Neoantimon

Neoantimon predicts tumor-specific neoantigens by identifying mutant peptides from somatic point mutations, insertions, deletions, and structural variants and evaluating their MHC binding and expression for cancer immunotherapy applications.

Key Features:

• R package implementation: Implemented as an R package for computational neoantigen analysis.
• Mutation support: Predicts neoantigens from somatic point mutations (including missense), insertions, deletions (including frameshift), and structural variants.
• MHC binding comparison: Assesses whether mutant peptides bind more effectively to major histocompatibility complex (MHC) molecules than their wild-type counterparts.
• Allele-specific expression integration: Incorporates allele-specific expression data to refine neoantigen identification based on transcript expression.
• SNP incorporation: Utilizes single nucleotide polymorphism (SNP) data to refine MHC-binding predictions.
• Combinatorial mutation analysis: Evaluates multiple mutations in tandem to detect neoantigens arising from complex genetic alterations.
• Candidate generation: Produces comprehensive lists of candidate neoantigens for downstream prioritization.

Scientific Applications:

• Immunotherapy research: Identification and prioritization of neoantigens presented by MHC for recognition by antitumor T cells.
• Personalized medicine: Generation of candidate neoantigens to support personalized cancer treatment strategies.
• Target identification: Detection of tumor-specific mutant peptides that may serve as targets for T cell–mediated therapies.

Methodology:

Computational methods include neoantigen prediction from somatic point mutations, insertions, deletions, and structural variants; MHC binding comparison between mutant and wild-type peptides; integration of allele-specific expression and SNP data; and combinatorial evaluation of multiple mutations.