2. Academic Validation
  3. Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

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

  • Nat Commun. 2019 Sep 25;10(1):4369. doi: 10.1038/s41467-019-12291-6.
Hatice U Osmanbeyoglu 1 2,  Fumiko Shimizu 3,  Angela Rynne-Vidal 4,  Direna Alonso-Curbelo 5,  Hsuan-An Chen 5,  Hannah Y Wen 6,  Tsz-Lun Yeung 4,  Petar Jelinic 7,  Pedram Razavi 8,  Scott W Lowe 5,  Samuel C Mok 4,  Gabriela Chiosis 3,  Douglas A Levine 7,  Christina S Leslie 9
Affiliations

Affiliations

  • 1 Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. osmanbeyogluhu@pitt.edu.
  • 2 Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. osmanbeyogluhu@pitt.edu.
  • 3 Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • 4 Department of Gynecologic Oncology and Reproductive Medicine-Research, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • 5 Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • 6 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • 7 Laura and Isaac Perlmutter Cancer Center, New York University Langone Medical Center, New York, NY, USA.
  • 8 Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • 9 Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. cleslie@cbio.mskcc.org.
PMID: 31554806 DOI: 10.1038/s41467-019-12291-6
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.

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