Begg's rank correlation test (z = 2.30, p = 0.02) and the Egger test (bias = 1.41, p = 0.007). Stratification by study design (Table 6.25S) revealed that the heterogeneity resulted mainly from the variation among the 24 case-control studies (RR = 1.27; 95% CI, 1.11–1.44; p_h <0.000), although significant heterogeneity was also found for the 10 cohort studies (RR = 1.02; 95% CI, 0.95–1.10; p_h = 0.038).

Figure 6.40

Forest plot showing the association between the most comprehensive measure of exposure to secondhand smoke and risk for breast cancer, based on the subset of cohort and case-control studies published before 2012 (n = 34). Note: * = cohort study; ^ = case-control (more...)

Figure 6.41

Funnel plot showing estimates in the meta-analysis of the most comprehensive measure of exposure to secondhand smoke with risk for breast cancer, based on the subset of cohort and case-control studies published before 2012 (n = 34). Note: l = cohort study; (more...)

The funnel plot in Figure 6.41 also indicates the presence of some studies with extreme outlier estimates (i.e., those that fall well outside the boundaries of the funnel) (Smith et al. 1994; Morabia et al. 1996; Lash and Ashengrau 1999; Zhao et al. 1999; Kruk 2007). Because extreme estimates can strongly affect a summary estimate, these outlier studies were inspected more closely for potential problems with study design. The case-control studies by Kruk (2007) and Zhao and colleagues (1999) appeared to include smokers along with nonsmokers in the analysis of exposure to passive smoke. Furthermore, the number of cases and controls reported in the tables in both of these studies could not be reconciled with totals provided in the text or in other tables. Excluding these two studies (Table 6.25S) attenuated the overall risk estimate (RR = 1.08; 95% CI, 1.01–1.14; p_h = 0.001; n = 32) and the risk estimate for the case-control studies (RR = 1.14; 95% CI, 1.04–1.26; p_h = 0.003; n = 22).

The extreme estimate from Smith and colleagues (1994) was based on a very small subset of cases and controls (n = 193) that represented only 27% of the nonsmokers (n = 703) in the full study. Other studies were also based on a small number of cases. For example, estimates reported by Morabia and colleagues (1996) were based on only 126 cases (620 controls), and the results from the cohort study by Lin and colleagues (2008) were based on only 140 incident cases. However, although small studies are statistically more likely to produce extreme estimates, these studies adjusted for appropriate covariates and did not have other limitations to their respective designs.

Limitations in study design were detected in three other studies that did not provide extreme estimates. Two studies included an unknown percentage of deceased persons for whom information was collected from proxies and did not adjust for menopausal status (Lash and Aschengrau 1999, 2002), and one study included both incident and prevalent cases based on medical records and did not adjust for covariates relevant to breast cancer (including menopausal status) other than age (Jee et al. 1999). Excluding these three studies plus Kruk (2007) and Zhao and colleagues (1999) (Table 6.25S) did not meaningfully alter the overall summary estimate (RR = 1.07; 95% CI, 1.01–1.13; p_h = 0.002; Begg z = 2.21; p = 0.03; Egger bias = 0.98; p = 0.02, n = 29).

Because the funnel plot in Figure 6.41 indicated publication bias stemming from small studies, 5 more studies with fewer than 100 cases were excluded (Sandler et al. 1985a; Smith et al. 1994; Delfino et al. 2000; Nishino et al. 2001; Alberg et al. 2004) in addition to the 5 with design limitations (Jee et al. 1999; Lash and Aschengrau 1999, 2002; Zhao et al. 1999; Kruk 2007). The summary estimate (RR) then became 1.06 (95% CI, 1.00–1.12; n = 24). Although significant heterogeneity remained (p_h = 0.010), excluding the 10 studies reduced publication bias, as expected (Begg z = 1.79; p = 0.07; Egger bias = 0.95; p = 0.05). However, the estimate by Morabia and colleagues (1996) remained an extreme outlier. Excluding this study resulted in a summary estimate (RR) of 1.04 (95% CI, 0.99–1.09; p_h = 0.131; n = 23). Figure 6.42 shows the forest plot for the 23 studies that remained after the exclusions. The accompanying funnel plot in Figure 6.43 shows that publication bias (Begg z = 1.35; p = 0.18; Egger bias = 0.68; p = 0.12; see note for Figure 6.42) and the effects of case-control studies with extreme estimates well outside of the 95% CI of the funnel no longer leveraged the RR. The case-control studies that were removed did not appear to have better assessments of exposure than many other studies that were included. While the estimate for the cohort study by Lin and colleagues (2008) is just outside the outer margin of the funnel, it is balanced by the estimate for the case-control study by Kropp and Chang-Claude (2002).

