Fang Dalang, Lin Jie, Wang Jin, Nong Qingxiao, Tao Shouwen, Lu Bimin, Yu Yanrong, Peng Hao, Tian Yingying, Su Qunying, Ma Yanfei, Huang Yuanlu
Department of Gland Surgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Key Laboratory of Tumor Molecular Pathology of Baise, Baise, Guangxi, China.
Department of Pathology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
Front Immunol. 2025 Aug 27;16:1662004. doi: 10.3389/fimmu.2025.1662004. eCollection 2025.
Hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer is the most common subtype, characterized by heterogeneous neoadjuvant chemotherapy (NAC) responses and low pCR rates. 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.
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.
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 top-ranked SHAP feature, with high expression predicting RD and was associated with poor survival (p=0.014). GSEA revealed CEACAM6-high tumors were enriched in drug resistance pathways (such as 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 IC50 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.
CEACAM6 is a promising predictive biomarker in HR+/HER2- breast cancer, associated with chemoresistance and immune suppression. Machine learning models integrating immune signatures and pathway features may optimize personalized NAC strategies.
激素受体阳性/人表皮生长因子受体2阴性（HR+/HER2-）乳腺癌是最常见的亚型，其特征为新辅助化疗（NAC）反应异质性和低病理完全缓解（pCR）率。现有生物标志物的预测准确性有限，阻碍了个性化治疗。本研究旨在识别NAC反应的预测生物标志物，并探索其在HR+/HER2-乳腺癌中的治疗潜力。
我们整合了来自TCGA的497个HR+/HER2-样本和来自9个GEO数据集的956个样本（训练集：n = 708；测试集：n = 248）。确定了肿瘤与正常组织（TCGA）之间以及pCR与残留疾病（RD）组（GEO）之间的差异表达基因（DEG）。使用LASSO、随机森林和支持向量机递归特征消除（SVM-RFE）算法进一步筛选重叠的DEG。用10种机器学习算法构建预测模型，并使用SHAP进行解释。进行了基因集富集分析（GSEA）、基于CIBERSORT的免疫浸润分析以及使用oncoPredict和GDSC2进行药物敏感性预测。对配对的NAC前/后样本（n = 9）进行免疫组织化学（IHC）检测。在106例HR+/HER2- NAC患者的回顾性队列中分析临床相关性。
鉴定出38个重叠的DEG，并选择了四个关键基因（癌胚抗原相关细胞黏附分子6（CEACAM6）、丝氨酸/苏氨酸蛋白激酶MELK、视黄酸受体应答蛋白1（RARRES1）、凋亡抑制蛋白5（BIRC5））。神经网络显示出最佳的模型性能（曲线下面积（AUC）= 0.816）。CEACAM6是排名最高的SHAP特征，高表达预测RD，且与不良生存相关（p = 0.014）。GSEA显示CEACAM6高表达的肿瘤在耐药途径（如氧化磷酸化）中富集，而低表达与免疫激活相关。免疫分析显示pCR肿瘤有更多效应细胞（滤泡辅助性T细胞（Tfh）、γδ T细胞、M1巨噬细胞），而RD肿瘤中调节性T细胞（Tregs）和静止肥大细胞富集。CEACAM6与Tregs和初始CD4+ T细胞呈正相关，与CD8+ T细胞和M1巨噬细胞呈负相关。CEACAM6高表达的肿瘤对六种NAC相关药物的半数抑制浓度（IC50）更高。IHC证实RD肿瘤在NAC后持续表达CEACAM6。临床上，pCR患者淋巴细胞计数更高，N2 - N3淋巴结状态更常见。
CEACAM6是HR+/HER2-乳腺癌中有前景的预测生物标志物，与化疗耐药和免疫抑制相关。整合免疫特征和通路特征的机器学习模型可能优化个性化NAC策略。
