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  • Artificial Intelligence and Bioinformatics in Cancer: From Multi-Omics Integration to Precision Immunotherapy

  • Reyhaneh Sajed,1,* Mehdi Dadashpour,2 Najaf Allahyari Fard,3
    1. semums
    2. semums
    3. National Institute of Genetic Engineering and Biotechnology


  • Introduction: Background: The convergence of artificial intelligence (AI) and bioinformatics has transformed cancer research by enabling the analysis of complex multi-omics datasets. Recent advances in single-cell and spatial profiling technologies have significantly enhanced our understanding of tumor biology and immunotherapy response. Objective: This review synthesizes current knowledge on the application of AI and bioinformatics in cancer, focusing on patient stratification, biomarker discovery, treatment optimization, and immunotherapy response prediction.
  • Methods: We conducted a comprehensive narrative review of recent literature on AI-driven bioinformatics applications in oncology.
  • Results: AI models have demonstrated remarkable capabilities in analyzing high-dimensional genomic, transcriptomic, proteomic, radiomic, and histopathological data. Multi-omics integration through deep learning architectures, including transformers and graph neural networks, has enabled the discovery of novel predictive biomarkers and improved patient stratification for immunotherapy. Foundation models trained on large-scale datasets have shown superior performance in predicting immune checkpoint inhibitor response, outperforming traditional biomarkers such as PD-L1 and TMB. Digital pathology and radiomics approaches, combined with machine learning, have enabled non-invasive prediction of molecular features directly from routine H&E-stained slides and medical images. In cell therapy, AI has been applied to optimize CAR-T manufacturing, predict cytokine release syndrome, and identify tumor-reactive T-cell signatures. Challenges including data heterogeneity, model interpretability, prospective validation, and regulatory approval remain significant barriers to clinical translation.
  • Conclusion: AI and bioinformatics are reshaping precision oncology by enabling personalized immunotherapy strategies. Addressing current limitations through prospective multicenter validation and explainable AI frameworks will be essential for clinical adoption.
  • Keywords: Bioinformatics, Artificial Intelligence, Cancer, Immunotherapy, Multi-omics

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