Colorectal cancer (CRC) is the third most common malignancy globally, with microsatellite instability (MSI) present in approximately 15% of cases. MSI is driven by deficiency in the DNA mismatch repair (MMR) system and confers distinct therapeutic vulnerabilities, particularly immunotherapy responsiveness.
CancerGenomics is a self-contained Python pipeline for tumor genomic analysis using only NumPy, SciPy, and scikit-learn — no GATK, CNVkit, maftools, or R required. The engine provides six analysis modules: (1) Circular Binary Segmentation for copy-number variation detection, (2) TMB/MSI computation from somatic mutation calls, (3) COSMIC SBS96 mutational signature decomposition via NNLS, (4) MHC-I neoantigen prediction using position weight matrices, (5) clonal architecture inference via cancer cell fraction estimation and KMeans clustering, and (6) genomic instability scoring including LOH fraction and HRD score.
Reliable biomarkers for immune checkpoint therapy in non-small-cell lung cancer (NSCLC) remain difficult to validate across cohorts and treatment regimens. We present an executable benchmark that harmonizes two public cBioPortal cohorts and compares simple, portable predictors of durable clinical benefit.