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Cancer immunotherapy, which leverages the immune system to combat tumor cells, has made significant advancements in oncology treatment in recent years. Yet significant challenges remain in predicting treatment responses and understanding mechanisms of resistance. Artificial intelligence (AI) and machine learning (ML) provide powerful tools to address these challenges, enabling breakthroughs in patient stratification, biomarker discovery, and treatment strategy optimization. While remarkable progress has been made in developing deep learning frameworks, including large language models (LLMs) to integrate the exponentially growing multi‐omics biomedical data for cancer immunotherapy, little effort has been made to systematically and comprehensively summarize these developments or critically evaluate their translational potential. To fill these gaps, this review comprehensively examines the current landscape and future directions of AI/ML applications in cancer immunotherapy. Specifically, we discuss four key areas in AI for cancer immunotherapy: (1) patient stratification, (2) biomarker discovery, (3) treatment strategy optimization, and (4) foundation models and LLMs for cancer immunotherapy. In addition, we also critically discuss current limitations and future directions for existing AI approaches for cancer immunotherapy, highlighting the actionable insights and roadmaps to accelerate the integration of AI/ML into precision cancer immunotherapy.
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This content will become publicly available on January 20, 2027
Medea: An omics AI agent for therapeutic discovery
AI agents promise to empower biomedical discovery, but realizing this promise requires the ability to complete transparent, long-horizon analyses using tools. Agents must make intermediate decisions explicit, and validate each decision and output against data and tool constraints as the analysis unfolds. We present Medea, an AI agent that takes an omics objective and executes a transparent multi-step analysis using tools. Medeacomprises four modules: research planning with context and integrity verification, code execution with pre- and post-run checks, literature reasoning with evidence-strength assessment, and a consensus stage that reconciles evidence across datasets, tools, and literature. Medeauses 20 tools spanning single-cell and bulk transcriptomic datasets, cancer vulnerability maps, pathway knowledge bases, and machine learning models. We evaluate Medeaacross 5,679 analyses in three open-ended domains: target identification across five diseases and cell type contexts (2,400 analyses), synthetic lethality reasoning in seven cell lines (2,385 analyses), and immunotherapy response prediction in bladder cancer (894 patient analyses). In evaluations that vary large language models, tool sets, omics objectives, and agentic modules, Medeaimproves the performance of existing approaches by up to 46% for target identification, 22% for synthetic lethality, and 24% for immunotherapy response prediction, while maintaining low failure rates and calibrated abstention. Medeashows that verification-aware AI agents improve performance by producing transparent analyses, not simply more efficient workflows.
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- Award ID(s):
- 2339524
- PAR ID:
- 10673904
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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