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 differences between and within tumor types. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Many identified TFs are significantly associated with survival outcome. To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis.
Highlights
Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators
We represented every gene by its feature vector of TFbinding scores, where motif information was summarized across all promoter, intronic, and intergenic chromatin accessible sites assigned to the gene
Our patient-specific inference of networks incorporating chromatin (PSIONIC) method provides a systematic framework for integrating resources on regulatory genomics with tumor expression data to better understand gene regulation in cancers and infer patient-specific Transcription factors (TFs) networks
Summary
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. Data-driven computational strategies may help to infer patient-specific transcriptional regulatory programs and to identify and therapeutically target the TFs that lead to cancer phenotypes. Such strategies could be used to personalize therapy and improve patient outcomes. Extensive pan-cancer genomic analyses have shown that the same genes and pathways are targeted by somatic alterations across multiple tumor types These results suggest that pancancer modeling of regulatory programs could be informative, as similar TFs may be dysregulated across cancers. Inferring patientspecific transcriptional regulatory programs may identify and eventually enable therapeutic targeting of transcriptional mechanisms underlying gynecologic malignancies for individualized treatment
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