Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance

Large-scale genomic studies have identified multiple somatic aberrations in breast cancer, including copy number alterations and point mutations. Still, identifying causal variants and emergent vulnerabilities that arise as a consequence of genetic alterations remain major challenges. We performed w...

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Bibliographic Details
Published in:	Cell 2016-01, Vol.164 (1-2), p.293-309
Main Authors:	Marcotte, Richard, Sayad, Azin, Brown, Kevin R., Sanchez-Garcia, Felix, Reimand, Jüri, Haider, Maliha, Virtanen, Carl, Bradner, James E., Bader, Gary D., Mills, Gordon B., Pe’er, Dana, Moffat, Jason, Neel, Benjamin G.
Format:	Article
Language:	eng
Subjects:	Algorithms Breast Neoplasms - drug therapy Breast Neoplasms - genetics Breast Neoplasms - pathology Cell Line, Tumor Class I Phosphatidylinositol 3-Kinases Cluster Analysis Drug Resistance, Neoplasm Gene Dosage Gene Expression Profiling Genome-Wide Association Study Humans Linear Models Nuclear Proteins - genetics Phosphatidylinositol 3-Kinases Transcription Factors - genetics
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Summary:	Large-scale genomic studies have identified multiple somatic aberrations in breast cancer, including copy number alterations and point mutations. Still, identifying causal variants and emergent vulnerabilities that arise as a consequence of genetic alterations remain major challenges. We performed whole-genome small hairpin RNA (shRNA) “dropout screens” on 77 breast cancer cell lines. Using a hierarchical linear regression algorithm to score our screen results and integrate them with accompanying detailed genetic and proteomic information, we identify vulnerabilities in breast cancer, including candidate “drivers,” and reveal general functional genomic properties of cancer cells. Comparisons of gene essentiality with drug sensitivity data suggest potential resistance mechanisms, effects of existing anti-cancer drugs, and opportunities for combination therapy. Finally, we demonstrate the utility of this large dataset by identifying BRD4 as a potential target in luminal breast cancer and PIK3CA mutations as a resistance determinant for BET-inhibitors. [Display omitted] •We screened 77 breast cancer lines using a genome-wide pooled shRNA library•We developed an algorithm (siMEM) to improve identification of context-dependent genes•Integrating screen results with genomic data reveals potential “drivers”•BRD4 is essential for luminal cancer, and mutant PIK3CA confers BET-I resistance Pooled shRNA screens of a large panel of breast cancer cell lines, coupled with an improved analytical tool, siMEM, and integration with genomic and proteomic data, identify general and context-dependent essential genes in breast cancer. This study constitutes the largest functional characterization of breast cancer to date.
ISSN:	0092-8674 1097-4172