Figure 6.42

Forest plot showing the association between the most comprehensive measure of secondhand smoke and risk for breast cancer, based on the subset of cohort and case-control studies published before 2012, excluding studies with design or analysis issues (n (more...)

Figure 6.43

Funnel plot for estimates in the meta-analysis of the most comprehensive measure of exposure to secondhand smoke with risk for breast cancer, based on the subset of cohort and case-control studies published before 2012, excluding studies with design or (more...)

Comparison of Adult—Any Source with Ever in Lifetime for Most Comprehensive

An evaluation was made of whether an additional source of bias in the meta-analysis of the Most comprehensive category was due to a mix of the Ever in lifetime (n = 20) and Adult—any source (n = 14) measures of exposure (see Table 6.24S for listing of studies). As described previously, the Ever in lifetime category uses a broad definition of passive exposure—that is, it includes studies with estimates based on exposure to passive smoke during childhood and adulthood. In contrast, the Adult—any source category provides a measure mainly of current exposure that often includes both source (spouse, partner) and location (home, workplace). The Most comprehensive category was based on the Ever in lifetime category when both results were available.

The summary RR for all 26 studies with an Adult—any source estimate was 1.15 (95% CI, 1.03–1.28; p_h <0.001) (Table 6.25S), and the summary estimate for the subset of 14 studies contributing to the Most comprehensive category was nearly identical: RR = 1.15; 95% CI, 0.94–1.39 (data not shown). In contrast, all 20 studies with an estimate for the category Ever in lifetime were included in the Most comprehensive category. The summary RR for these 20 studies was 1.11 (95% CI, 1.03–1.20; p_h <0.001) (Table 6.25S). There was less indication of publication bias for the 14 studies in the Adult—any source exposure category (Begg z = 0.38, p = 0.70; Egger bias = 0.23, p = 0.88) than for the 20 studies in the Ever in lifetime category (Begg z = 2.60, p = 0.009; Egger bias = 1.84, p = 0.001), as shown in funnel plots in Figure 6.44.

Figure 6.44

Funnel plots for estimates in the meta-analysis of Adult—any source (n = 14) and Ever in lifetime (n = 20) measures of exposure to secondhand smoke that contributed to the Most comprehensive exposure category, based on the subset of cohort and (more...)

When small studies, those with design or analysis issues, and the 1 outlier study (Morabia et al. 1996) were excluded from each of the two categories, the RRs were attenuated similarly. The exclusion of 6 of the 14 Adult—any source studies resulted in an RR of 1.01 (95% CI, 0.88–1.17; n = 8) (data not shown). The exclusion of 5 of the 20 Ever in lifetime studies resulted in an RR of 1.03 (95% CI, 0.99–1.07; n = 15) (Table 6.25S). Thus, the exclusion of these 11 studies did not produce differential bias between the Adult—any source and Ever in lifetime categories that were used for the Most comprehensive RR. The RR for all studies in the Adult—any source and Ever in lifetime categories as well as in the reduced analyses after exclusions were similar. Thus, one of these two categories does not provide a better assessment of exposure than the other, nor is one of the categories a greater source of bias in the meta-analyses than the other.

