A Framework for Comparing Mouse Neoantigen Immunogenicity
Abstract
SUMMARY Cytotoxic CD8+ T cell responses targeting tumor neoantigens are critical for immunotherapy efficacy and are widely studied across different preclinical mouse tumor models. Defined neoantigens are commonly identified or introduced into mouse tumors to enable tracking of tumor-specific T cell responses. However, these neoantigens differ in immunogenicity and variation in their composition may produce immune phenotypes that are mistakenly attributed to tumor-intrinsic mechanisms and complicate comparison across studies. Here, we sought to create a framework for comparing neoantigen immunogenicity by determining relative peptide-major histocompatibility complex (pMHC) affinity (K D ) and stability (K off ) and in vivo neoantigen-targeted vaccine responses for 25 commonly used mouse tumor-derived and model neoantigens. We found that in silico prediction of pMHC affinity moderately stratified strong versus weak in vivo immunogenicity but could not accurately predict rank order; experimental measurements of affinity did not improve prediction. In contrast, experimental measurement of pMHC stability closely correlated with the relative magnitude of vaccine responses demonstrating strong predictive value. Thus, we report the relative stability of commonly used neoantigens as a resource and provide a simple method to benchmark novel neoantigens against this library. This tool allows contextualization of the level of immunogenicity of newly identified neoantigens and aids in comparative interpretation of tumor-immune phenotypes across studies. SIGNIFICANCE Peptide-MHC stability strongly correlates with the in vivo response a neoantigen elicits, and this framework enables comparison of neoantigens across studies to evaluate the neoantigen contribution to the anti-tumor T cell response.
Fields of Study & Subjects
Immunotherapy and Immune Responses
Cancer Immunotherapy and Biomarkers
vaccines and immunoinformatics approaches
Publication details
- Journal / source
- bioRxiv (Cold Spring Harbor Laboratory)
- Publication date
- 12 February 2026
- Language
- EN
- Document type
- Preprint