Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Dougan, 2019, Cancer immunotherapy: beyond checkpoint blockade, Annu. Rev. Cancer Biol., 3, 55, 10.1146/annurev-cancerbio-030518-055552 Tan, 2020, Cancer immunotherapy: pros, cons and beyond, Biomed. Pharmacother., 124, 10.1016/j.biopha.2020.109821 Waldman, 2020, A guide to cancer immunotherapy: from T cell basic science to clinical practice, Nat. Rev. Immunol., 20, 651, 10.1038/s41577-020-0306-5 Rosenberg, 2015, Adoptive cell transfer as personalized immunotherapy for human cancer, Science, 348, 62, 10.1126/science.aaa4967 Sadelain, 2013, The basic principles of chimeric antigen receptor designmaking better chimeric antigen receptors, Cancer Discov., 3, 388, 10.1158/2159-8290.CD-12-0548 Luksza, 2017, A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy, Nature, 551, 517, 10.1038/nature24473 Yarchoan, 2017, Targeting neoantigens to augment antitumour immunity, Nat. Rev. Cancer, 17, 209, 10.1038/nrc.2016.154 Bjerregaard, 2017, MuPeXI: prediction of neo-epitopes from tumor sequencing data, Cancer Immunol. Immunother., 66, 1123, 10.1007/s00262-017-2001-3 Smith, 2019, Alternative tumour-specific antigens, Nat. Rev. Cancer, 19, 465, 10.1038/s41568-019-0162-4 Lee, 2020, Predicting cross-reactivity and antigen specificity of T cell receptors, Front. Immunol., 11, 10.3389/fimmu.2020.565096 Pan, 2021, RNA dysregulation: an expanding source of cancer immunotherapy targets, Trends Pharm. Sci., 42, 268, 10.1016/j.tips.2021.01.006 Chen, 2021, An integrated approach for discovering noncanonical MHC-I peptides encoded by small open reading frames, J. Am. Soc. Mass Spectrom., 32, 2346, 10.1021/jasms.1c00076 Carter, 2022, Designing antibodies as therapeutics, Cell, 185, 2789, 10.1016/j.cell.2022.05.029 Jain, 2017, Biophysical properties of the clinical-stage antibody landscape, Proc. Natl. Acad. Sci. USA, 114, 944, 10.1073/pnas.1616408114 Lipinski, 1997, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev., 23, 3, 10.1016/S0169-409X(96)00423-1 Wilman, 2022, Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery, Brief. Bioinform., 23, 10.1093/bib/bbac267 Alfaleh, 2020, Phage display derived monoclonal antibodies: from bench to bedside, Front. Immunol., 11, 10.3389/fimmu.2020.01986 R. Akbar, H. Bashour, P. Rawat, P.A. Robert, E. Smorodina, T.-S. Cotet, K. Flem-Karlsen, R. Frank, B.B. Mehta, M.H. Vu, et al., Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies, in: Mabs, Taylor & Francis, 2022, p. 2008790. 〈https://doi.org/10.1080/19420862.2021.2008790〉. Doneva, 2021, Predicting immunogenicity risk in biopharmaceuticals, Symmetry, 13, 388, 10.3390/sym13030388 Kang, 2022, Artificial intelligence-based radiomics in the era of immuno-oncology, Oncologist, 27, e471, 10.1093/oncolo/oyac036 Topalian, 2016, Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy, Nat. Rev. Cancer, 16, 275, 10.1038/nrc.2016.36 Saunders, 2012, Role of intratumoural heterogeneity in cancer drug resistance: molecular and clinical perspectives, EMBO Mol. Med., 4, 675, 10.1002/emmm.201101131 Kalbasi, 2020, Tumour-intrinsic resistance to immune checkpoint blockade, Nat. Rev. Immunol., 20, 25, 10.1038/s41577-019-0218-4 Havel, 2019, The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy, Nat. Rev. Cancer, 19, 133, 10.1038/s41568-019-0116-x Wang, 2019, Treatment-related adverse events of PD-1 and PD-L1 inhibitors in clinical trials: a systematic review and meta-analysis, JAMA Oncol., 5, 1008, 10.1001/jamaoncol.2019.0393 Wang, 2019, Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis, JAMIA Open, 2, 173, 10.1093/jamiaopen/ooy045 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Cao, 2018, Deep learning and its applications in biomedicine, Genom. Proteom. Bioinform., 16, 17, 10.1016/j.gpb.2017.07.003 Bhinder, 2021, Artificial intelligence in cancer research and precision medicine, Cancer Discov., 11, 900, 10.1158/2159-8290.CD-21-0090 