文献类型：期刊文献

英文题名：A spatiotemporal state-inference framework for adaptive immunotherapy in glioblastoma

作者：Chen, Xiao[1];Li, Shuping[2];Liu, Xiaojun[2];Ma, Wen[2]

第一作者：陈鑫;陈霞;陈欣

通信作者：Ma, W[1]

机构：[1]Gansu Univ Chinese Med, Clin Med Sch 1, Lanzhou, Peoples R China;[2]Gansu Prov Hosp, Dept Radiotherapy, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Radiotherapy, Lanzhou, Peoples R China.

年份：2026

卷号：16

外文期刊名：FRONTIERS IN ONCOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001815488100001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. This work was supported by Gansu Provincial Hospital (Grant Nos. 24GSSYE-9 and 24GSSYH-1). The funder had no role in the design of this review, interpretation of the literature, writing of the manuscript, or decision to submit it for publication.

语种：英文

外文关键词：adaptive immunotherapy; glioblastoma; graph neural networks; liquid biopsy; spatiotemporal state inference; tumor microenvironment

摘要：Although immunotherapy has transformed outcomes in several solid tumors, it has yielded little survival benefit in glioblastoma (GBM). This limited efficacy may in part reflect not only modest drug activity and a chronically immunosuppressive microenvironment, but also a temporal mismatch between fixed treatment schedules and a tumor-immune ecosystem that evolves across space and time. This review outlines the GBM Immune-Spatiotemporal Feedback Loop (GBM-ISFL), a clinician-governed, hypothesis-generating framework that conceptualizes adaptive immunotherapy as a process of longitudinal sensing, biologic state inference, phase-matched intervention, and iterative feedback. Drawing on single-cell and spatial multi-omics, radiologic assessment, and liquid-biopsy studies, we outline a four-phase atlas of GBM evolution and define a patient-specific Critical Transition Window. This window may functionally overlap with the post-radiotherapy interval highlighted by Response Assessment in Neuro-Oncology (RANO) 2.0, but it should not be treated as a fixed calendar block or as a validated clinical interval. To narrow the resulting observability gap, we position spatiotemporal graph neural networks (STGNNs) as candidate tools for noninvasive inference of latent tumor-immune states from serial multimodal data, including imaging dynamics, treatment exposure, and circulating biomarker trajectories. We further describe how uncertainty-aware state inference could support exploratory phase-specific therapeutic reasoning, translational validation, and lifecycle governance. By reframing GBM immunotherapy around biologic phase rather than chronology alone, the GBM-ISFL offers a testable route toward adaptive, state-informed, and clinically governed precision intervention, but it should not be interpreted as a current standard-of-care algorithm.