Radiogenomic Analysis of F-18-Fluorodeoxyglucose Positron Emission Tomography and Gene Expression Data Elucidates the Epidemiological Complexity of Colorectal Cancer Landscape

Efstathios-Iason Vlachavas; Eleftherios Pilalis; Olga Papadodima; Dirk Koczan; Stefan Willis; Sven Klippel; Caixia Cheng; Leyun Pan; Christos Sachpekidis; Alexandros Pintzas; Vasilis Gregoriou; Antonia Dimitrakopoulou-Strauss; Aristotelis Chatziioannou

doi:10.1016/j.csbj.2019.01.007

Radiogenomic Analysis of F-18-Fluorodeoxyglucose Positron Emission Tomography and Gene Expression Data Elucidates the Epidemiological Complexity of Colorectal Cancer Landscape

Comput Struct Biotechnol J. 2019 Jan 25:17:177-185. doi: 10.1016/j.csbj.2019.01.007. eCollection 2019.

Authors

Efstathios-Iason Vlachavas^{1

2

3}, Eleftherios Pilalis^{1

3}, Olga Papadodima¹, Dirk Koczan⁴, Stefan Willis⁵, Sven Klippel⁵, Caixia Cheng⁶, Leyun Pan⁶, Christos Sachpekidis⁶, Alexandros Pintzas¹, Vasilis Gregoriou¹, Antonia Dimitrakopoulou-Strauss⁶, Aristotelis Chatziioannou^{1

3}

Affiliations

¹ Institute of Biology, Medicinal Chemistry & Biotechnology, National Hellenic Research Foundation, Athens, Greece.
² Department of Molecular Biology and Genetics, Democritus University of Thrace, 68100 Dragana, Greece.
³ Enios Applications Private Limited Company, A17671 Athens, Greece.
⁴ Core Facility Micro-Array-Technology, Center of Medical Research, University of Rostock, Germany.
⁵ Surgical Clinic A, Klinikum Ludwigshafen, Germany.
⁶ Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany.

Abstract

Purpose: Transcriptomic profiling has enabled the neater genomic characterization of several cancers, among them colorectal cancer (CRC), through the derivation of genes with enhanced causal role and informative gene sets. However, the identification of small-sized gene signatures, which can serve as potential biomarkers in CRC, remains challenging, mainly due to the great genetic heterogeneity of the disease.

Methods: We developed and exploited an analytical framework for the integrative analysis of CRC datasets, encompassing transcriptomic data and positron emission tomography (PET) measurements. Profiling data comprised two microarray datasets, pertaining biopsy specimen from 30 untreated patients with primary CRC, coupled by their F-18-Fluorodeoxyglucose (FDG) PET values, using tracer kinetic analysis measurements. The computational framework incorporates algorithms for semantic processing, multivariate analysis, data mining and dimensionality reduction.

Results: Transcriptomic and PET data feature sets, were evaluated for their discrimination performance between primary colorectal adenocarcinomas and adjacent normal mucosa. A composite signature was derived, pertaining 12 features: 7 genes and 5 PET variables. This compact signature manifests superior performance in classification accuracy, through the integration of gene expression and PET data.

Conclusions: This work represents an effort for the integrative, multilayered, signature-oriented analysis of CRC, in the context of radio-genomics, inferring a composite signature with promising results for patient stratification.

Keywords: 18F-FDG PET; ACADM, Acyl-Coenzyme A Dehydrogenase; AUC, Area Under the Curve; CCT7, Chaperonin Containing TCP1 Subunit 7; CD44, CD44 Molecule (Indian Blood Group); CRC, Colorectal cancer; Colorectal cancer; DE, Differentially Expressed; FD, Fractal Dimension; FDG, F-18-Fluorodeoxyglucose; GDC, Genomics Data Commons; GEO, Gene Expression Omnibus; GSTP1, Glutathione S-Transferase Pi 1; KIT, Proto-Oncogene Receptor Tyrosine Kinase; Lasso, least absolute shrinkage and selection operator; MFA, Multiple Factor Analysis; Microarray analysis; PCs, Principal Components; PET, Positron Emission Tomography; ROC, Receiver-operator Characteristic curve; Radiogenomics; SUV, Standardized Uptake Value; TCGA; TCGA-COAD, The Cancer Genome Atlas-Colon Adenocarcinoma; Translational bioinformatics.