github link
Accession IconGSE23884

An Integrated Approach to Uncover Drivers of Cancer

Organism Icon Homo sapiens
Sample Icon 31 Downloadable Samples
Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Submitter Supplied Information

Description
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.
PubMed ID
Total Samples
31
Submitter’s Institution

Samples

Show of 0 Total Samples
Filter
Add/Remove
Accession Code
Title
Cell line
Processing Information
Additional Metadata
No rows found
Loading...