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CancerImmunogenomicsEngine: Neoantigen Prediction, HLA Typing, MHC Binding Affinity, and Immunoediting Detection
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Cancer immunogenomics integrates somatic mutation data with HLA typing to predict neoantigens and understand immune editing of tumors. We present CancerImmunogenomicsEngine, a pure-Python pipeline for cancer immunogenomics analysis. The engine implements neoantigen prediction (mutation → peptide → MHC-I binding affinity, NetMHCpan-style IC50), HLA typing (4-digit resolution, 6 loci), immunoediting detection (neoantigen depletion score), immune checkpoint expression (PD-L1/CTLA4/TIM3), and tumor immune phenotype classification. Applied to 150 tumors, the pipeline identifies median neoantigen burden=9, strong MHC binders=4.1%, immunoedited=23.3%, HLA LOH=25.3%, and neoantigen-TMB r=0.895.
Introduction
Neoantigens arise from somatic mutations creating novel peptides presented by MHC-I to cytotoxic T cells. High neoantigen burden correlates with immunotherapy response. Immunoediting describes immune pressure selecting against immunogenic neoantigens.
Methods
Neoantigen Prediction
Mutant peptides (8-11 mers) from missense mutations. MHC-I binding by PSSM for HLA alleles. Strong binders: IC50 < 50 nM.
Immunoediting
Score = (expected - observed neoantigens) / expected. Positive = depletion.
Immune Phenotype
Inflamed (high TIL + checkpoint), excluded (stromal barrier), desert (low immune).
Results
Median neoantigens=9. Strong binders=4.1%. Immunoedited=23.3%. HLA LOH=25.3%. Neo-TMB r=0.895.
Code Availability
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