---
title: "NeoGen-BC: PLM-driven pipeline for shared breast cancer neoantigen design"
id: "pubmed-42721858"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42721858"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42721858/"
doi: "10.1016/j.bbrc.2026.154567"
published_at: "2026-10-22T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# NeoGen-BC: PLM-driven pipeline for shared breast cancer neoantigen design
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42721858
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42721858/)
- **DOI:** [10.1016/j.bbrc.2026.154567](https://doi.org/10.1016%2Fj.bbrc.2026.154567)
- **Published At:** 2026-10-22T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- NeoGen-BC is a computational framework developed to identify and design **shared neoantigens** in breast cancer, targeting recurrent driver mutations rather than private neoantigens. - The pipeline integrates protein language model (PLM) embeddings with Retrieval-Augmented Generation (RAG) and a multi-scale MCNN-BiLSTM classifier to capture immunogenic features beyond simple peptide-HLA binding. - A sliding-window epitope-mapping strategy systematically interrogates mutation-derived peptide space around driver variants. - The authors incorporated a controlled ProtGPT2 generative module to produce de novo peptide sequences exploring immunogenic chemical space. - Candidates undergo biophysical filtering, structural validation, and immunogenicity screening as downstream refinement steps. - On an independent test set, NeoGen-BC achieved an AUC of 0.9053, outperforming conventional machine learning comparators reported in the abstract. - The framework detected validated **MHC class II**-restricted shared neoantigens from **ESR1** (Y537S, D538G) and **PIK3CA** (H1047R) mutations that were often missed by MHC-II binding tools such as **NetMHCIIpan 4.3**. - De novo generated peptides recapitulated physicochemical and structural properties of experimentally validated **ESR1** neoantigens and showed favorable predicted **MHC-II** binding and immunogenicity. - The source reports that NeoGen-BC supplies a computational foundation for scalable, tunable antigen vaccine design and may complement therapeutic strategies including oral SERD-based approaches. - The abstract did not report in vivo validation results, clinical testing, detailed dataset sizes, or training hyperparameters; those methodological specifics were not provided in the source text.
## Clinical Analysis & Structured Key Points
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Epub 2026 Sep 9. # NeoGen-BC: A synergistic framework combining generative protein language models and multi-window deep learning for designing shared neoantigens in breast cancer [Van The Le](https://pubmed.ncbi.nlm.nih.gov/?term=Le+VT&cauthor_id=42721858)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42721858/#full-view-affiliation-1 "Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan."), [Juan Peter Timothy Yuune](https://pubmed.ncbi.nlm.nih.gov/?term=Yuune+JPT&cauthor_id=42721858)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42721858/#full-view-affiliation-1 "Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan."), [Jiun-I Lai](https://pubmed.ncbi.nlm.nih.gov/?term=Lai+JI&cauthor_id=42721858)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42721858/#full-view-affiliation-2 "Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: jilai@nycu.edu.tw."), [Yu-Yen Ou](https://pubmed.ncbi.nlm.nih.gov/?term=Ou+YY&cauthor_id=42721858)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42721858/#full-view-affiliation-3 "Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; Graduate Program in Biomedical Informatics, Yuan Ze University, Chung-Li, 32003, Taiwan. Electronic address: yien@saturn.yzu.edu.tw.") Affiliations Expand ### Affiliations * 1 Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan. * 2 Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: jilai@nycu.edu.tw. * 3 Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; Graduate Program in Biomedical Informatics, Yuan Ze University, Chung-Li, 32003, Taiwan. Electronic address: yien@saturn.yzu.edu.tw. * PMID: **42721858** * DOI: [ 10.1016/j.bbrc.2026.154567 ](https://doi.org/10.1016/j.bbrc.2026.154567) Item in Clipboard # NeoGen-BC: A synergistic framework combining generative protein language models and multi-window deep learning for designing shared neoantigens in breast cancer Van The Le et al. Biochem Biophys Res Commun. 2026. Show details Display options Display options Format Abstract PubMed PMID Biochem Biophys Res Commun Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Biochem+Biophys+Res+Commun%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Biochem+Biophys+Res+Commun%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42721858/) . 