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Updated: Jul 29, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Artificial intelligence-enabled multi-omics biomarkers for immune checkpoint blockade: mechanisms, predictive
Xiaodong Wang1, Di Xiong2, Songli Cui1
1Department of Oncology, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, China.
Abstract:
Immune checkpoint inhibitors (ICIs) have transformed oncology, yet durable benefit remains confined to a minority of patients, revealing the limitations of single biomarkers such as PD-L1 expression, tumor mutational burden, and microsatellite instability. Multi-omics profiling, spanning genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiomics, and imaging-derived radiomics/pathomics, enables a systems-level interrogation of tumor-immune interactions. It captures lineage plasticity, antigen-presentation defects, metabolic and epigenetic suppression, stromal remodeling, and microbiome-driven immune tone that collectively shape ICI sensitivity and resistance. Artificial intelligence (AI) and machine learning are increasingly indispensable for fusing these heterogeneous, high-dimensional data into deployable composite predictors and mechanistically grounded signatures, while explainability approaches (e.g., SHAP, Grad-CAM) help link model outputs to actionable biology. This review synthesizes emerging AI-enabled multi-omics biomarkers across major tumor types, highlights clinical applications in response stratification, combination-therapy selection, and longitudinal monitoring, and discusses key translational barriers, including cohort and platform heterogeneity, limited prospective validation, privacy constraints, model drift, and equity. We conclude by outlining future directions in single-cell and spatial multi-omics integration, federated learning, and generative modeling to accelerate robust, generalizable precision immunotherapy. Pragmatic implementation will require harmonized pre-analytics, clinically feasible assays or distilled panels, and decision-support interfaces that communicate calibrated uncertainty to oncologists.
Insights
Multi-omics profiling combined with artificial intelligence (AI) offers a comprehensive approach to predict responses to immune checkpoint inhibitors (ICIs). This strategy moves beyond single biomarkers to reveal complex tumor-immune interactions for personalized cancer therapy.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
- Artificial Intelligence
Background:
- Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but their durable efficacy is limited to a subset of patients.
- Current biomarkers like PD-L1, tumor mutational burden, and microsatellite instability are insufficient for predicting ICI response.
- Understanding the complex tumor-immune microenvironment is crucial for improving immunotherapy outcomes.
Purpose of the Study:
- To review the application of multi-omics profiling integrated with artificial intelligence (AI) for developing advanced biomarkers in cancer immunotherapy.
- To explore how AI-driven multi-omics data can enhance the prediction of patient response to ICIs.
- To identify challenges and future directions for implementing AI-enabled multi-omics biomarkers in clinical practice.
Main Methods:
- Comprehensive analysis of multi-omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiomics, and imaging-derived radiomics/pathomics.
- Application of AI and machine learning algorithms to integrate heterogeneous, high-dimensional datasets.
- Utilization of explainability approaches (e.g., SHAP, Grad-CAM) to link AI model outputs to biological mechanisms.
- Synthesis of findings from existing literature on AI-enabled multi-omics biomarkers across various tumor types.
Main Results:
- Multi-omics profiling captures critical biological processes influencing ICI sensitivity, such as lineage plasticity, antigen presentation, metabolic/epigenetic suppression, stromal remodeling, and microbiome effects.
- AI and machine learning are essential for creating composite predictors and mechanistic signatures from complex multi-omics data.
- AI-enabled multi-omics biomarkers show promise in stratifying patient responses, guiding combination therapy selection, and enabling longitudinal monitoring.
- Key translational barriers include data heterogeneity, limited prospective validation, privacy concerns, model drift, and equity issues.
Conclusions:
- AI-driven multi-omics represents a powerful paradigm shift beyond single biomarkers for precision immunotherapy.
- Future directions involve integrating single-cell and spatial multi-omics, federated learning, and generative modeling for robust and generalizable biomarkers.
- Successful clinical implementation requires standardized pre-analytics, clinically validated assays, and decision-support tools that convey model uncertainty to clinicians.
