A mixture model for signature discovery from sparse mutation data

Genome Medicine

Itay Sason¹  Roded Sharan¹  Mark D.M. Leiserson²  Yuexi Chen² 

[1]Blavatnik School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel

[2]Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, 20742, College Park, MD, USA

关键词: Mutational signatures;    Probabilistic modeling;    Gene panel sequencing;   

DOI  :  10.1186/s13073-021-00988-7

来源: Springer

【 摘 要 】

Mutational 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, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and 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.

【 授权许可】

CC BY