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mSignatureDB: a database for deciphering mutational signatures in human cancers
Po-Jung Huang1,2, Ling-Ya Chiu3, Chi-Ching Lee2,4
1Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.
Abstract:
Cancer is a genetic disease caused by somatic mutations; however, the understanding of the causative biological processes generating these mutations is limited. A cancer genome bears the cumulative effects of mutational processes during tumor development. Deciphering mutational signatures in cancer is a new topic in cancer research. The Wellcome Trust Sanger Institute (WTSI) has categorized 30 reference signatures in the COSMIC database based on the analyses of ∼10 000 sequencing datasets from TCGA and ICGC. Large cohorts and bioinformatics skills are required to perform the same analysis as WTSI. The quantification of known signatures in custom cohorts is not possible under the current framework of the COSMIC database, which motivates us to construct a database for mutational signatures in cancers and make such analyses more accessible to general researchers. mSignatureDB (http://tardis.cgu.edu.tw/msignaturedb) integrates R packages and in-house scripts to determine the contributions of the published signatures in 15 780 individual tumors from 73 TCGA/ICGC cancer projects, making comparison of signature patterns within and between projects become possible. mSignatureDB also allows users to perform signature analysis on their own datasets, quantifying contributions of signatures at sample resolution, which is a unique feature of mSignatureDB not available in other related databases.
Insights
Understanding cancer
Area of Science:
- Genomics
- Cancer Research
- Bioinformatics
Background:
- Cancer arises from somatic mutations, but the biological processes causing them are not fully understood.
- Cancer genomes accumulate mutations during tumor development, making mutational signature analysis crucial.
- Existing databases like COSMIC require large cohorts and bioinformatics expertise, limiting accessibility for custom analyses.
Purpose of the Study:
- To develop a user-friendly database for analyzing mutational signatures in cancer.
- To enable quantification of known mutational signatures in individual tumors and custom cohorts.
- To facilitate accessible comparison of mutational signature patterns across diverse cancer projects.
Main Methods:
- Integrated R packages and in-house scripts for mutational signature analysis.
- Analyzed 15,780 tumors from 73 The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects.
- Developed mSignatureDB (http://tardis.cgu.edu.tw/msignaturedb) for signature quantification at sample resolution.
Main Results:
- mSignatureDB successfully quantified published mutational signatures across 73 cancer projects.
- Enabled within- and between-project comparisons of mutational signature patterns.
- Provided a unique capability for users to perform signature analysis on their own datasets.
Conclusions:
- mSignatureDB enhances accessibility for researchers studying cancer mutational signatures.
- The database facilitates detailed analysis of mutational processes across large cancer cohorts.
- Offers a novel tool for quantifying mutational signatures at the individual tumor level.
