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Series GSE74702 Query DataSets for GSE74702
Status Public on Jan 14, 2016
Title The Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance (pooled shRNA screens)
Organism Homo sapiens
Experiment type Genome variation profiling by array
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 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/.

 
Overall design Pooled shRNA screens of 77 cell lines, with dropout trends for each screen measured in triplicate across 3 time-points (with a few exceptions), resulting in 8-9 arrays per screen, and a total of 621 arrays.
Web link http://neellab.github.io/bfg/
 
Contributor(s) Sayad A
Citation(s) 26771497
Submission date Nov 05, 2015
Last update date Apr 14, 2016
Contact name Benjamin G. Neel
E-mail(s) benjamin.neel@nyumc.org
Organization name NYU Perlmutter Cancer Centre
Lab Neel Lab
Street address 550 First Avenue
City New York
State/province New York
ZIP/Postal code 10016
Country USA
 
Platforms (1)
GPL21133 GMAP-UTS520601 array
Samples (621)
GSM1938098 184A1_T0_A
GSM1938099 184A1_T0_B
GSM1938100 184A1_T1_A
Relations
BioProject PRJNA301998

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE74702_RAW.tar 2.4 Gb (http)(custom) TAR (of CEL)
Raw data provided as supplementary file
Processed data included within Sample table

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