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

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


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Breast Neoplasms / drug therapy
  • Breast Neoplasms / genetics*
  • Breast Neoplasms / pathology
  • Cell Cycle Proteins
  • 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


  • BRD4 protein, human
  • Cell Cycle Proteins
  • Nuclear Proteins
  • Transcription Factors
  • Class I Phosphatidylinositol 3-Kinases
  • PIK3CA protein, human