Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions

John K. L. Wong (), Christian Aichmüller, Markus Schulze, Mario Hlevnjak, Shaymaa Elgaafary, Peter Lichter and Marc Zapatka ()
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John K. L. Wong: German Cancer Research Center (DKFZ)
Christian Aichmüller: German Cancer Research Center (DKFZ)
Markus Schulze: National Center for Tumor Diseases (NCT) and DKFZ
Mario Hlevnjak: National Center for Tumor Diseases (NCT) and DKFZ
Shaymaa Elgaafary: National Center for Tumor Diseases (NCT) and University of Heidelberg
Peter Lichter: German Cancer Research Center (DKFZ)
Marc Zapatka: German Cancer Research Center (DKFZ)

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.

Date: 2022
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DOI: 10.1038/s41467-021-27792-6

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