Dataset Information

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

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.

SUBMITTER: Marcotte R

PROVIDER: S-EPMC4724865 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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

Marcotte Richard R Sayad Azin A Brown Kevin R KR Sanchez-Garcia Felix F Reimand Jüri J Haider Maliha M Virtanen Carl C Bradner James E JE Bader Gary D GD Mills Gordon B GB Pe'er Dana D Moffat Jason J Neel Benjamin G BG

Cell 20160101 1-2

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 accompanyin ...[more]

PMID: 26771497

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Project description: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/.

Project description:BackgroundPhosphatase and tensin homolog (PTEN) is one of the most frequently inactivated tumor suppressors in breast cancer. While PTEN itself is not considered a druggable target, PTEN synthetic-sick or synthetic-lethal (PTEN-SSL) genes are potential drug targets in PTEN-deficient breast cancers. Therefore, with the aim of identifying potential targets for precision breast cancer therapy, we sought to discover PTEN-SSL genes present in a broad spectrum of breast cancers.MethodsTo discover broad-spectrum PTEN-SSL genes in breast cancer, we used a multi-step approach that started with (1) a genome-wide short interfering RNA (siRNA) screen of ~ 21,000 genes in a pair of isogenic human mammary epithelial cell lines, followed by (2) a short hairpin RNA (shRNA) screen of ~ 1200 genes focused on hits from the first screen in a panel of 11 breast cancer cell lines; we then determined reproducibility of hits by (3) identification of overlaps between our results and reanalyzed data from 3 independent gene-essentiality screens, and finally, for selected candidate PTEN-SSL genes we (4) confirmed PTEN-SSL activity using either drug sensitivity experiments in a panel of 19 cell lines or mutual exclusivity analysis of publicly available pan-cancer somatic mutation data.ResultsThe screens (steps 1 and 2) and the reproducibility analysis (step 3) identified six candidate broad-spectrum PTEN-SSL genes (PIK3CB, ADAMTS20, AP1M2, HMMR, STK11, and NUAK1). PIK3CB was previously identified as PTEN-SSL, while the other five genes represent novel PTEN-SSL candidates. Confirmation studies (step 4) provided additional evidence that NUAK1 and STK11 have PTEN-SSL patterns of activity. Consistent with PTEN-SSL status, inhibition of the NUAK1 protein kinase by the small molecule drug HTH-01-015 selectively impaired viability in multiple PTEN-deficient breast cancer cell lines, while mutations affecting STK11 and PTEN were largely mutually exclusive across large pan-cancer data sets.ConclusionsSix genes showed PTEN-SSL patterns of activity in a large proportion of PTEN-deficient breast cancer cell lines and are potential specific vulnerabilities in PTEN-deficient breast cancer. Furthermore, the NUAK1 PTEN-SSL vulnerability identified by RNA interference techniques can be recapitulated and exploited using the small molecule kinase inhibitor HTH-01-015. Thus, NUAK1 inhibition may be an effective strategy for precision treatment of PTEN-deficient breast tumors.

Dataset Information

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

Publications

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

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