Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology
Mukherjee, S.; Patiyal, S.; Pal, L. R.; Chang, T.; Biswas, S.; Dhruba, S. R.; Stemmer, A.; Singh, A.; Yousefi-Rad, A.; Chen, T.-H.; Wang, B.; Marino, D.; Shon, W.; Yuan, Y.; Faries, M.; Hamid, O.; Reckamp, K.; Waissengrin, B.; Ornelas, B.; Chu, P.-Y.; Boudjadi, S.; Ley, L.; Akbulut, D.; Ahmar, N. E.; Signoretti, S.; Braun, D. A.; Joo, H.; Kim, H.; Osipov, A.; Figlin, R. A.; Bar, J.; Barshack, I.; Day, C.-P.; Sargsyan, K.; Apolo, A. B.; Aldape, K.; Yang, M.-H.; Atkins, M. B.; Ronai, Z. A.; Hoang, D.-T.; Ruppin, E.
Show abstract
Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA melanoma data. First, TIME_ACT scores accurately identify tumors with activated immune microenvironments across cancer types. Analysis of spatial features of the tumor microenvironment revealed that TIME_ACT-high regions exhibit dense lymphocyte infiltration near tumor cells, indicating localized immune activation. Second, in 15 anti-PD1 transcriptomic cohorts spanning six cancer types, TIME_ACT outperforms 22 established signatures and methods, achieving a mean AUC of 0.76 and a clinically meaningful mean odds ratio of 6.11. Thirdly, TIME_ACT scores can be accurately inferred from tumor histopathology slides. Finally, slide-inferred TIME_ACT scores predict ICB response across eight unseen cohorts, achieving a mean AUC of 0.72 and a mean odds ratio of 5.02. These findings establish TIME_ACT as a robust, pan-cancer, and low-cost predictor of ICB response.
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