Abstract

Abstract Epigenomic data on transcription factor occupancy and chromatin accessibility 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 cell line chromatin accessibility data with large tumor expression data sets and model the effect of enhancers on transcriptional programs in multiple cancers. We generated a new ATAC-seq data set profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and applied 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 differences between and within tumor types. Many of theidentified TFs were significantly associated with survival outcome in basal breast, uterine serous and endometrioid carcinomas. To validate one PSIONIC-derived prognostic TF, we performed immunohistochemical analyses in 31 uterine serous tumors for ETV6 and confirmed that the corresponding protein expression pattern was also significantly associated with prognosis. Moreover, PSIONIC-predicted activity for MTF1 in cell line models correlated with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Citation Format: Hatice Ulku Osmanbeyoglu, Fumiko Shimizu, Angela Rynne-Vidal, Tsz-Lun Yeung, Petar Jelinic, Samuel C. Mok, Gabriela Chiosis, Douglas A. Levine, Christina Leslie. Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3370.

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