mSignatureDB: a database for deciphering mutational signatures in human cancers.

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.