NeoGen-BC: A synergistic framework combining generative protein language models and multi-window deep learning for designing shared neoantigens in breast cancer.

Abstract

Breast cancer, particularly hormone receptor-positive (HR+) and triple-negative (TNBC) subtypes, is often immunologically "cold," limiting immunotherapy efficacy. Neoantigen-based vaccines hold promise but face challenges from inaccurate identification and overreliance on HLA binding affinity, which poorly captures true immunogenicity. Most approaches focus on patient-specific (private) neoantigens, hindering scalability. In contrast, recurrent driver mutations in ESR1 and PIK3CA produce shared (public) neoantigens suitable for off-the-shelf vaccines, yet systematic discovery and design frameworks for breast cancer are lacking. We developed NeoGen-BC, a synergistic framework advancing from neoantigen prediction to rational design. It integrates protein language model (PLM) embeddings with Retrieval-Augmented Generation (RAG) and a multi-scale MCNN-BiLSTM classifier to capture immunogenic features beyond peptide-HLA binding. A sliding-window strategy maps epitopes from driver mutations, while a controlled ProtGPT2 generative module explores immunogenic peptide space. Candidates are refined via biophysical filtering, structural validation, and immunogenicity screening. NeoGen-BC achieved an AUC of 0.9053 on an independent test set, outperforming other machine learning models. It identified immunogenic peptides difficult to assess by MHC-II binding tools like NetMHCIIpan 4.3 and accurately detected validated MHC class II-restricted shared neoantigens from ESR1 (Y537S, D538G) and PIK3CA (H1047R) mutations, often missed by conventional predictors. De novo generated peptides matched experimentally validated ESR1 neoantigens in physicochemical and structural properties, showing favorable MHC-II binding and immunogenicity. NeoGen-BC provides a computational foundation for next-generation "tunable antigen" vaccine design by prioritizing immunogenicity alongside peptide-MHC binding characteristics. The framework enables scalable identification of candidate peptide vaccines targeting shared breast cancer vulnerabilities and may complement emerging therapeutic strategies, including oral SERD-based approaches aimed at enhancing immune modulation and advancing precision immunotherapy.