Conditional Differential Expression for Biomarker Discovery In High-throughput Cancer Data

Abstract

Biomarkers have important clinical uses as diagnostic, prognostic, and predictive tools for cancer therapy. However, translation from biomarkers claimed in literature to clinical use has been traditionally poor. Importantly, clinical covariates have been shown to be important factors in biomarker discovery in small-scale studies. Yet, traditional differential gene expression analysis for expression biomarkers ignores covariates, which are only accounted for later, if at all. We conjecture that covariate-sensitive biomarker identification should lead to the discovery of more robust and true biomarkers as confounding effects are considered. Here we examine gene expression in more than 750 breast invasive ductal carcinoma cases from The Cancer Genome Atlas (TCGA-BRCA) in the form of RNA-Seq data. Specifically, we focus on differential gene expression with respect to understanding HER2, ER, and PR biology – the three key receptors in breast cancer. We explore methods of differential expression analysis, including non-parametric Mann-Whitney-Wilcoxon analysis, generalized linear models with covariates, and a novel categorical method for covariates. We tested the influence of common patient characteristics, such as age and race, and clinical covariates such as HER2, ER, and PR receptor statuses. More importantly, we show that inclusion of a correlated covariate (e.g. PR status as a covariate in ER analysis) substantially changes the list of differentially expressed genes, removing many likely false positives and revealing genes obscured by the covariate. Incorporation of relevant covariates in differential gene expression analysis holds strong biological importance with respect to biomarker discovery and may be the next step towards better translation of biomarkers to clinical use.