Abstract B1-60: Predicting drug response by profiling the epigenome: Super-enhancers as biomarkers

Abstract

Abstract Identifying biomarkers that predict clinical responses to anticancer therapies is an important challenge in oncology. A desirable biomarker is a cell-identifying feature that can distinguish between drug responsive and unresponsive cell types. Most of the best studied biomarkers are gene mutations or copy-number changes, where the genomic alteration is the key defining feature. However, not all differences in clinical responses can be attributed to genetic differences. Therefore, it is critical that we identify other cellular features that can predict response. A recently described epigenomic feature, termed super-enhancer (SE), defines key cell identity and disease genes and SEs are effectors of initiating and maintaining cell type-specific gene expression programs. In this work we generate super-enhancer maps across multiple cancer cell lines and connect those maps to response to different drug treatments. We show that super-enhancers can be used as biomarkers to predict response to drugs across multiple cell-line models of cancer. This work suggests that interrogating epigenomic features, and in particular super-enhancers, can be a powerful biomarker discovery platform and can enable rational patient selection and therapeutic strategies. Citation Format: David A. Orlando, Michael R. McKeown, Mei Wei Chen, Cindy Collins, Matthew G. Guenther, Christian C. Fritz. Predicting drug response by profiling the epigenome: Super-enhancers as biomarkers. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-60.