Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. (June 2023)

• Record Type: Journal Article
• Authors: Li, Tong; Li, Yupeng; Zhu, Xiaoyi; He, Yao; Wu, Yanling; Ying, Tianlei; Xie, Zhi
• Is Part Of: Seminars in cancer biology. Volume 91(2023)
• Page Start: 50
• Page End: 69
• Publication Date: 2023-06
• Subjects: AI artificial intelligence -- ICB immune checkpoint blockade -- ACT adoptive cell therapy -- PD-1 programmed cell death protein 1 -- CTLA-4 cytotoxic T-lymphocyte-associated protein 4 -- TAs tumor-associated antigens -- NGS next-generation sequencing -- ADCs antibodydrug conjugates -- mAbs monoclonal antibodies -- ADAs anti-drug antibodies -- PD-L1 programmed cell death ligand-1 -- TMB tumor mutational burden -- MSI microsatellite instability -- dMMR deficient mismatch repair -- irAEs immune-related adverse events -- DNNs deep neural networks -- MHC major histocompatibility complex -- ML machine learning -- NNs neural networks -- ANNs artificial neural networks -- MS mass spectrometry -- BA binding affinity -- AP antigen processing -- DL deep learning -- PWMs position weight matrices -- NLP natural language processing -- GRU gate recurrent unit -- CNNs convolutional neural networks -- pMHCs peptide-MHC complexes -- TCRs T cell surface receptors -- TILs tumor infiltrating lymphocytes -- IHC immunohistochemistry -- TME tumor microenvironment -- FFPE formalin-fixed, paraffin-embedded -- NSCLC non-small cell lung cancer -- HCDR3 heavy-chain complementarity-determining region 3 -- DMS deep mutational scanning -- LDA linear discriminant analysis; Artificial intelligence -- Cancer immunotherapy -- Deep learning -- Machine learning; Cancer -- Periodicals; Neoplasms -- Periodicals; Review Literature; Cancer -- Périodiques; Electronic journals; 616.994
• DOI: 10.1016/j.semcancer.2023.02.007
• Languages: English
• ISSNs: 1044-579X

Abstract

Abstract: Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.