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Cell. 2016 Jan 14;164(1-2):293-309. doi: 10.1016/j.cell.2015.11.062.

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

Author information

1
Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada.
2
The Donnelly Centre, University of Toronto, ON M5S 3E1, Canada.
3
Columbia University, New York, NY 10027, USA.
4
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA; Department of Medicine, Harvard Medical School, Boston, MA 02215, USA.
5
Department of Systems Biology, Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
6
The Donnelly Centre, University of Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, ON M5S 3E1, Canada.
7
Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Laura and Isaac Perlmutter Cancer Centre, NYU-Langone Medical Center, NY 10016, USA. Electronic address: benjamin.neel@nyumc.org.

Abstract

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.

PMID:
26771497
PMCID:
PMC4724865
DOI:
10.1016/j.cell.2015.11.062
[Indexed for MEDLINE]
Free PMC Article

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