Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer.

ABSTRACT:

Background

Mutations in cancer cells can result in the production of neoepitopes that can be recognized by T cells and trigger an immune response. A reliable pipeline to identify such immunogenic neoepitopes for a given tumor would be beneficial for the design of cancer immunotherapies. Current methods, such as the pipeline proposed by the Tumor Neoantigen Selection Alliance (TESLA), aim to select short peptides with the highest likelihood to be MHC-I restricted minimal epitopes. Typically, only a small percentage of these predicted epitopes are recognized by T cells when tested experimentally. This is particularly problematic as the limited amount of sample available from patients that are acutely sick restricts the number of peptides that can be tested in practice. This led our g

SUBMITTER: Chihab LY 

PROVIDER: S-EPMC11914794 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature