期刊
COMMUNICATIONS BIOLOGY
卷 4, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s42003-021-01938-0
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资金
- Multiple Myeloma Research Foundation (MMRF)
- Perelman Family Foundation
- Riney Family Multiple Myeloma Research Program Fund
- Memorial Sloan Kettering Cancer Center NCI Core Grant [P30 CA 008748]
- Sylvester Comprehensive Cancer Center NCI Core Grant [P30 CA 240139]
- American Society of Hematology
- International Myeloma Foundation
- Society of Memorial Sloan Kettering Cancer Center
- European Research Council under the European Union's Horizon 2020 research and innovation program [817997]
- European Regional Development Fund
- Welsh Government (Ser Cymru programme)
Mutational signatures are powerful biomarkers in cancer patients with prognostic and therapeutic implications. The mmsig software package allows for accurate detection of mutational signatures in hematological cancer genomes, highlighting the importance of careful interpretation in light of biological knowledge.
Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational signatures in a given tumor sample. Here, we show how mutational signature fitting can be applied to hematological cancer genomes to identify biologically and clinically important mutational processes, highlighting the importance of careful interpretation in light of biological knowledge. Our newly released R package mmsig comes with a dynamic error-suppression procedure that improves specificity in important clinical and biological settings. In particular, mmsig allows accurate detection of mutational signatures with low abundance, such as those introduced by APOBEC cytidine deaminases. This is particularly important in the most recent mutational signature reference (COSMIC v3.1) where each signature is more clearly defined. Our mutational signature fitting algorithm mmsig is a robust tool that can be implemented immediately in the clinic. Rustad et al. present a software package for the R statistical environment for the accurately quantify of somatic mutational signatures in hematological malignancies
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