Comparison of Premenopausal with Postmenopausal for Most Comprehensive

The meta-analysis for the Most comprehensive measure of exposure to passive smoke was stratified on menopausal status for all studies with available estimates (Table 6.25S). The summary estimate (RR) for 17 studies with data on exposure among premenopausal women was 1.45 (95% CI, 1.20–1.75; p_h <0.001) (Table 6.25S). The funnel plot in Figure 6.45A displays substantial publication bias associated with an excess of positive estimates from smaller studies with data for premenopausal women (Begg z = 2.97, p = 0.003; Egger bias = 2.61, p = 0.001). Fourteen case-control studies produced a summary estimate of 1.52 (95% CI, 1.23–1.87; p_h <0.001) for premenopausal women, and 3 cohort studies produced a summary estimate for this group of 1.23 (95% CI, 0.69–2.19; p_h = 0.027) (Table 6.25S). In contrast, the summary estimate (RR) for 17 studies with data for postmenopausal women was 1.11 (95% CI, 0.99–1.25; p_h = 0.001) (Table 6.25S). Although the estimate for 1 study was an extreme outlier (Kruk 2007), the funnel plot for postmenopausal women in Figure 6.45B does not reveal substantial bias (Begg z = 0.91, p = 0.37; Egger bias = 0.78, p = 0.31). For postmenopausal women, the summary estimate for 13 case-control studies was 1.18 (95% CI, 1.00–1.39; p_h = 0.004), and the summary estimate for 4 cohort studies was 1.01 (95% CI, 0.85–1.20; p_h = 0.035) (Table 6.25S). According to Figure 6.45A, estimates for studies that reported exposure among premenopausal women were not randomly distributed within the boundaries of the funnel plot; an excess of small studies had positive estimates; and a few studies were extreme outliers, appearing outside the upper level of the pseudo 95% CI. This is less apparent in the funnel plot for studies that reported exposure among postmenopausal women (Figure 6.45B).

Figure 6.45

Funnel plots showing estimates in the meta-analysis of premenopausal (n = 17) and postmenopausal (n = 17) status for the Most comprehensive measure of exposure to secondhand smoke with risk for breast cancer, based on the subset of cohort and case-control (more...)

Exclusion of the 11 studies with design or analysis limitations, small samples, or extreme estimates had a major impact on all estimates for the Most comprehensive exposure category, with the summary estimate for all studies decreasing from 1.14 to 1.04 (Table 6.25S). The summary estimate for premenopausal women decreased from 1.45 to 1.21 (Table 6.25S, Figure 6.46A), and the summary estimate for postmenopausal women decreased from 1.11 to 1.04 (Table 6.25S, Figure 6.46B).

Figure 6.46

Forest plots showing the association between premenopausal (n = 12) and postmenopausal (n = 13) status for the Most comprehensive measure of exposure to secondhand smoke with risk for breast cancer, based on the subset of cohort and case-control studies (more...)

Taken together, these sensitivity and stratified analyses suggest that the meta-analysis of the Most comprehensive exposure category, which included both the Adult—any source and Ever in lifetime definitions of exposure, produced highly heterogeneous results, and that the summary estimate was subject to bias from small case-control studies, some of which had extreme (outlier) estimates (Table 6.25S). The summary result for premenopausal women may have been influenced by smaller case-control studies that reported statistically significant, positive associations. However, among the three cohort studies, the report by Hanaoka and colleagues (2005), with relatively few breast cancer cases, stands out as reporting a significant increased risk for breast cancer in premenopausal women (RR = 2.6; 95% CI, 1.3–5.2) and a reduced risk in postmenopausal women (RR = 0.70; 95% CI, 0.4–1.0). These findings are inconsistent with those from the other two larger and more recent cohort studies that reported no significantly increased or decreased risk in either premenopausal women, RR = 1.04; 95% CI, 0.79–1.38 (Reynolds et al. 2009); or postmenopausal women, RR = 1.22; 95% CI, 0.97–1.52 (Reynolds et al. 2009) and RR = 1.09; 95% CI, 0.92–1.29 (Luo et al. 2011b).

