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

Henrik Hornshøj(Aarhus University Hospital), Morten Muhlig Nielsen(Aarhus University Hospital), Nicholas A. Sinnott‐Armstrong(Massachusetts Institute of Technology), Michał Świtnicki(Aarhus University Hospital), Malene Juul(Aarhus University Hospital), Tobias Madsen(Aarhus University), Richard Sallari(Massachusetts Institute of Technology), Manolis Kellis(Massachusetts Institute of Technology), Torben Ørntoft(Aarhus University Hospital), Asger Hobolth(Aarhus University), Jakob Skou Pedersen(Aarhus University)
npj Genomic Medicine
January 5, 2018
Cited by 86Open Access
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Abstract

Abstract Cancer develops by accumulation of somatic driver mutations, which impact cellular function. 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 10 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 4128 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. In conclusion, the screen for significant mutation patterns coupled with 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.


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