2026 Oct 22:836:154567. doi: 10.1016/j.bbrc.2026.154567. Epub 2026 Sep 9. ### Authors [Van The Le](https://pubmed.ncbi.nlm.nih.gov/?term=Le+VT&cauthor_id=42721858)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42721858/#short-view-affiliation-1 "Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan."), [Juan Peter Timothy Yuune](https://pubmed.ncbi.nlm.nih.gov/?term=Yuune+JPT&cauthor_id=42721858)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42721858/#short-view-affiliation-1 "Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan."), [Jiun-I Lai](https://pubmed.ncbi.nlm.nih.gov/?term=Lai+JI&cauthor_id=42721858)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42721858/#short-view-affiliation-2 "Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: jilai@nycu.edu.tw."), [Yu-Yen Ou](https://pubmed.ncbi.nlm.nih.gov/?term=Ou+YY&cauthor_id=42721858)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42721858/#short-view-affiliation-3 "Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; Graduate Program in Biomedical Informatics, Yuan Ze University, Chung-Li, 32003, Taiwan. Electronic address: yien@saturn.yzu.edu.tw.") ### Affiliations * 1 Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan. * 2 Comprehensive Breast Health Center, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: jilai@nycu.edu.tw. * 3 Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; Graduate Program in Biomedical Informatics, Yuan Ze University, Chung-Li, 32003, Taiwan. Electronic address: yien@saturn.yzu.edu.tw. * PMID: **42721858** * DOI: [ 10.1016/j.bbrc.2026.154567 ](https://doi.org/10.1016/j.bbrc.2026.154567) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID Linked Discoveries ## 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. **Keywords:** Deep learning; Generative AI; Neoantigen vaccine design; Protein language models; breast cancer immunotherapy. Copyright © 2026 Elsevier Inc. All rights reserved. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declaration of competing interest I, Le Van The, hereby declare that I have no financial interests or relationships with any organizations that could potentially influence the subject matter of this work. I also confirm that I do not hold any professional or personal affiliations that may be perceived as affecting the impartiality and objectivity of my research. I have received no funding, grants, or honoraria related to the research presented in this work. Additionally, I have no personal relationships or collaborations that might pose a conflict of interest. This work is conducted with complete transparency, and I am committed to upholding the highest standards of integrity in my scholarly contributions. ## Similar articles * [ HLA class II-restricted T cell epitopes in public neoantigens of ESR1 and PIK3CA in breast cancer. ](https://pubmed.ncbi.nlm.nih.gov/40186192/) Ando Y, Miyadera H, Bando H, Hashimoto S, Noguchi E, Hara H.Ando Y, et al.BMC Cancer. 2025 Apr 4;25(1):610. doi: 10.1186/s12885-025-13992-6.BMC Cancer. 2025.PMID: 40186192Free PMC article. * [ AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition. ](https://pubmed.ncbi.nlm.nih.gov/42410432/) Bakhshian A, Ghorghanlu S, Atanaki FF, Ezadyar EE, Ashkiyan A, Etemadi A, Negahdari B, Kavousi K, Kardar G, Mazloomi M.Bakhshian A, et al.J Transl Med. 2026 Jul 6;24(1):924. doi: 10.1186/s12967-026-08535-x.J Transl Med. 2026.PMID: 42410432Free PMC article.Review. * [ Exploring the role of ESR1 mutations in metastatic hormone receptor-positive breast cancer T cell immune surveillance disruption. ](https://pubmed.ncbi.nlm.nih.gov/39920833/) Lopez M, Spehner L, André F, Viot J, Seffar E, Marguier A, Curtit E, Meynard G, Dobi E, Ladoire S, Boidot R, Loyon R, Derangere V, Bidard FC, Borg C, Mansi L, Kroemer M.Lopez M, et al.Breast Cancer Res. 2025 Feb 7;27(1):19. doi: 10.1186/s13058-025-01962-6.Breast Cancer Res. 2025.PMID: 39920833Free PMC article.Clinical Trial. * [ Artificial Intelligence and the Evolving Landscape of Immunopeptidomics. ](https://pubmed.ncbi.nlm.nih.gov/40741879/) Vo TH, McNeela E, O'Donovan O, Rani S, Mehta JP.Vo TH, et al.Proteomics Clin Appl. 2025 Nov;19(6):e70018. doi: 10.1002/prca.70018. Epub 2025 Jul 31.Proteomics Clin Appl. 2025.PMID: 40741879Free PMC article.Review. * [ Virus-like particle-mediated delivery of structure-selected neoantigens demonstrates immunogenicity and antitumoral activity in mice. ](https://pubmed.ncbi.nlm.nih.gov/38172991/) Barajas A, Amengual-Rigo P, Pons-Grífols A, Ortiz R, Gracia Carmona O, Urrea V, de la Iglesia N, Blanco-Heredia J, Anjos-Souza C, Varela I, Trinité B, Tarrés-Freixas F, Rovirosa C, Lepore R, Vázquez M, de Mattos-Arruda L, Valencia A, Clotet B, Aguilar-Gurrieri C, Guallar V, Carrillo J, Blanco J.Barajas A, et al.J Transl Med. 2024 Jan 3;22(1):14. doi: 10.1186/s12967-023-04843-8.J Transl Med. 2024.PMID: 38172991Free PMC article. 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