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ORIGINAL RESEARCH article

Front. Immunol.

Sec. Systems Immunology

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1662004

This article is part of the Research TopicExploring the Applications of Artificial Intelligence in Disease Screening, Diagnosis, Treatment, and NursingView all 11 articles

CEACAM6 as a Machine Learning Derived Immune Biomarker for Predicting Neoadjuvant Chemotherapy Response in HR+/HER2-Breast Cancer

Provisionally accepted
Dalang  FangDalang Fang1*Jie  LinJie Lin1Wang  JinWang Jin1Qingxiao  NongQingxiao Nong1Shouwen  TaoShouwen Tao1Bimin  LuBimin Lu1Yanrong  YuYanrong Yu1Hao  PengHao Peng1Yingying  TianYingying Tian1Qunying  SuQunying Su1Yanfei  MaYanfei Ma1Yuanlu  HuangYuanlu Huang2
  • 1Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
  • 2Baise People's Hospital, Baise, China

The final, formatted version of the article will be published soon.

Background: Hormone receptor-positive/human epidermal growth factor receptor 2negative (HR+/HER2-) breast cancer is the most common subtype, characterized by heterogeneous responses to neoadjuvant chemotherapy (NAC) and a low pathological complete response (pCR) rate. Existing biomarkers have limited predictive accuracy, hindering personalized treatment. This study aimed to identify predictive biomarkers for NAC response and explore their therapeutic potential in HR+/HER2-breast cancer. Methods: We integrated 497 HR+/HER2-samples from TCGA and 956 from nine GEO datasets (training set: n=708; test set: n=248). Differentially expressed genes (DEGs) between tumors and normal tissues (TCGA) and between pCR and residual disease (RD) groups (GEO) were identified. Overlapping DEGs were further screened using LASSO, random forest, and SVM-RFE algorithms. Predictive models were constructed with 10 machine learning algorithms and interpreted using SHAP. Gene set enrichment analysis (GSEA), CIBERSORT-based immune infiltration, and drug sensitivity prediction using oncoPredict and GDSC2 were performed. Immunohistochemistry (IHC) was conducted on paired pre/post-NAC samples (n=9). Clinical correlation was analyzed in a retrospective cohort of 106 HR+/HER2-NAC patients. Results: Thirty-eight overlapping DEGs were identified, and four key genes (CEACAM6, MELK, RARRES1, BIRC5) were selected. NeuralNet showed the best model performance (AUC=0.816). CEACAM6 was the most important SHAP feature; its high expression predicted RD and was associated with poor survival (p=0.014). GSEA revealed CEACAM6-high tumors were enriched in drug resistance pathways (e.g., oxidative phosphorylation), while low expression correlated with immune activation. Immune analysis showed pCR tumors had more effector cells (Tfh, γδ T cells, M1 macrophages), whereas RD tumors were enriched in Tregs and resting mast cells. CEACAM6 positively correlated with Tregs and naïve CD4+ T cells, and negatively with CD8+ T cells and M1 macrophages. CEACAM6-high tumors had higher IC50s for six NAC-related drugs. IHC confirmed persistent CEACAM6 expression in RD tumors post-NAC. Clinically, pCR patients had higher lymphocyte counts and more frequent N2-N3 nodal status. Conclusion: CEACAM6 is a promising predictive biomarker in HR+/HER2-breast cancer, associated with chemoresistance and immune suppression. Machine learning models incorporating immune and pathway features may enhance personalized NAC strategies.

Keywords: machine learning, Immune infiltration, HR+/HER2-breast cancer, CEACAM6, neoadjuvant chemotherapy (NAC)

Received: 08 Jul 2025; Accepted: 08 Aug 2025.

Copyright: © 2025 Fang, Lin, Jin, Nong, Tao, Lu, Yu, Peng, Tian, Su, Ma and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dalang Fang, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China

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