State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China.
Front Immunol. 2024 Jun 28;15:1424259. doi: 10.3389/fimmu.2024.1424259. eCollection 2024.
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.
共刺激分子是新型潜在靶点或当前免疫疗法的潜在补充,但它们在三阴性乳腺癌(TNBC)中的表达模式和临床价值尚待阐明。
从癌症基因组图谱和基因表达综合数据库中获取 TNBC 患者的基因表达谱数据集。使用最小绝对收缩和选择算子(LASSO)和支持向量机-递归特征消除(SVM-RFE)算法确定用于分层个体化肿瘤免疫微环境(TIME)的诊断生物标志物。此外,我们通过多重免疫组化(mIHC)探索了它们与免疫治疗反应的相关性。
共获得 60 个共刺激分子基因(CMGs),并通过 K-均值聚类方法确定了两种不同的 TIME 亚类(“热”和“冷”)。“热”肿瘤表现出更高浸润的活化免疫细胞,即 CD4 记忆激活 T 细胞、静止 NK 细胞、M1 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 算法鉴定出三个 CMGs(CD86、TNFRSF17 和 TNFRSF1B)作为诊断生物标志物。随后,构建了一种新的诊断列线图,用于预测个体化 TIME 状态,并在 TCGA、GSE76250 和 GSE58812 数据库中验证了其具有良好的预测准确性。进一步的 mIHC 证实,CD86、TNFRSF17 和 TNFRSF1B 水平较高的 TNBC 患者倾向于对免疫治疗有反应。
本研究补充了 CMGs 在 TNBC 中的价值证据。此外,CD86、TNFRSF17 和 TNFRSF1B 被发现为潜在的生物标志物,显著促进了 TNBC 患者选择免疫治疗指导。