To outline the GBM Immune–Spatiotemporal Feedback Loop (GBM-ISFL) framework for adaptive immunotherapy in glioblastoma (GBM), addressing the limitations of fixed treatment schedules in a dynamic tumor-immune ecosystem.
Approach:
Conceptual Framework: The GBM-ISFL conceptualizes adaptive immunotherapy as a process involving longitudinal sensing, biologic state inference, phase-matched intervention, and iterative feedback.
Data Utilization: Utilizes single-cell and spatial multi-omics, radiologic assessment, and liquid-biopsy studies to define a four-phase atlas of GBM evolution.
Critical Transition Window: Defines a patient-specific Critical Transition Window that may overlap with the post-radiotherapy interval but should not be treated as a fixed clinical interval.
AI Integration: Proposes the use of spatiotemporal graph neural networks (STGNNs) for noninvasive inference of tumor-immune states from multimodal data.
Key Findings:
Immunotherapy has shown limited survival benefit in GBM due to a combination of modest drug activity and an evolving tumor-immune ecosystem.
The GBM tumor microenvironment is dynamic, influenced by treatment and biological factors, necessitating a timing-sensitive approach to therapy.
A Critical Transition Window exists during which therapeutic interventions may be more effective, but it is patient-specific and not strictly defined by calendar time.
Interpretation:
The GBM-ISFL framework offers a testable hypothesis for adaptive immunotherapy in GBM, emphasizing biologic phase over fixed treatment schedules.
Limitations:
The GBM-ISFL is a hypothesis-generating framework and not a validated clinical algorithm.
The observability gap between necessary tumor-immune dynamics and measurable signals remains a challenge.
Conclusion:
The framework aims to support adaptive, state-informed, and clinically governed precision interventions in GBM immunotherapy.