Deng, 2020, Development of pathological reconstructed high-resolution images using artificial intelligence based on whole slide image, MedComm, 1, 410, 10.1002/mco2.39 Lesterhuis, 2011, Cancer immunotherapy–revisited, Nat. Rev. Drug Discov., 10, 591, 10.1038/nrd3500 Chen, 2019, Neoantigen identification strategies enable personalized immunotherapy in refractory solid tumors, J. Clin. Invest., 129, 2056, 10.1172/JCI99538 Richters, 2019, Best practices for bioinformatic characterization of neoantigens for clinical utility, Genome Med., 11, 56, 10.1186/s13073-019-0666-2 Tran, 2015, Immunogenicity of somatic mutations in human gastrointestinal cancers, Science, 350, 1387, 10.1126/science.aad1253 Parkhurst, 2019, Unique neoantigens arise from somatic mutations in patients with gastrointestinal cancers, Cancer Discov., 9, 1022, 10.1158/2159-8290.CD-18-1494 Jurtz, 2017, NetMHCpan-4.0: improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data, J. Immunol., 199, 3360, 10.4049/jimmunol.1700893 Gfeller, 2018, The length distribution and multiple specificity of naturally presented HLA-I ligands, J. Immunol., 201, 3705, 10.4049/jimmunol.1800914 Bulik-Sullivan, 2018, Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification, Nat. Biotechnol. Wells, 2020, Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction, Cell, 183, 818, 10.1016/j.cell.2020.09.015 Bzdok, 2018, Statistics versus machine learning, Nat. Methods, 15, 233, 10.1038/nmeth.4642 Nielsen, 2003, Reliable prediction of T-cell epitopes using neural networks with novel sequence representations, Protein Sci., 12, 1007, 10.1110/ps.0239403 Lundegaard, 2008, Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers, Bioinformatics, 24, 1397, 10.1093/bioinformatics/btn128 Peters, 2006, A community resource benchmarking predictions of peptide binding to MHC-I molecules, PLoS Comput. Biol., 2, 10.1371/journal.pcbi.0020065 Mei, 2020, A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction, Brief. Bioinform., 21, 1119, 10.1093/bib/bbz051 Vita, 2015, The immune epitope database (IEDB) 3.0, Nucleic Acids Res., 43, D405, 10.1093/nar/gku938 Zhao, 2018, Systematically benchmarking peptide-MHC binding predictors: from synthetic to naturally processed epitopes, PLoS Comput. Biol., 14, 10.1371/journal.pcbi.1006457 Andreatta, 2016, Gapped sequence alignment using artificial neural networks: application to the MHC class I system, Bioinformatics, 32, 511, 10.1093/bioinformatics/btv639 Nielsen, 2016, NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets, Genome Med., 8, 33, 10.1186/s13073-016-0288-x The problem with neoantigen prediction, 2017, Nat. Biotechnol., 35, 97, 10.1038/nbt.3800 O’Donnell, 2018, MHCflurry: open-source class I MHC binding affinity prediction, Cell Syst., 7, 129, 10.1016/j.cels.2018.05.014 O’Donnell, 2020, MHCflurry 2.0: improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing, Cell Syst., 11, 42, 10.1016/j.cels.2020.06.010 Strønen, 2016, Targeting of cancer neoantigens with donor-derived T cell receptor repertoires, Science, 352, 1337, 10.1126/science.aaf2288 Bassani-Sternberg, 2016, Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry, Nat. Commun., 7, 16, 10.1038/ncomms13404 Chen, 2019, Predicting HLA class II antigen presentation through integrated deep learning, Nat. Biotechnol., 37, 1332, 10.1038/s41587-019-0280-2 Bassani-Sternberg, 2016, Unsupervised HLA peptidome deconvolution improves ligand prediction accuracy and predicts cooperative effects in peptide-HLA interactions, J. Immunol., 197, 2492, 10.4049/jimmunol.1600808 Rasmussen, 2014, Uncovering the peptide-binding specificities of HLA-C: a general strategy to determine the specificity of any MHC class I molecule, J. Immunol., 193, 4790, 10.4049/jimmunol.1401689 Rammensee, 1999, SYFPEITHI: database for MHC ligands and peptide motifs, Immunogenetics, 50, 213, 10.1007/s002510050595 Reche, 2007, Prediction of peptide-MHC