The Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance (RNASeq)
Transcriptomics Dataset
FAIR Metrics
0 evaluations
General Information
| Accession | GSE73526 |
| Title | The Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance (RNASeq) |
| Description | Large-scale genomic studies have identified multiple somatic aberrations in breast cancer, including copy number alterations, translocations, 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 new hierarchical linear regression algorithm to score our screen results and integrate them with accompanying detailed genetic and proteomic information, we identify novel vulnerabilities in breast cancer, including new candidate drivers, and reveal general functional genomic properties of cancer cells. Comparisons of gene essentiality with drug sensitivity data suggest potential resistance mechanisms, novel effects of existing anti-cancer drugs, and new 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. 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/. |
| Landing Page | https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73526 |
| Repository | Gene Expression Omnibus |
Tools
used to analyze the dataset
Canned Analyses
generated with the dataset
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.
