2026-10-14T10:11:47Z https://www.scidb.cn/oai

10.57760/sciencedb.j00217.13805 2026-06-23T11:10:59Z

2026-06-23 Tumor Immunometabolic Atlas for Predicting Immunotherapy Response and Metabolism-Targeted Combination Strategies doi:10.57760/sciencedb.j00217.13805 en Although immune checkpoint inhibitors have transformed the landscape of cancer treatment, most patients still develop primary or acquired resistance, and traditional biomarkers struggle to accurately reflect the complex state of the tumor microenvironment. The tumor immunometabolic atlas integrates mechanisms such as competition for nutritional substrates, accumulation of inhibitory metabolites, and abnormal activation of immune checkpoints, providing a new framework for identifying patient subgroups likely to benefit from treatment, assessing the risk of resistance, and guiding metabolism-targeted combinations with immunotherapy. With advancements in technologies such as molecular imaging, spatial omics, mass spectrometry imaging, and liquid biopsy, multidimensional tumor metabolic features can now be extracted and, following integration via machine learning, applied to subtype classification, treatment response prediction, and decisions regarding combination therapy. This review summarizes the biological basis, feature extraction, and dynamic construction of the tumor immunometabolic atlas, as well as recent progress in its application to predict immunotherapy response and inform combination treatment strategies, thereby providing guidance for clinical precision stratification and combined interventions. Tumor microenvironment; Immunotherapy; Metabolic reprogramming; Tumor immunometabolic atlas; Multi-omics; Machine learning; Immunotherapy response prediction; Combination treatment strategies Ziyang.ZHOU Tao.HUANG Ziwei.ZHANG Hanyu.ZHANG Zhihang.LUO Jie.YANG Yin.HAN Qiyuan.WANG PUBLIC https://creativecommons.org/publicdomain/zero/1.0/ dataset Science Data Bank