The Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance (pooled shRNA screens)

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 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. The T0 measurements for the EFM19, HCC1954, HCC38 screens were omitted for technical reasons. T0 measurements, regardless of cell line, represent the initial abundance of shRNAs before cell line-specific selection effects, leading to highly correlated T0 measurements across cell lines. Our analyses showed a median correlation of 0.92 between pairs of T0 arrays from different cell lines, compared to correlations of 0.94-0.97 for replicate arrays within a cell line, a median correlation of 0.79 between T1 arrays of different cell lines and median correlation of 0.68 between T2 arrays from different cell lines. As a result, we used to T0 measurements of the MCF7 screen to provide initial shRNA abundance measurements for the HCC1954 and HCC38 screens, and T0 measurements from the SW527 screen to provide initial measurements for the EFM19 screen. Additional formatted data can be found at http://neellab.github.io/bfg/. Code and tutorials for the siMEM algorithm can be found at http://neellab.github.io/simem/.

ORGANISM(S): Homo sapiens

PROVIDER: GSE74702 | GEO | 2016/01/14

SECONDARY ACCESSION(S): PRJNA301998

REPOSITORIES: GEO