A mixture model for signature discovery from sparse mutation data

Itay Sason, Yuexi Chen, Mark D.M. Leiserson, Roded Sharan
2021 Genome Medicine  
AbstractMutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, . In application to simulated and real gene-panel sequences, is shown to outperform current approaches and yield mutational signatures
more » ... d patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM.
doi:10.1186/s13073-021-00988-7 pmid:34724984 pmcid:PMC8559697 fatcat:b2xtgngi6ffdbpmdq4leyelp5e