binding using profiles, Methods Mol. Biol., 409, 185, 10.1007/978-1-60327-118-9_13 Pertseva, 2021, Applications of machine and deep learning in adaptive immunity, Annu. Rev. Chem. Biomol. Eng., 12, 39, 10.1146/annurev-chembioeng-101420-125021 Phloyphisut, 2019, MHCSeqNet: a deep neural network model for universal MHC binding prediction, BMC Bioinform., 20, 270, 10.1186/s12859-019-2892-4 Abelin, 2019, Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction, Immunity, 51, 766, 10.1016/j.immuni.2019.08.012 Lecun, 1989, Backpropagation applied to handwritten zip code recognition, Neural Comput., 1, 541, 10.1162/neco.1989.1.4.541 Gartner, 2021, A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types, Nat. Cancer, 2, 563, 10.1038/s43018-021-00197-6 Lybaert, 2023, Challenges in neoantigen-directed therapeutics, Cancer Cell, 41, 15, 10.1016/j.ccell.2022.10.013 Blankenstein, 2012, The determinants of tumour immunogenicity, Nat. Rev. Cancer, 12, 307, 10.1038/nrc3246 Linnemann, 2015, High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4+ T cells in human melanoma, Nat. Med., 21, 81, 10.1038/nm.3773 Rasmussen, 2016, Pan-specific prediction of peptide-MHC class I complex stability, a correlate of T cell immunogenicity, J. Immunol., 197, 1517, 10.4049/jimmunol.1600582 Jørgensen, 2014, NetMHCstab – predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery, Immunology, 141, 18, 10.1111/imm.12160 Hennecke, 2001, T cell receptor-MHC interactions up close, Cell, 104, 1, 10.1016/S0092-8674(01)00185-4 Szeto, 2020, TCR recognition of peptide-MHC-I: rule makers and breakers, Int. J. Mol. Sci., 22, 68, 10.3390/ijms22010068 Bobisse, 2018, Sensitive and frequent identification of high avidity neo-epitope specific CD8 + T cells in immunotherapy-naive ovarian cancer, Nat. Commun., 9, 1092, 10.1038/s41467-018-03301-0 Cafri, 2019, Memory T cells targeting oncogenic mutations detected in peripheral blood of epithelial cancer patients, Nat. Commun., 10, 449, 10.1038/s41467-019-08304-z Leng, 2020, Pre-existing heterologous T-cell immunity and neoantigen immunogenicity, Clin. Transl. Immunol., 9, 10.1002/cti2.1111 Hu, 2021, Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma, Nat. Med., 27, 515, 10.1038/s41591-020-01206-4 Zhang, 2020, Investigation of antigen-specific T-cell receptor clusters in human cancers, Clin. Cancer Res., 26, 1359, 10.1158/1078-0432.CCR-19-3249 Sidhom, 2021, DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires, Nat. Commun., 12, 1605, 10.1038/s41467-021-21879-w Laumont, 2022, Tumour-infiltrating B cells: immunological mechanisms, clinical impact and therapeutic opportunities, Nat. Rev. Cancer, 22, 414, 10.1038/s41568-022-00466-1 Zaenker, 2016, Autoantibody production in cancer–the humoral immune response toward autologous antigens in cancer patients, Autoimmun. Rev., 15, 477, 10.1016/j.autrev.2016.01.017 Cui, 2021, Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses, Cell, 184, 6101, 10.1016/j.cell.2021.11.007 Jespersen, 2019, Antibody specific B-cell epitope predictions: leveraging information from antibody-antigen protein complexes, Front. Immunol., 10, 10.3389/fimmu.2019.00298 Ras-Carmona, 2022, Prediction of B cell epitopes in proteins using a novel sequence similarity-based method, Sci. Rep., 12, 13739, 10.1038/s41598-022-18021-1 Collatz, 2021, EpiDope: a deep neural network for linear B-cell epitope prediction, Bioinformatics, 37, 448, 10.1093/bioinformatics/btaa773 da Silva, 2022, epitope3D: a machine learning method for conformational B-cell epitope prediction, Brief. Bioinform., 23, 10.1093/bib/bbab423 Cia, 2023, Critical review of conformational B-cell epitope prediction methods, Brief. Bioinform., 24, 10.1093/bib/bbac567 Blass, 2021, Advances in the development of personalized neoantigen-based therapeutic cancer vaccines, Nat. Rev. Clin. Oncol., 18, 215, 10.1038/s41571-020-00460-2 Lang, 2022, Identification of neoantigens