Dynamic Tumor Immune Microenvironment Remodeling Predicts Response of Checkpoint Inhibitor Therapy

Abstract Immune checkpoint inhibitors (ICI) have transformed cancer therapy, yet the basis of variable patient responses remains unclear. We assembled a longitudinal single-cell RNA sequencing atlas of 441 samples from 241 patients across ten cancers to map treatment-associated remodeling of the tumor immune microenvironment (TIME). Using a hierarchical reference-guided deep-phenotyping framework, we defined 77 immune and stromal subtypes and resolved four conserved TIME subtypes. Approximately 40% of tumors shifted between states during therapy, and the transition was more predictive of outcome than the baseline state. Across 1,988 bulk transcriptomic tumors, favorable transitions toward inflamed or B-cell-enriched subtype tracked with improved response and survival, while persistence in or shifts towards myeloid dominance indicated resistance. We derived a transition score that predicted outcomes for baseline tumors across independent cohorts. These findings establish immunotype transitions as a central determinant of ICI, offering new avenues for response prediction and rational immunotherapy design. Highlights A pan-cancer meta-analysis maps treatment-associated remodeling of the tumor immune microenvironment. Four conserved TIME states emerge across cancer subtypes. TIME transition patterns during treatment are associated with clinical outcome. Baseline immune programs derived transition scores predict treatment response and survival. Competing Interest Statement JCB is receiving speakers bureau honoraria from Amgen, Pfizer, Recordati and Sanofi, is a paid consultant/advisory board member/DSMB member for Almirall, Boehringer Ingelheim, InProTher, ICON, MerckSerono, Pfizer, Regeneron, 4SC, and Sanofi. His group receives research grants from Merck Serono, HTG, IQVIA, and Alcedis. DA is a consultant to Link Cell Therapies. All other authors declare no competing financial interests. Footnotes Major revision with expanded dataset and revised analytical framework. This version includes: 1. Expanded cohort: 441 samples from 241 patients across ten cancer types (vs. 326 samples from 163 patients previously) 2. Revised deep-phenotyping framework: hierarchical reference-guided approach defining 77 immune and stromal subtypes (vs. integration-free framework with 83 cell states) 3. New central finding: identification of four conserved tumor immune microenvironment (TIME) subtypes with ~40% of tumors transitioning between states during therapy Addition of bulk transcriptomic validation cohort (1,988 tumors) Development of transition score for outcome prediction applicable to baseline tumors 4. Reframed focus on immunotype transitions as determinants of ICI response rather than cell subtype compositional changes 5. Updated figures and analyses throughout to reflect new framework and findings