Other Categories of Passive Exposure

For comparison with the findings of the 2006 Surgeon General's report, Table 6.26S summarizes the results of the meta-analysis for other exposure categories: childhood, childhood and adulthood, and adulthood (spouse, home, and workplace). Most of the summary estimates are similar to those in the 2006 report, but several changed because of new studies published since 2006 with data for these categories.

There are now 15 studies (5 cohort and 10 case-control) with estimates for passive smoking exposure from the spouse versus 9 in the 2006 Surgeon General's report. The summary RR for these studies is 1.22 (95% CI, 1.05–1.42; p_h = 0.001), similar to the 2006 estimate of 1.17 (95% CI, 0.96–1.44; p_h = 0.002). However, when 7 studies with design or analysis issues are excluded, the RR drops to 1.05 (95% CI, 0.97–1.13; p_h = 0.185). The previous Surgeon General's report provided a summary RR of 1.01 (95% CI, 0.85–1.19; p_h = 0.006) for 8 studies reporting passive exposure at home. There are now 20 studies for home exposure (7 cohort and 13 case-control), for which the summary RR is 1.16 (95% CI, 1.02–1.31; p_h = 0.001. When 8 studies with design or analysis issues are excluded, the estimate drops considerably, in this case to 1.02 (95% CI, 0.94–1.11; p_h = 0.061). The new summary estimates for exposure in the workplace (RR = 1.03; 95% CI, 0.92–1.15) and during childhood (RR = 1.01; 95% CI, 0.95–1.07) are quite close to the estimates in the 2006 Surgeon General's report. For exposure in childhood and adulthood, however, the previous estimate, based on 4 studies, was 1.39 (95% CI, 0.88–2.18; p_h = 0.021) compared to 1.09 (95% CI, 0.95–1.24; p_h = 0.102) based on a new total of 7 studies.

Results for these exposure categories by menopausal status are considered unstable because they are based on nine or fewer studies. Moreover, only two of the summary RRs are significant: exposure to secondhand smoke at home among premenopausal women (n = 9; RR = 1.35; 95% CI, 1.03–1.78; p_h = 0.003); and, exposure during childhood among postmenopausal women (n = 4; RR = 1.15; 95% CI, 1.03–1.28; p_h = 0.888). In general, point estimates tend to be higher in premenopausal than post-menopausal women, but it is difficult to interpret this difference because the CIs are wide and overlapping.

In Utero Exposure to Secondhand Smoke

Several studies have examined the possible association of in utero exposure to passive smoking with breast cancer in adulthood. Park and colleagues (2008) published a meta-analysis of seven case-control (Sandler et al. 1985b; Sanderson et al. 1996, 1998; Weiss et al. 1997; Innes and Byers 2001; Titus-Ernstoff et al. 2002; Park et al. 2006) and two cohort (Strohsnitter et al. 2005; Sanderson et al. 2006) studies of possible associations between passive exposure to maternal or paternal smoking in utero and subsequent risk of breast cancer. The summary estimate (RR) from Park and colleagues' (2008) meta-analysis was 1.03 (95% CI, 0.93–1.15) for the case-control studies and 0.59 (0.41–0.85) for the cohort studies. However, these results are difficult to interpret because the meta-analysis included a case-control study of active smoking by the participant during pregnancy and her subsequent risk of breast cancer (Innes and Byers 2001), two of the case-control studies appear to have had overlap for the diagnosis time period and geographic location (Sanderson et al. 1996, 1998), and one of the cohort studies had breast cancer mortality as an outcome (Sanderson et al. 2006). Additionally, most studies did not adequately control for potential confounders.

Estimates from three studies that examined in utero exposure to maternal smoking and adjusted for potential confounders in addition to age were 1.3 (95% CI, 0.9–2.1) for women, 50–64 years of age, in western Washington state (Sanderson et al. 1996), 1.1 (95% CI, 0.8–1.4) for women younger than 55 years of age in three areas of the United States (Weiss et al. 1997), and 1.21 (95% CI, 0.99–1.47) for women in Warsaw and Lodz, Poland, who were in most instances postmenopausal (Park et al. 2006). In the case-control study by Park et al. (2006), the risk estimate was essentially the same in participants whose mothers had smoked only at times other than during pregnancy (OR = 1.22; 95% CI, 0.81–1.84). Taken together, these studies do not provide consistent evidence that in utero exposure to secondhand smoke is associated with breast cancer.

