Format

Send to

Choose Destination
Cell. 2010 Dec 10;143(6):1005-17. doi: 10.1016/j.cell.2010.11.013. Epub 2010 Dec 2.

An integrated approach to uncover drivers of cancer.

Author information

1
Department of Biological Sciences, Columbia University, New York, NY 10027, USA.

Abstract

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. 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 data set. Our analysis correctly identified known drivers of melanoma and predicted multiple 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 candidate drivers with biological, and possibly therapeutic, importance in cancer.

Comment in

PMID:
21129771
PMCID:
PMC3013278
DOI:
10.1016/j.cell.2010.11.013
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center