Pan-Cancer Prediction of Immune Response from Tumor Pathology Using TIME_ACT Signature

Background: Immune checkpoint blockade (ICB) has transformed cancer therapy, yet only a subset of patients benefit, underscoring the need for robust, generalizable biomarkers capable of identifying tumors with sufficient immune activation. Although numerous immune-related gene signatures and transcriptomic approaches have been proposed, most lack pan-cancer robustness and fail to generalize across independent cohorts. To address this gap, we derived TIME_ACT, an unsupervised 66-gene tumor immune activation (“hotness”) signature that integrates immune infiltration, inflammatory activity, and spatial TIL density. This signature accurately predicts tumor hotness across diverse cancer types, forming a robust pan-cancer measure of immune activation. Methods: TIME_ACT was evaluated across 22 publicly available pre-treatment ICB transcriptomic cohorts (n=1,416) spanning seven cancer types. To enable prediction from pathology, TIME_ACT scores were inferred from H&E slides using Path2Omics, a recently published deep-learning model trained on TCGA, and validated in nine new histopathology cohorts from different medical centers and populations (n=459). Results: TIME_ACT accurately identifies immune-hot tumors across eight TCGA cancer types (AUC 0.96-1.00) and two external datasets (AUC 0.96-0.98). TIME_ACT genes were consistently enriched in T cells, B cells, and dendritic cells at single-cell resolution. Spatial analyses shows that TIME_ACT-high regions co-localize with lymphocyte-dense niches adjacent to the tumor epithelium. Across 22 transcriptomic ICB cohorts, TIME_ACT robustly predicted response (mean AUC 0.74; mean OR 5.49). Importantly, histopathology-inferred TIME_ACT scores achieve strong performance across nine multi-centric cohorts (mean AUC 0.73; mean OR 5.07), matching transcriptomic-based prediction levels and importantly, outperforming direct slide-based models. Conclusions: TIME_ACT is a robust pan-cancer tumor immune activation signature that predicts ICB response directly from the readily available tumor pathology slides. Upon further prospective testing and validation, it offers an exciting new way for further democratizing precision immunotherapy. Citation Format: Danh-Tai Hoang, Sumit Mukherjee, Sumeet Patiyal, Lipika R. Pal, Tiangen Chang, Sumona Biswas, Saugato Rahman Dhruba, Amos Stemmer, Arashdeep Singh, Abbas Yousefi-Rad, Tien-Hua Chen, Binbin Wang, Denis Marino, Wonwoo Shon, Yuan Yuan, Mark Faries, Omid Hamid, Karen Reckamp, Barliz Waissengrin, Beatriz Ornelas, Keluo Yao, Pen-Yuan Chu, Lisa Ley, Dilara Akbulut, Nourhan El Ahmar, Sabina Signoretti, David Alexander Braun, Joo Sang Lee, Hyunjeong Joo, Hyungsoo Kim, Arsen Osipov, Robert A. Figlin, Jair Bar, Iris Barshack, Chi-Ping Day, Sridhar Hannenhalli, Karine Sargsyan, Andrea B. Apolo, Kenneth D. Aldape, Muh-Hwa Yang, Michael B. Atkins, Ze’ev A. Ronai, Eytan Ruppin. Pan-cancer prediction of response to immune checkpoint blockade from histopathology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3999.