RePhine: An Integrative Method for Identification of Drug Response-Related Transcriptional Regulators

Abstract

Transcriptional regulators (TRs) participate in essential processes in cancer pathogenesis and are critical therapeutic targets. Identification of drug response-related TRs from cell line-based compound screening data is often challenging due to low mRNA abundance of TRs, protein modifications, and other confounders (CFs). In this study, we developed a regression-based pharmacogenomic and ChIP-seq data integration method (RePhine) to infer the impact of TRs on drug response through integrative analyses of pharmacogenomic and ChIP-seq data. RePhine was evaluated in simulation and pharmacogenomic data and was applied to pan-cancer datasets with the goal of biological discovery. In simulation data with added noises or CFs and in pharmacogenomic data, RePhine demonstrated an improved performance in comparison with three commonly used methods (including Pearson correlation analysis, logistic regression model, and gene set enrichment analysis). Utilizing RePhine and Cancer Cell Line Encyclopedia data, we observed that RePhine-derived TR signatures could effectively cluster drugs with different mechanisms of action. RePhine predicted that loss-of-function of EZH2/PRC2 reduces cancer cell sensitivity toward the BRAF inhibitor PLX4720. Experimental validation confirmed that pharmacological EZH2 inhibition increases the resistance of cancer cells to PLX4720 treatment. Our results support that RePhine is a useful tool for inferring drug response-related TRs and for potential therapeutic applications. The source code for RePhine is freely available at https://github.com/coexps/RePhine.

Transcription Elongation Machinery Is a Druggable Dependency and Potentiates Immunotherapy in Glioblastoma Stem Cells

“…1A; ref. 23). By integrating differentially expressed genes (DEG) between 38 GSCs and 5 NSCs (24) with chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) profiling of 160 chromatin regulators, 39 chromatin regulators showed active programs inferred by their target gene expression in GSCs, including transcriptional programs driven by MYC, polycomb repressive complex 2 (PRC2; including EZH2 and SUZ12), and YY1, supporting potential roles in gliomagenesis (Fig.…”

“…Enrichment of the transcription regulator target program was performed using the RePhine algorithm (23). RePhine analyzed the enrichment score of 160 transcription regulator programs in a preranked gene list.…”

Single‐Cell Transcriptome Identifies Drug‐Resistance Signature and Immunosuppressive Microenvironment in Metastatic Small Cell Lung Cancer

“…[23] By integrated analysis, we pro-vided evidence that high expression of FZD8 in WNT pathway was correlated with drug resistance in SCLC malignant cells. [30] Second, ASCL1 and NEUROD1 were indeed the most common markers in the malignant cells from the SCLC patients. However, certain cell types (i.e., A+, N+, and A+N+) are dominant in a given tumor tissue and can be mixed with other types.…”

Discovery of Targets for Immune–Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha

“…BartWeb ( 10 ) infers likely transcription factor or chromatin regulators (TFCR) of an input gene set by leveraging 13,000 chromatin immunoprecipitation sequencing (ChIP-seq) datasets. RePhine ( 11 ) uses regression to ascertain TFCRs underlying response or resistance to an input drug. We previously developed eQTeL ( 12 ), a Bayesian multiomics approach, to find regulators of variation of gene transcription within a population.…”