The Application of Artificial Intelligence in Tumor Immunotherapy: Integrating Multi-Omics Data to Achieve Efficacy Prediction and Combination Strategies
DOI:
https://doi.org/10.65455/q72yq328Keywords:
Artificial Intelligence, Tumor Immunotherapy, Multi-Omics, Efficacy Prediction, Combination Therapy, Machine LearningAbstract
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet only a minority of patients achieve durable clinical benefit due to primary or acquired resistance. This heterogeneity necessitates precise predictive biomarkers and rational combination strategies. The rapid accumulation of multi-omics data-including genomics, transcriptomics, radiomics, pathomics, and microbiomics-offers unprecedented opportunities to decode tumor-immune interactions. Artificial intelligence, particularly machine learning and deep learning, excels at integrating high-dimensional, heterogeneous data to build interpretable efficacy prediction models and accelerate the discovery of synergistic combination regimens. In this review, we systematically summarize AI-based multimodal integration frameworks (e.g:deep multimodal fusion, graph neural networks, and single-cell omics models) and key technologies. We critically analyze the types and characteristics of multi-omics data used in immunotherapy prediction. Furthermore, we discuss AI-driven applications in personalized stratification, combination therapy target discovery, and drug synergy prediction. Finally, we identify current bottlenecks-including interpretability, data standardization, clinical validation, and model generalizability-and propose future directions. This review provides a systematic reference for transitioning from empirical treatment to data-driven precision immunotherapy.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP
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