# Artificial intelligence–enabled multi-omics biomarkers for immune checkpoint blockade: mechanisms, predictive modeling, and clinical translation

## TL;DR

This paper reviews how combining multiple types of biological data with AI can improve cancer immunotherapy predictions and outcomes.

## Contribution

The paper introduces AI-enabled multi-omics biomarkers that integrate diverse data types to better predict immune checkpoint inhibitor responses.

## Key findings

- Multi-omics data combined with AI improves prediction of ICI response.
- Explainable AI methods link model outputs to biological mechanisms.
- Challenges include data heterogeneity and limited prospective validation.

## 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.

Infographic summarizing AI-enabled multi-omics biomarkers for immune checkpoint blockade, showing sources like genomics, proteomics, and microbiome feeding into an AI/ML fusion engine, generating a composite biomarker score for clinical decision-making, with identified roadblocks such as heterogeneity, validation, privacy, model drift, and equity, and future directions including single-cell mapping, federated learning, generative models, feasible panels, and decision support with uncertainty.