Rasmussen, M. J. | Search bioRxiv

bioRxiv2017

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Non-coding mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from ten cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g. TERT and CDH10) and survival (e.g. CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4,128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217 and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. This screen for significant mutation patterns followed by correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

cancer biology↗

bioRxiv2017

A Site Specific Model And Analysis Of The Neutral Somatic Mutation Rate In Whole-Genome Cancer Data

BackgroundDetailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration.\n\nResultsTo take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures.\n\nConclusionWe find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

bioinformatics↗