Secondhand Smoke Exposure and Genotype Interaction

Eight case-control studies have examined potential modification of the effect of exposure to passive smoking by the NAT2 phenotype (Millikan et al. 1998; Delfino et al. 2000; Morabia et al. 2000; Chang-Claude et al. 2002; Alberg et al. 2004; Kocabas et al. 2004; Sillanpaa et al. 2005a; Conlon et al. 2010). None found statistically significant modification of effect, and results were inconsistent across studies for the direction of effect modification. Four studies suggested that risk may be increased in women with the rapid NAT2 phenotype (Millikan et al. 1998; Morabia et al. 2000; Chang-Claude et al. 2002; Kocabas et al. 2004), and three studies suggested that risk is increased with the slow phenotype (Alberg et al. 2004; Sillanpaa et al. 2005a; Conlon et al. 2010). One study (Delfino et al. 2000) reported nonsignificant findings, but numerical results were not provided. A case-only study by Lash and colleagues (2005) also reported a nonsignificant interaction that suggested increased risk in women with the slow NAT2 phenotype. Two studies (Millikan et al. 1998; Morabia et al. 2000) reported that menopausal status further modified the interaction, but they disagreed substantially in their findings. Millikan and colleagues (1998) reported an OR of 2.3 (95% CI, 0.9–6.2) in premenopausal women exposed to passive smoke who had the NAT2 rapid phenotype, as opposed to an OR of 1.2 (95% CI, 0.5–2.8) for women who had the slow phenotype. In contrast, for premenopausal women, Morabia and colleagues (2000) found that risk was not modified by phenotype; RRs were approximately 3.0 for both rapid and slow phenotypes. For postmenopausal women, Millikan and colleagues (1998) found that risk was lower in those with the rapid phenotype (OR = 0.8; 95% CI, 0.4–1.8) than in those with the slow phenotype (OR = 1.9; 95% CI, 0.7–5.2), while Morabia and colleagues (2000) reported that risk was higher in postmenopausal women with the rapid phenotype (OR = 11.6; 95% CI, 2.2–62.2) than in those with the slow phenotype (OR = 1.1; 95% CI, 0.3–4.3). Ambrosone and colleagues (2008) performed a meta-analysis of several of these studies of exposure to passive smoking and derived a summary estimate (RR) of 1.13 (95% CI, 0.81–1.56) for slow acetylators and 1.19 (95% CI, 0.84–1.68) for rapid acetylators. Significant heterogeneity was present among the estimates, particularly for the rapid phenotype, and thus the authors did not calculate summary estimates by menopausal status.

Only a few studies have examined interactions between exposure to passive smoke and genotypes other than NAT1/2. Mordukhovich and colleagues (2010) reported that women with exposure to passive smoking were more likely to have p53-negative tumors, and Lilla and colleagues (2005) examined effect modifications by the SULT1A1 gene using data from the same German case-control study as Chang-Claude and colleagues (2002). No statistically significant interaction was found. The study suggested a possible three-way interaction between exposure to passive smoke, SULT1A1, and NAT2, but this was not statistically significant. Millikan and colleagues (2004) found no evidence of an interaction between exposure to passive smoke and MnSOD on risk for breast cancer, and Gaudet and colleagues (2005) reported that risk for breast cancer increased with exposure to passive smoke regardless of the MnSOD genotype. In a case-only analysis, Bradbury and colleagues (2006) reported departures from multiplicative interaction for the COM-THL genotype and history of ever being exposed to passive smoking (OR = 2.0; 95% CI, 0.8–5.2) or of having lived with a smoker after 20 years of age (OR = 2.8; 95% CI, 0.8–10). Evaluating this result requires assumptions that the interaction is multiplicative rather than additive and that genotype and exposure are independent.

