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Related Concept Videos

Cytotoxic T Cells-mediated Immune Response01:27

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Cytotoxic T cells are a vital component of the immune system. They have the remarkable ability to identify and target antigens on infected or abnormal cells. These antigens often originate from intracellular pathogens such as viruses or abnormal proteins cancer cells produce.
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...
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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Predictive Immune Modeling of Solid Tumors
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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.

Journal of Multidisciplinary Healthcare
|March 4, 2026
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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.

Keywords:
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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.