pyCancerSig

pyCancerSig deconstructs mutational signatures from whole genome sequencing by integrating single nucleotide variations (SNVs), structural variations (SVs), and microsatellite instability (MSI) to subclassify human cancers and characterize cancer-associated processes.

Key Features:

• Integration of multiple mutation profiles: Combines SNV, SV, and MSI data into a unified mutational profile for each sample.
• File format support: Accepts standard genomic file formats including VCF (Variant Call Format) and BAM (Binary Alignment Map).
• Python implementation: Implemented as a Python package for computational analysis of mutational data.
• Signature decomposition using NMF: Employs non-negative matrix factorization to decompose integrated profiles into distinct mutational signatures.
• Visualization and reporting: Produces visualizations and generates production-ready PDF reports summarizing signature analyses.
• Detection of known and novel processes: Enables identification of both known and previously unrecognized cancer-associated mutational processes from integrated data.

Scientific Applications:

• Cancer subclassification: Enhances subclassification of tumors based on integrated mutational signatures derived from whole genome sequencing.
• Identification of cancer processes: Facilitates discovery and characterization of cancer-associated mutational processes by combining SNV, SV, and MSI signals.
• Validation on TCGA cancers: Demonstrated on breast and colorectal cancer datasets from The Cancer Genome Atlas (TCGA), showing enhancement of signatures when SNV-only profiles are insufficient.
• Research and potential clinical utility: Supports research applications and potential clinical investigations by providing comprehensive mutational signature analyses.

Methodology:

Processes input VCF and BAM files to extract and integrate SNV, SV, and MSI information; applies non-negative matrix factorization (NMF) to decompose integrated mutational profiles into signatures; evaluated using breast and colorectal cancer data from The Cancer Genome Atlas (TCGA).