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.

Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers

“…In each of these 59 miRNA-mediated gene-cancer-drug combinations, since both the miRNA and the target gene were predictive of survival difference in the corresponding cancer-drug group, these combinations may reflect the functional role of the miRNA markers in affecting drug response through modulating the expression of their target genes. Interestingly, we observed miR-18a and its target gene SMAD4 were predictive of carboplatin response in HNSC, which was consistent with our previous study on the relationship between SMAD4 and carboplatin response 38 . Additionally, we also identified miR-18a to be predictive of the prognosis of HNSC patients treated with cetuximab.…”

Therapeutic implications of transcriptomics in head and neck cancer patient-derived xenografts

“…One of the most significantly differentially regulated genes between HPV+ versus HPV- PDX tumors was C19orf57 ( BRME1 ), which demonstrated 6-fold higher expression in HPV+ models compared to HPV- models (p = 7x10 -17 ; Table 2 ). A previous study of TCGA patients with a variety of tumor types identified an association between high C19orf57 expression with superior survival on cisplatin therapy compared to low C19orf57 expression in cervical squamous cell carcinoma and endocervical adenocarcinoma [ 16 ].…”

“…This protein plays a key role in meiotic recombination by facilitating homology-directed DNA repair via recruitment of recombinases such as RAD51 to DNA double-strand breaks in gametes [ 32 ]. A study that analyzed the correlation between tumor gene expression and patient survival on chemotherapy found that the top gene-drug interaction among patients with cervical squamous cell carcinoma and endocervical adenocarcinoma was that of C19orf57 and cisplatin [ 16 ]. Specifically, high C19orf57 expression was associated with improved survival in the setting of cisplatin therapy relative to the survival of patients whose tumors expressed lower levels of C19orf57 [ 16 ].…”

“…Further transcriptomic analysis between these clusters demonstrated differential regulation of key genes between HPV+ and HPV- PDXs. Among the most highly differentially expressed transcripts by PDX HPV status was C19orf57 , a gene previously correlated with positive response to cisplatin in cervical cancer [ 16 ]. In this PDX cohort, we demonstrate enhanced anti-tumor responses to cisplatin in HNSCC PDXs with higher C19orf57 expression, which underscores the translational relevance of our transcriptomic findings in this PDX collection.…”

Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer

“…It is especially suited for identifying biomarkers predictive of drug response because the molecular assays are performed on pre-treatment samples, representing the state of a tumor at the point when treatment decisions are made. Our group has previously identified molecular features associated with drug-specific survival using the TCGA gene expression, 29 copy number variation, 30,31 protein, 32 and miRNA 33 datasets. While the high dimensionality of the methylation dataset makes it the most challenging to analyze, the promise that DNA methylation holds as a source of biomarkers as well as our success with these other molecular data types make it a critical dataset to explore for molecular features related to drug efficacy.…”