An integrated approach to uncover drivers of cancer.
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.
Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
Uri David
UD
Akavia
Oren
O
Litvin
Jessica
J
Kim
Felix
F
Sanchez-Garcia
Dylan
D
Kotliar
Helen C
HC
Causton
Panisa
P
Pochanard
Eyal
E
Mozes
Levi A
LA
Garraway
Dana
D
Pe'er
D001499:Bayes Theorem; D020690:GTPase-Activating Proteins / Q000235:genetics / Q000378:metabolism*; D020869:Gene Expression Profiling; D006801:Humans; D008545:Melanoma / Q000235:genetics*; D051739:Microphthalmia-Associated Transcription Factor / Q000235:genetics / Q000378:metabolism; D021381:Protein Transport; D020691:rab GTP-Binding Proteins / Q000235:genetics / Q000378:metabolism*; D000075745:rab27 GTP-Binding Proteins
D020690:GTPase-Activating Proteins; C497234:MITF protein, human; D051739:Microphthalmia-Associated Transcription Factor; C555060:TBC1D16 protein, human; D000075745:rab27 GTP-Binding Proteins; C066694:RAB27A protein, human; D020691:rab GTP-Binding Proteins