We introduce two generative models of somatic mutations in cancer and study the algorithmic complexity of discovering driver pathways in both models.
Given that driver aberrations recurrently occur around driver cancer genes whereas passenger aberrations randomly exist across chromosomes, finding recurrent chromosomal aberrations is deemed a powerful approach for discovering driver aberrations and associated driver genes.
We investigate the problem of discovering driver pathways, groups of genes containing driver mutations, directly from cancer mutation data and without prior knowledge of pathways or other interactions between genes.
These data have been hugely informative in discovering driver mutations that are causally responsible for the development and progression of cancer (Garraway and Lander, 2013, Vogelstein et al., 2013, Wheeler and Wang, 2013).
Indeed, discovering driver mutations is one of the main goals of genome re-sequencing efforts, as the knowledge generated by exome-sequencing will translate from research to the clinic.
"On three separate inspections of Chariot Transportation, Inc. it was discovered drivers without valid Class B California Driver Licenses were driving passenger buses which required a Class B license," CHP Public Information Officer John D. Fransen told TechCrunch.
The goal of DawnRank is to integrate mutation data, gene expression, and network information to discover drivers in a personalized manner.
Here, we prove that for both generative models, when mutation data from enough patients is available, the weight function is monotone in the number of discovered driver genes and is maximized by the driver pathway.
Discovering rare drivers in the long tail of genetic alterations remains difficult.
After discovering the driver's identity, the police proceeded to change Mr. Busch's tire, according to Terry Ganey, co-author of "Under the Influence: the Unauthorized Story of the Anheuser-Busch Dynasty".
