结合机器学习算法和多重免疫组织化学验证揭示基于协同刺激分子的新型诊断标志物，用于预测三阴性乳腺癌中的免疫微环境状态

Integrating machine learning algorithms and multiple immunohistochemistry validation to unveil novel diagnostic markers based on costimulatory molecules for predicting immune microenvironment status in triple-negative breast cancer

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Frontiers in Immunology

影响因子:5.9

分区:医学2区 / 免疫学2区

发表日期:2024

作者: Chao Zhang, Wenyu Zhai, Yuyu Ma, Minqing Wu, Qiaoting Cai, Jiajia Huang, Zhihuan Zhou, Fangfang Duan

DOI: 10.3389/fimmu.2024.1424259

摘要

协同刺激分子被认为是潜在的新型靶点或对现有免疫治疗的潜在补充，但其在三阴性乳腺癌（TNBC）中的表达模式及临床价值尚需阐明。TNBC患者的基因表达谱数据集来自癌症基因组图谱（TCGA）和基因表达谱数据集（GEO）数据库。利用最小绝对收缩与选择算子（LASSO）和支持向量机-递归特征消除（SVM-RFE）算法识别用于区分个体化肿瘤免疫微环境（TIME）的诊断生物标志物。此外，我们还通过多重免疫组织化学（mIHC）探讨了它们与免疫治疗反应的关系。共获得60个协同刺激分子基因（CMGs），并通过K-means聚类方法确定了两种不同的TIME亚类（“热”与“冷”）。“热”肿瘤表现出较高的活化免疫细胞浸润，即CD4记忆激活T细胞、静息NK细胞、M1型巨噬细胞和CD8 T细胞，从而富集在B细胞和T细胞受体信号通路中。LASSO和SVM-RFE算法鉴定出三种CMGs（CD86、TNFRSF17和TNFRSF1B）作为诊断生物标志物。随后，构建了一种新颖的诊断列线图，用于预测个体化的TIME状态，并在TCGA、GSE76250和GSE58812数据库中验证了其良好的预测准确性。进一步的mIHC验证显示，CD86、TNFRSF17和TNFRSF1B高表达的TNBC患者更倾向于对免疫治疗产生反应。该研究补充了关于CMGs在TNBC中的价值的证据。此外，发现CD86、TNFRSF17和TNFRSF1B具有潜在生物标志物作用，显著促进TNBC患者的免疫治疗筛选和指导。

Abstract

Costimulatory molecules are putative novel targets or potential additions to current available immunotherapy, but their expression patterns and clinical value in triple-negative breast cancer (TNBC) are to be clarified.The gene expression profiles datasets of TNBC patients were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Diagnostic biomarkers for stratifying individualized tumor immune microenvironment (TIME) were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Additionally, we explored their associations with response to immunotherapy via the multiplex immunohistochemistry (mIHC).A total of 60 costimulatory molecule genes (CMGs) were obtained, and we determined two different TIME subclasses ("hot" and "cold") through the K-means clustering method. The "hot" tumors presented a higher infiltration of activated immune cells, i.e., CD4 memory-activated T cells, resting NK cells, M1 macrophages, and CD8 T cells, thereby enriched in the B cell and T cell receptor signaling pathways. LASSO and SVM-RFE algorithms identified three CMGs (CD86, TNFRSF17 and TNFRSF1B) as diagnostic biomarkers. Following, a novel diagnostic nomogram was constructed for predicting individualized TIME status and was validated with good predictive accuracy in TCGA, GSE76250 and GSE58812 databases. Further mIHC conformed that TNBC patients with high CD86, TNFRSF17 and TNFRSF1B levels tended to respond to immunotherapy.This study supplemented evidence about the value of CMGs in TNBC. In addition, CD86, TNFRSF17 and TNFRSF1B were found as potential biomarkers, significantly promoting TNBC patient selection for immunotherapeutic guidance.