Deciphering spatial-temporal mechanisms of PD-1 blockade resistance via biologically informed machine learning.

Immune checkpoint inhibitors (ICIs), especially PD-1/PD-L1 blockade, have transformed cancer therapy; yet objective response rates to anti-PD-(L)1 monotherapy remain ∼20-30% and resistance is common. Meanwhile, multi-omics, spatial profiling, imaging, and clinical datasets are expanding faster than our ability to extract mechanistic insight, creating a "data-rich but mechanism-poor" bottleneck in immuno-oncology. Conventional biomarkers such as tumor mutational burden and PD-L1 expression lack spatiotemporal resolution, while purely data-driven artificial intelligence models often suffer from limited causal interpretability and black-box behavior. To address these challenges, Biologically Informed Machine Learning (BIML) offers a new paradigm by embedding biophysical principles (e.g., pharmacokinetic ordinary differential equations) and biological priors (e.g., protein-protein interaction networks) into predictive models. Recent applications of BIML have enabled integrative decoding of tumor immune microenvironment heterogeneity, quantitative characterization of T-cell exhaustion dynamics, and identification of spatial barriers such as fibroblast-mediated immune exclusion. Crucially, to bridge the translational gap between computational inference and clinical reality, we emphasize the mandatory integration of orthogonal ex vivo validation (e.g., patient-derived organoids and microphysiological systems). Ultimately, by transforming static spatial snapshots into testable dynamic trajectories, this computation-experiment closed-loop aims to generate actionable insights and prioritize rational combination strategies safely under expert oversight.