2535 Background: Predicting immunotherapy response is challenging in treatment-refractory/resistant (R/R) tumors where heterogeneity limits conventional biomarker utility. BOT (Fc-enhanced anti–CTLA-4) augments T-cell priming, depletes Tregs, and activates antigen-presenting cells. BOT+BAL (anti–PD-1) has shown activity across “cold” and R/R tumors including PD-L1–low and tumor mutational burden–low disease; thus, non-conventional predictive biomarkers are needed. A self-supervised AI foundation model applied to routine pretreatment H ranges from 0.5 chance to 1.0 perfect separation). Cross-validated AUROC was 0.61 in MSS CRC, 0.67 in sarcoma, and 0.77 in ovarian cancer (table; shows all findings). The model-recommended population is predicted to have a higher rate of clinical benefit, as measured by cross-validated precision. Beyond this binary analysis, the concordance index (C-index; measures accuracy and ranges from 0.5 chance to 1.0 perfect) for predicting overall survival (OS; via Cox proportional hazards model) was >0.5 in all tumor types, and highest in ovarian cancer. Conclusions: A self-supervised AI foundation model applied to routine pretreatment H analyses ongoing.
Dalton et al. (Wed,) studied this question.