Adverse Health Outcomes in Cancer Patients and Survivors

As survival from cancer has improved over time, the question of the potential impact of cigarette smoking on cancer patients and survivors is of increasing relevance. This topic is of growing importance, because survival following the diagnosis of many types of cancer has improved markedly during the past decades, such that the prevalence of cancer survivors in the United States is now more than 14 million and increasing (Siegel et al. 2012). This section reviews the evidence concerning cigarette smoking as a risk factor for adverse health outcomes in cancer patients during treatment and their survivorship.

Evidence Summary

This extensive chapter covers a wide range of evidence on tobacco and cancer. It returns to the topic of smoking and lung cancer, which was the primary focus of the 1964 report. The section on lung cancer describes changes in cigarettes and cigarette smoke, since the first report, and tracks the changes in the types of lung cancer over time. The composition of cigarette smoke has changed to have a greater concentration of tobacco-specific nitrosamines and lower concentration of PAHs. These and other changes in cigarette smoke may have led to the rise of adenocarcinoma of the lung; the changes in composition of tobacco smoke may have implications for other cancers and, possibly, other smoking-caused diseases. The evidence reviewed shows that the risk of lung cancer associated with smoking has increased over time and during the same time period machine-measured yields of tar and nicotine have decreased.

Since the 1964 report, many additional types of cancer have been found to be causally associated with smoking. This report finds the evidence to be sufficient to infer that smoking causes liver cancer and cancer of the colon and rectum. In the 2004 report, the strength of evidence was considered to be “suggestive but not sufficient to infer a causal relationship” for both of these cancers; however, additional studies have sufficiently strengthened the evidence to infer a causal relationship between smoking and liver cancer and cancer of the colon or rectum. For liver cancer, there are several potential confounding factors, including alcohol consumption and infection with hepatitis B virus and hepatitis C virus. The review in this chapter shows that confounding can be set aside as the explanation for the association of smoking with liver cancer. With regard to colorectal cancer, the evidence has emerged in more recent decades linking smoking with this cancer. The epidemiologic studies indicate that the risk is manifest only after an exposure of long duration and, consequently, only recently have epidemiologic studies identified the association of smoking with colorectal cancer.

The association between smoking and breast cancer received detailed consideration in both the 2004 and 2006 reports of the Surgeon General. Substantial new evidence has been reported during the decade following the release of these reports. This report provides a detailed synthesis of the literature on both active smoking and exposure to secondhand smoke. The evidence shows that carcinogens in tobacco smoke do reach the tissues of the breast and active smoking affects sex hormones, which are relevant to breast cancer risk in women, in complicated ways. There are many epidemiologic studies of both active smoking and exposure to secondhand smoke; they are subject to potential bias from the reporting of smoking and exposure to secondhand smoke, and confounding is also a concern. Overall, meta-analysis finds the associations of active smoking and exposure to secondhand smoke with breast cancer risk to be weak, and the evidence was judged to be suggestive that smoking causes breast cancer.

For prostate cancer, the evidence did not show an association of smoking with incidence. The evidence confirmed the association of smoking with higher mortality from prostate cancer and also indicated that smoking may enhance progression. The biological processes underlying the suggestive association between cigarette smoking and prostate cancer mortality, case fatality, and, more seriously, unfavorable pathologic characteristics of the tumor require further investigation, particularly because incidence is not associated with smoking.

This chapter includes a new topic related to smoking and cancer, which bridges across all types of cancer—the impact of smoking on the outcome of cancer. The extensive review, included in this chapter, shows that smoking does adversely affect outcome for those developing cancer. The implications of this finding are clear: patients who develop cancer and who are still smoking need to quit. A cancer patient, who is a current cigarette smoker, can improve his/her prognosis by quitting smoking at any time. Evidence-based smoking cessation services for cancer patients are likely to have substantial benefits for survival.

Chapter Conclusions

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