Systematic characterization of cancer genomes has revealed a staggering number of diverse alterations that differ among individuals, so that their functional importance and physiological impact remains poorly defined. In order to identify which genetic alterations are functional, the lab of Dr. Dana Pe’er has developed a novel Bayesian probabilistic algorithm, CONEXIC, to integrate copy number and gene expression data in order to identify tumor-specific “driver” aberrations, as well as the cellular processes they affect.
In work published in the journal Cell, the new method was applied on data from melanoma patients, identifying a list of 64 putative ‘drivers’ and the core processes affected by them. This list includes many known driver genes (e.g., MITF), which CONEXIC correctly identified and paired with their known targets. This list also includes novel ‘driver’ candidates including Rab27a and TBC1D16, both involved in protein trafficking. ShRNA-mediated silencing of these genes in short-term tumor-derived cultures determined that they are tumor dependencies and validated their computationally predicted role in melanoma (including target identification), suggesting that protein trafficking may play an important role in this malignancy.