for individualized therapeutic cancer vaccines, Nat. Rev. Drug Discov., 21, 261, 10.1038/s41573-021-00387-y Devlin, 2020, Structural dissimilarity from self drives neoepitope escape from immune tolerance, Nat. Chem. Biol., 16, 1269, 10.1038/s41589-020-0610-1 Graves, 2020, A review of deep learning methods for antibodies, Antibodies, 9, 12, 10.3390/antib9020012 Jumper, 2021, Highly accurate protein structure prediction with AlphaFold, Nature, 596, 583, 10.1038/s41586-021-03819-2 Turajlic, 2017, Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis, Lancet Oncol., 18, 1009, 10.1016/S1470-2045(17)30516-8 Yang, 2019, Immunogenic neoantigens derived from gene fusions stimulate T cell responses, Nat. Med., 25, 767, 10.1038/s41591-019-0434-2 Cibulskis, 2013, Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples, Nat. Biotechnol., 31, 213, 10.1038/nbt.2514 Keşmir, 2002, Prediction of proteasome cleavage motifs by neural networks, Protein Eng., 15, 287, 10.1093/protein/15.4.287 Stranzl, 2010, NetCTLpan: pan-specific MHC class I pathway epitope predictions, Immunogenetics, 62, 357, 10.1007/s00251-010-0441-4 Wen, 2020, Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis, Nat. Commun., 11, 1759, 10.1038/s41467-020-15456-w Schmidt, 2021, Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting, Cell Rep. Med., 2 Zhou, 2019, pTuneos: prioritizing tumor neoantigens from next-generation sequencing data, Genome Med., 11, 67, 10.1186/s13073-019-0679-x Hundal, 2020, pVACtools: a computational toolkit to identify and visualize cancer neoantigens, Cancer Immunol. Res., 8, 409, 10.1158/2326-6066.CIR-19-0401 Vita, 2019, The immune epitope database (IEDB): 2018 update, Nucleic Acids Res., 47, D339, 10.1093/nar/gky1006 Grillo-López, 2003, Rituximab (Rituxan®/MabThera®): the first decade (1993–2003), Expert Rev. Anticancer Ther., 3, 767, 10.1586/14737140.3.6.767 Kaplon, 2022, Antibodies to watch in 2022, MAbs, 14, 10.1080/19420862.2021.2014296 Hummer, 2022, Advances in computational structure-based antibody design, Curr. Opin. Struct. Biol., 74, 10.1016/j.sbi.2022.102379 Akbar, 2022, Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies, MAbs, 14, 10.1080/19420862.2021.2008790 Norman, 2020, Computational approaches to therapeutic antibody design: established methods and emerging trends, Brief. Bioinform., 21, 1549, 10.1093/bib/bbz095 Makowski, 2021, Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods, MAbs, 13, 10.1080/19420862.2021.1895540 Georgiou, 2014, The promise and challenge of high-throughput sequencing of the antibody repertoire, Nat. Biotechnol., 32, 158, 10.1038/nbt.2782 Liu, 2020, Antibody complementarity determining region design using high-capacity machine learning, Bioinformatics, 36, 2126, 10.1093/bioinformatics/btz895 Mason, 2021, Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning, Nat. Biomed. Eng., 10.1038/s41551-021-00699-9 Lim, 2022, Predicting antibody binders and generating synthetic antibodies using deep learning, MAbs, 14, 10.1080/19420862.2022.2069075 Saka, 2021, Antibody design using LSTM based deep generative model from phage display library for affinity maturation, Sci. Rep., 11, 5852, 10.1038/s41598-021-85274-7 Makowski, 2022, Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space, Nat. Commun., 13, 3788, 10.1038/s41467-022-31457-3 Jumper, 2021, Highly accurate protein structure prediction with AlphaFold, Nature, 596, 583, 10.1038/s41586-021-03819-2 Baek, 2021, Accurate prediction of protein structures and interactions using a three-track neural network, Science, 7 Xu, 2019, Distance-based protein folding powered by deep learning, Proc. Natl. Acad. Sci. USA, 116, 16856, 10.1073/pnas.1821309116 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Las Vegas, NV, USA, 2016, pp. 770–8. 