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

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.

Figure 1

Schematic of the data analysis pipeline used for this study. This figure outlines the major steps of the analysis pipeline.

Figure 2

Heatmap of patient numbers by cancer and drug. This heatmap shows the number of patients in each cancer–drug patient group by cancer site and drugs taken. Cancers are listed by TCGA project identifiers, which are defined here: https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations.

Figure 3

Specific genes are associated with survival following treatment in individual cancer–drug patient groups. Kaplan–Meyer survival curves of patients with the indicated cancer and exposed to the indicated drug, grouped into either high (orange line) or low (blue line) pre-treatment expression levels of the indicated gene. Cancers are referred to their TCGA project identifiers (see Fig. 2). (A) Patients who received temozolomide for lower grade glioma, grouped by XRCC2 expression. (B) Stomach adenocarcinoma patients who took fluorouracil, grouped by TWIST1 expression. (C) Patients grouped by BTG1 expression who took paclitaxel for head and neck squamous cell carcinoma. (D) Patients who received carboplatin for head and neck squamous cell carcinoma, grouped by SMAD4 expression.

Figure 4

Genes that are associated with drug-specific survival in multiple cancers. Kaplan–Meyer survival curves of patients taking the indicated drug, grouped into either high (orange line) or low (blue line) pre-treatment expression levels of the indicated gene across two different cancers. These are the four gene–drug interactions identified in multiple cancers. (A) Survival of breast invasive carcinoma patients (left) and patients with head and neck squamous cell carcinoma (right) who took paclitaxel, grouped by expression of LPP. (B–D) Survival of low-grade glioma patients (left) and glioblastoma (right) patients taking temozolomide, grouped by pre-treatment expression of (B) QRSL1, (C) RP11-338C15.5, and (D) KRT17P7.