Identifying gene expression patterns associated with drug-specific survival in cancer patients

• Type of Material: Article
• Authors: Bridget Neary, Georgia Institute of Technology; Jie Zhou, Georgia Institute of Technology; Peng Qiu, Emory University
• Language: English
• Date: 2021-03-02
• Publisher: NATURE RESEARCH
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2021
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-021-84211-y
• Title of Journal or Parent Work: SCIENTIFIC REPORTS
• Volume: 11
• Issue: 1
• Start Page: 5004
• End Page: 5004
• Grant/Funding Information: This work was partially supported by funding from the National Science Foundation [CCF1552784 and CCF2007029]; and the Giglio Family Breast Cancer Fund. P.Q. is an ISAC Marylou Ingram Scholar and a Carol Ann and David D. Flanagan Faculty Fellow. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: Multidisciplinary Sciences; Science & Technology - Other Topics; Science & Technology
• Research Categories: Engineering, Biomedical

Abstract

The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions.