Related Experiment Video
Updated: Apr 11, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Artificial Intelligence-Enabled Multi-Omics for Predicting Immune Checkpoint Inhibitor Response and Resistance
Xiaodong Wang1, Jing He1, Gouping Ding1
1Department of Oncology, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, People's Republic of China.
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.
Insights
Artificial intelligence (AI) and multi-omics data integration improve prediction of response to immune checkpoint inhibitors (ICIs) in cancer. This approach overcomes limitations of current biomarkers for personalized immunotherapy strategies.
Area of Science:
- Oncology
- Immunotherapy
- Bioinformatics
- Artificial Intelligence
Background:
- Immune checkpoint inhibitors (ICIs) have transformed cancer treatment but face challenges with modest response rates and common resistance.
- Existing biomarkers like PD-L1, tumor mutational burden, and microsatellite instability offer incomplete predictive power due to tumor heterogeneity and complex immune interactions.
Purpose of the Study:
- To review advances in artificial intelligence (AI)-driven multi-omics data integration for predicting ICI efficacy.
- To explore how AI can uncover novel determinants of treatment response and guide personalized immunotherapy strategies.
Main Methods:
- Synthesis of recent literature on AI and multi-omics profiling (genomic, transcriptomic, proteomic, epigenomic, metabolomic, microbiomic).
- Discussion of AI techniques (e.g., SHAP, Grad-CAM) for fusing heterogeneous data and identifying cross-layer signatures.
- Integration of radiomics, pathomics, and liquid biopsy data for response modeling and monitoring.
Main Results:
- Multi-omics profiling reveals key factors influencing ICI efficacy, including lineage plasticity, stromal remodeling, immunometabolic reprogramming, and microbiome modulation.
- AI models can link routine imaging/histology to molecular phenotypes, stratify patients beyond single biomarkers, and suggest combination therapies.
- AI-multi-omics approaches enable improved immune-relevant tumor subtyping and toxicity risk prediction.
Conclusions:
- AI-powered multi-omics integration represents a significant advancement in precision immunotherapy, enhancing prediction of treatment response and patient stratification.
- Future directions include integrating single-cell/spatial multi-omics, federated learning, and generative modeling to improve model robustness, equity, and clinical translation.
Related Concept Videos
01:27Cytotoxic T Cells-mediated Immune Response
Immunological surveillance is the ability of immune cells to monitor and eliminate infected cells with intracellular pathogens, neoplastically transformed cells, and cells with non-self antigens. Cytotoxic T cells and NK...
01:27Tumor Immunotherapy