Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

By S. Islam, M. Díaz-Gay, Yang Wu, M. Barnes, R. Vangara, E. Bergstrom, Yudou He, M. Vella, Jingwei Wang, J. Teague and 25 more. bioRxiv, 2020

322 citations recorded by the source corpus.

Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 34 distinct scenarios encompassing 2,500 simulated signatures operative in more than 60,000 unique synthetic genomes and 20,000 synthetic exomes demonstrates that SigProfilerExtractor outperforms thirteen other tools across all datasets with and without noise. For genome simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome sequenced and 19,184 whole-exome sequenced cancers reveals four previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6deb833d6a92cf2393b5a734a4f1fe3164cd0302

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