A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer

Michael R. Kelly, Kamila Wisniewska, Matthew J. Regner, Michael W. Lewis, Andrea A. Perreault, Eric S. Davis, Douglas H. Phanstiel, Joel S. Parker and Hector L. Franco ()
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Michael R. Kelly: University of North Carolina at Chapel Hill
Kamila Wisniewska: University of North Carolina at Chapel Hill
Matthew J. Regner: University of North Carolina at Chapel Hill
Michael W. Lewis: University of North Carolina at Chapel Hill
Andrea A. Perreault: University of North Carolina at Chapel Hill
Eric S. Davis: University of North Carolina at Chapel Hill
Douglas H. Phanstiel: University of North Carolina at Chapel Hill
Joel S. Parker: University of North Carolina at Chapel Hill
Hector L. Franco: University of North Carolina at Chapel Hill

Nature Communications, 2022, vol. 13, issue 1, 1-22

Abstract: Abstract The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we utilize an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We systematically probe the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we construct chromatin interaction maps that enable the annotation of direct target genes for these super-enhancers and confirm their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach examines a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.

Date: 2022
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DOI: 10.1038/s41467-022-31919-8

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