Artificial Intelligence–Enabled Multi-Omics for Predicting Immune Checkpoint Inhibitor Response and Resistance

Abstract: Immune checkpoint inhibitors (ICIs) have reshaped oncology, yet overall response rates remain modest and resistance is common, driven by tumor heterogeneity and evolving tumor–immune crosstalk. Established biomarkers (PD-L1, tumor mutational burden, microsatellite instability) provide incomplete prediction. Multi-omics profiling across genomic, transcriptomic, proteomic, epigenomic, metabolomic and microbiomic layers offers a systems-level view of malignant and immune states, uncovering determinants of ICI efficacy such as lineage plasticity, stromal remodeling, immunometabolic reprogramming and microbiome-associated immune modulation. Artificial intelligence (AI) is uniquely positioned to fuse these heterogeneous data, learn non-linear cross-layer signatures, and enable interpretable predictions using approaches such as SHAP and Grad-CAM. Representative models link routine histology or imaging to molecular phenotypes, stratify patients beyond single biomarkers, and may nominate rational combinations that target oncogenic pathways, lactate-driven immune suppression, or the gut microbiome. In this narrative review, we synthesize recent AI–multi-omics advances for response modeling, immune-relevant tumor subtyping, and clinical translation, including radiomics/pathomics integration and liquid-biopsy–based monitoring, as well as emerging applications in toxicity risk prediction. We also discuss barriers to implementation—platform heterogeneity, limited prospective validation, bias, interpretability and cost—and outline future directions, including single-cell and spatial multi-omics integration, federated learning and generative modeling to improve robustness and equity of precision immunotherapy. Keywords: artificial intelligence, multi-omics, immunotherapy, cancer, biomarkers