〈https://doi.org/10.1109/CVPR.2016.90〉. Ruffolo, 2020, Geometric potentials from deep learning improve prediction of CDR H3 loop structures, Bioinformatics, 36, i268, 10.1093/bioinformatics/btaa457 Dunbar, 2014, SAbDab: the structural antibody database, Nucl. Acids Res., 42, D1140, 10.1093/nar/gkt1043 Leaver-Fay, 2011, Rosetta3: an object-oriented software suite for the simulation and design of macromolecules, 545, 10.1016/B978-0-12-381270-4.00019-6 Leman, 2020, Macromolecular modeling and design in Rosetta: recent methods and frameworks, Nat. Methods, 17, 665, 10.1038/s41592-020-0848-2 Weitzner, 2017, Modeling and docking of antibody structures with Rosetta, Nat. Protoc., 12, 401, 10.1038/nprot.2016.180 Ruffolo, 2022, Antibody structure prediction using interpretable deep learning, Patterns, 3, 10.1016/j.patter.2021.100406 Kovaltsuk, 2018, Observed antibody space: a resource for data mining next-generation sequencing of antibody repertoires, J.I, 201, 2502 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is All you Need, n.d., 11. Raybould, 2019, Five computational developability guidelines for therapeutic antibody profiling, Proc. Natl. Acad. Sci. USA, 116, 4025, 10.1073/pnas.1810576116 Chiu, 2019, Antibody structure and function: the basis for engineering therapeutics, Antibodies, 8, 55, 10.3390/antib8040055 Akpinaroglu, 2022, Simultaneous prediction of antibody backbone and side-chain conformations with deep learning, PLoS One, 17, 10.1371/journal.pone.0258173 Abanades, 2022, ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation, Bioinformatics, 38, 1877, 10.1093/bioinformatics/btac016 V.G. Satorras, E. Hoogeboom, M. Welling, E(n) Equivariant Graph Neural Networks, 2022. 〈http://arxiv.org/abs/2102.09844〉, (Accessed 9 October 2022). Muyldermans, 2013, Nanobodies: natural single-domain antibodies, Annu. Rev. Biochem., 82, 775, 10.1146/annurev-biochem-063011-092449 Cohen, 2022, NanoNet: rapid and accurate end-to-end nanobody modeling by deep learning, Front. Immunol., 13, 10.3389/fimmu.2022.958584 Davila, 2022, AbAdapt: an adaptive approach to predicting antibody–antigen complex structures from sequence, Bioinform. Adv., 2 Xu, 2022, Improved antibody‐specific epitope prediction using AlphaFold and AbAdapt**, ChemBioChem, 23, 10.1002/cbic.202200303 Jarasch, 2015, Developability assessment during the selection of novel therapeutic antibodies, J. Pharmaceut. Sci., 104, 1885, 10.1002/jps.24430 Xu, 2019, Structure, heterogeneity and developability assessment of therapeutic antibodies, MAbs, 11, 239, 10.1080/19420862.2018.1553476 Wollacott, 2019, Quantifying the nativeness of antibody sequences using long short-term memory networks, Protein Eng. Des. Sel., 32, 347, 10.1093/protein/gzz031 Prihoda, 2022, BioPhi: a platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning, MAbs, 14, 10.1080/19420862.2021.2020203 Mitragotri, 2014, Overcoming the challenges in administering biopharmaceuticals: formulation and delivery strategies, Nat. Rev. Drug Discov., 13, 655, 10.1038/nrd4363 J. Feng, solPredict: Antibody apparent solubility prediction from sequence by transfer learning, n.d., 27. Rives, 2021, Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences, Proc. Natl. Acad. Sci. USA, 118, 10.1073/pnas.2016239118 Lai, 2022, DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity, Comput. Struct. Biotechnol. J., 20, 2143, 10.1016/j.csbj.2022.04.035 Agrawal, 2016, Computational tool for the early screening of monoclonal antibodies for their viscosities, MAbs, 8, 43, 10.1080/19420862.2015.1099773 Wu, 2007, Development of motavizumab, an ultra-potent antibody for the prevention of respiratory syncytial virus infection in the upper and lower respiratory tract, J. Mol. Biol., 368, 652, 10.1016/j.jmb.2007.02.024 Kelly, 2016, Target-independent variable region mediated effects on antibody clearance can be FcRn independent, MAbs, 8, 1269, 10.1080/19420862.2016.1208330 Grinshpun, 2021, Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies, MAbs, 13, 10.1080/19420862.2021.1932230 Ferrara, 2020, Atypical