CNSigs: An R Package for the Identification of Copy Number Mutational Signatures
- Open access
CNSigs identifies 13 pan-cancer copy number mutational signatures with high reproducibility and clinical relevance across diverse datasets.
- Why it matters: Understanding CNA mutational signatures is crucial for revealing underlying cancer processes and improving prognostic and therapeutic strategies, yet methods are limited.
- What they did: The authors developed CNSigs, an R package that extracts six copy number features from DNA sequencing data and applies mixed models and non-negative matrix factorization to identify signatures, validated on over 3,000 breast cancer samples.
- The result: CNSigs produced signatures with high accuracy (cosine similarity 0.89), associated with survival and treatment response, and proved effective in circulating tumor DNA, enabling better understanding of CNA-driven cancer mechanisms.