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Humboldt-Universität zu Berlin | IRI Life Sciences | Scientific Events | IRI Talks | Dates | CompCancer Talk - Donate Weghorn: "Probabilistic approaches to inference of mutation rate and selection in cancer"

CompCancer Talk - Donate Weghorn: "Probabilistic approaches to inference of mutation rate and selection in cancer"

What
  • IRI Talk
When Dec 19, 2019 from 04:00 PM to 05:00 PM (Europe/Vienna / UTC100) iCal
Where IRI Life Sciences, Philippstr. 13, Building 18, 3rd Floor, Room 410
Contact Name

Abstract:

 

Probabilistic approaches to inference of mutation rate and selection in cancer

Cancer is a highly complex system that evolves asexually under high mutation rates and strong selective pressures. Cancer genomics efforts have identified genes and regulatory elements driving cancer development and neoplastic progression. The detection of both significantly mutated (positive selection) and undermutated (negative selection) genes is completely confounded by the genomic heterogeneity of the cancer mutation rate. Here, I present an approach to address mutation rate heterogeneity in order to increase the power and accuracy of selection inference. Using a hierarchical model, we infer the distribution of mutation rates across genes that underlies the observed distribution of the synonymous mutation count within a given cancer type. This enables the inference of the probability of nonsynonymous mutations without additional parameters, however explicitly taking into account cancer-type-specific mutational signatures, which are known to be highly distinct. We then augmented our test through integrating information at the single-nucleotide level. Based on a model that accounts for the extended sequence context (> 5-mers) around mutated sites, this second component of the test identifies genes with an excess of mutations in specific nucleotide contexts, which deviate from the characteristic context around neutrally evolving passenger mutations. Using the combined test, we discovered a catalogue of well-known cancer driver genes as well as a long tail of novel candidate cancer genes with mutation frequencies as low as 1% and functional supporting evidence.