Abstract 1014: Multimodal AI predicts immune checkpoint inhibitor response from clinically available inputs and whole-slide images with explainable tumor biology and combination therapy insights

Abstract Background: Immune checkpoint inhibitors (ICIs) provide durable benefit for a subset of patients, yet most do not respond and current biomarkers (PD-L1, TMB, MSI) have limited predictive value. We developed AIM-io, a multimodal, biologically interpretable AI model that predicts ICI response using routine clinical inputs including liquid biopsy, tissue NGS and, for proof-of-concept, whole-slide images (WSI) processed with publicly available research-only foundation-model embeddings. AIM-io integrates reconstructed gene-expression and TME programs with predicted small-molecule sensitivities, enabling biological interpretation of each prediction. Methods: A pan-cancer real-world cohort (∼3,000 patients, 13 tumor types) treated with anti-PD-(L)1 or anti-CTLA-4 therapy was assembled with linked rwOS/rwPFS. Inputs included liquid-biopsy, tissue NGS, or WSIs encoded embeddings (e.g., GigaPath, UNI). AIM-io incorporated clinical variables, genomic alterations from commercial LDTs, reconstructed expression/TME signatures (AIM-Ex), ICI drug/target embeddings, and AIM-Bx-derived small-molecule sensitivity profiles (∼100 agents). Performance was assessed using C-index, hazard ratios, KM stratification, and comparisons to available biomarkers. Results: AIM-io showed robust cross-modality performance. In held-out LDT data, AIM-io significantly separated responders vs. non-responders (p 10-7, HR = 0.23, 95% CI 0.14-0.41), outperforming PD-L1 (p = 0.26, HR = 0.77, 95% CI 0.48-1.22) and TMB (p 10-3, HR = 0.39, 95% CI 0.24-0.65). Using liquid-biopsy DNA, AIM-io significantly stratified outcomes (p 0.05, HR = 0.63, 95% CI 0.39-1.01). Results were consistent across cancer types and NGS platforms. With WSI-only inputs, AIM-io achieved significant outcome separation (HR = 0.46, p 0.05; 95% CI 0.22-0.97) and outperformed models using WSI embeddings alone (HR = 0.62, p = 0.12), demonstrating feasibility in settings lacking molecular assays. Predicted responders showed enrichment for reconstructed TME programs (e.g., lymphocyte infiltration), whereas non-responders showed immunosuppressive signatures (e.g., TGF-β, wound healing). Small-molecule sensitivity predictions differed, with responders showing greater predicted PARPi sensitivity and non-responders enriched for VEGFi sensitivity. Conclusions: AIM-io provides an explainable multimodal framework for ICI-response prediction using diverse, clinically accessible inputs—including liquid biopsy DNA and WSI embeddings. By integrating reconstructed expression, TME biology, and predicted therapeutic vulnerabilities, AIM-io offers an assay-agnostic approach for retrospective evaluation of immunotherapy response and hypothesis generation for rational combinations. Prospective validation is warranted. Citation Format: Felicia Kuperwaser, Sunil Kumar, Sepideh Foroutan, Dillon Tracy, Kevin Freisen, Taylor Wood, Zong Miao, Nathaniel Tann, Fahad Khan, Jean Michel Rouly, Anshu Jain, Jeff Sherman, Emily Vucic, Maayan Baron. Multimodal AI predicts immune checkpoint inhibitor response from clinically available inputs and whole-slide images with explainable tumor biology and combination therapy insights 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 1014.