Dataset Information


An Integrated Approach to Uncover Drivers of Cancer

ABSTRACT: We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma dataset. Our analysis correctly identified known drivers of melanoma and predicted multiple novel tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify novel candidate drivers with biological, and possibly therapeutic, importance in cancer. Effects of knockdown of two genes -TBC1D16 and RAB27A - were tested on four cell lines each, each of them with two different hairpins. As a control, we used a hairping targeting GFP. shGFP experiments were done in biological duplicates or more. The second WM1976-shGFP sample was identified as an outlier by a PCA analysis and was excluded from our analysis.

ORGANISM(S): Homo sapiens  

SUBMITTER: Helen C Causton   Dana Pe'er  Jessica Kim  Uri-David Akavia  Dylan Kotliar  Oren Litvin  Eyal Mozes  Levi A Garraway  Felix Sanchez-Garcia  Panisa Pochanard 

PROVIDER: E-GEOD-23884 | ArrayExpress | 2010-12-01



Dataset's files

Action DRS
E-GEOD-23884.README.txt Txt
E-GEOD-23884.idf.txt Idf Processed Raw
E-GEOD-23884.sdrf.txt Txt
Items per page:
1 - 5 of 5

Similar Datasets

2014-06-17 | E-GEOD-38290 | ArrayExpress
2013-04-15 | E-GEOD-40270 | ArrayExpress
2010-12-01 | GSE23884 | GEO
2015-11-01 | E-GEOD-66862 | ArrayExpress
2016-03-14 | E-GEOD-79156 | ArrayExpress
2008-04-07 | E-GEOD-9717 | ArrayExpress
2014-10-20 | E-GEOD-62218 | ArrayExpress
2010-07-30 | E-GEOD-22064 | ArrayExpress
2010-01-01 | S-EPMC3013278 | BioStudies
2009-07-02 | E-GEOD-7948 | ArrayExpress