基于人工智能的病理组学模型预测IDH野生型胶质母细胞瘤中调节性T细胞浸润及放疗反应
AI-based pathomics model predicts regulatory T cell infiltration and radiotherapy response in IDH-wild-type glioblastoma.
摘要(中文译文)
调节性T细胞（Tregs）在异柠檬酸脱氢酶（IDH）野生型胶质母细胞瘤（GBM）的免疫抑制和治疗抵抗中发挥重要作用，该肿瘤为高度侵袭性脑肿瘤，预后较差。本研究开发了一种人工智能（AI）驱动的病理组学模型，用于预测接受放疗的GBM患者的Treg浸润并对其预后进行分层。利用从苏木精-伊红染色活检组织中提取的高维特征，我们通过最小冗余最大相关（mRMR）和Relief算法进行特征选择后，采用梯度提升法构建了病理组学评分（PS）。该模型在多中心队列（n > 300）中展现出较强的预测性能，高PS与Treg水平升高及总生存期缩短显著相关（TCGA：HR = 2.16；验证队列：HR = 1.706）。基因集富集分析将高PS与免疫逃逸通路相关联，包括Notch和IL-6/JAK/STAT3信号通路，同时DNA修复基因RAD50表达升高，提示其与放疗反应可能存在关联。这一基于AI的病理组学框架为免疫谱分析和结局预测提供了稳健且可解释的工具，为胶质母细胞瘤的精准放疗和Treg靶向治疗策略开辟了道路。
Abstract
Regulatory T cells (Tregs) contribute significantly to immune suppression and therapy resistance in isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM), a highly aggressive brain tumor with poor prognosis. In this study, we developed an artificial intelligence (AI)-powered pathomics model to predict Treg infiltration and stratify prognosis in GBM patients undergoing radiotherapy. Using high-dimensional features extracted from hematoxylin and eosin-stained biopsies, we constructed a pathomics score (PS) via gradient boosting after feature selection with Minimum Redundancy Maximum Relevance (mRMR) and Relief algorithms. The model demonstrated strong predictive performance across multi-center cohorts (n > 300), where high PS was significantly associated with elevated Treg levels and reduced overall survival (TCGA: HR = 2.16; validation cohort: HR = 1.706). Gene set enrichment analysis linked high PS to immune-evasive pathways, including Notch and IL-6/JAK/STAT3 signaling, along with increased expression of DNA repair gene RAD50, suggesting a potential association with radiotherapy response. This AI-based pathomics framework offers a robust and interpretable tool for immunoprofiling and outcome prediction, paving the way for precision radiotherapy and Treg-targeted therapeutic strategies in glioblastoma.
如何引用
AMA
Shaoli Peng, Jialei Chen, Xuezhen Wang, Xingfu Wang, Qiuyuan Yue, Hailin Lan, et al. AI-based pathomics model predicts regulatory T cell infiltration and radiotherapy response in IDH-wild-type glioblastoma.. Frontiers in immunology. 2026; doi:10.3389/fimmu.2026.1817892.
APA
Shaoli Peng, Jialei Chen, Xuezhen Wang, Xingfu Wang, Qiuyuan Yue, Hailin Lan, et al (2026). AI-based pathomics model predicts regulatory T cell infiltration and radiotherapy response in IDH-wild-type glioblastoma.. Frontiers in immunology. https://doi.org/10.3389/fimmu.2026.1817892
GB/T 7714
Shaoli Peng, Jialei Chen, Xuezhen Wang, Xingfu Wang, Qiuyuan Yue, Hailin Lan, et al. AI-based pathomics model predicts regulatory T cell infiltration and radiotherapy response in IDH-wild-type glioblastoma.[J]. Frontiers in immunology, 2026 doi:10.3389/fimmu.2026.1817892.
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