Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers.

ABSTRACT: Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory

SUBMITTER: Osmanbeyoglu HU 

PROVIDER: S-EPMC6761109 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature