Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

TL;DR

This review compares different computational methods for integrating gene expression and copy number data to prioritize cancer-related genes, introducing a benchmarking framework for performance evaluation.

Contribution

It provides a comprehensive comparison of modeling procedures for data integration in cancer genomics and introduces a transparent benchmarking approach.

Findings

Benchmarking algorithms enable quantitative comparison of methods.

Integration improves identification of cancer driver genes.

Available datasets facilitate reproducibility and method assessment.

Abstract

A variety of genome-wide profiling techniques are available to probe complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher-level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we provide a comparison among various modeling procedures for integrating genome-wide profiling data of gene copy number and transcriptional alterations and highlight common approaches to genomic data integration. A transparent benchmarking procedure is introduced to quantitatively compare the cancer gene prioritization performance of the alternative methods. The benchmarking algorithms and data sets are available at http://intcomp.r-forge.r-project.org