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
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.