Abstract
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.
Original language | English |
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Article number | 173 |
Journal | Genome Medicine |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2021 |
Keywords
- Gene panel sequencing
- Mutational signatures
- Probabilistic modeling
All Science Journal Classification (ASJC) codes
- Molecular Medicine
- Molecular Biology
- Genetics
- Genetics(clinical)