patterns of response and progression in the era of immunotherapy combinations, Future Oncol., 16, 1707 Cooper, 2017, , Intra-and interobserver reproducibility assessment of PD-L1 biomarker in non–small cell lung cancerreproducibility of PD-L1 biomarker assessment in NSCLC, Clin. Cancer Res., 23, 4569, 10.1158/1078-0432.CCR-17-0151 Brunnström, 2017, PD-L1 immunohistochemistry in clinical diagnostics of lung cancer: inter-pathologist variability is higher than assay variability, Mod. Pathol., 30, 1411, 10.1038/modpathol.2017.59 Bao, 2020, Analysis of the molecular nature associated with microsatellite status in colon cancer identifies clinical implications for immunotherapy, J. Immunother. Cancer, 8, 10.1136/jitc-2020-001437 Samstein, 2019, Tumor mutational load predicts survival after immunotherapy across multiple cancer types, Nat. Genet., 51, 202, 10.1038/s41588-018-0312-8 Runa, 2017, Tumor microenvironment heterogeneity: challenges and opportunities, Curr. Mol. Biol. Rep., 3, 218, 10.1007/s40610-017-0073-7 Lee, 2022, The multi-dimensional biomarker landscape in cancer immunotherapy, Int. J. Mol. Sci., 23, 7839, 10.3390/ijms23147839 Haider, 2020, Systematic assessment of tumor purity and its clinical implications, JCO Precis. Oncol., 4, 995, 10.1200/PO.20.00016 Borst, 2018, CD4+ T cell help in cancer immunology and immunotherapy, Nat. Rev. Immunol., 18, 635, 10.1038/s41577-018-0044-0 Casak, 2021, FDA approval summary: pembrolizumab for the first-line treatment of patients with MSI-H/dMMR advanced unresectable or metastatic colorectal carcinomafda approval summary: pembrolizumab, Clin. Cancer Res., 27, 4680, 10.1158/1078-0432.CCR-21-0557 Le, 2017, Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade, Science, 357, 409, 10.1126/science.aan6733 Priestley, 2019, Pan-cancer whole-genome analyses of metastatic solid tumours, Nature, 575, 210, 10.1038/s41586-019-1689-y Pei, 2021, Benchmarking variant callers in next-generation and third-generation sequencing analysis, Brief. Bioinform., 22, 10.1093/bib/bbaa148 Kather, 2019, Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer, Nat. Med., 25, 1054, 10.1038/s41591-019-0462-y Moss, 2020, Utility of circulating tumor DNA for detection and monitoring of endometrial cancer recurrence and progression, Cancers, 12, 2231, 10.3390/cancers12082231 Lu, 2022, Applications of circulating tumor DNA in immune checkpoint inhibition: emerging roles and future perspectives, Front. Oncol., 12 Echle, 2020, Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning, Gastroenterology, 159, 1406, 10.1053/j.gastro.2020.06.021 Barnetson, 2006, Identification and survival of carriers of mutations in DNA mismatch-repair genes in colon cancer, N. Engl. J. Med., 354, 2751, 10.1056/NEJMoa053493 Yamashita, 2021, Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study, Lancet Oncol., 22, 132, 10.1016/S1470-2045(20)30535-0 Koelzer, 2018, Digital image analysis improves precision of PD-L1 scoring in cutaneous melanoma, Histopathology, 73, 397, 10.1111/his.13528 Kapil, 2018, Deep semi supervised generative learning for automated tumor proportion scoring on NSCLC tissue needle biopsies, Sci. Rep., 8, 10, 10.1038/s41598-018-35501-5 Wu, 2022, Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer, Mod. Pathol., 35, 403, 10.1038/s41379-021-00904-9 Chan, 2019, Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic, Ann. Oncol., 30, 44, 10.1093/annonc/mdy495 Jain, 2020, Predicting tumour mutational burden from histopathological images using multiscale deep learning, Nat. Mach. Intell., 2, 356, 10.1038/s42256-020-0190-5 Niu, 2022, Predicting tumor mutational burden from lung adenocarcinoma histopathological images using deep learning, Front. Oncol., 12, 10.3389/fonc.2022.927426 He, 2020, Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker, J. Immunother. Cancer, 8, 10.1136/jitc-2020-000550 Lam, 2022, A radiomics-based machine learning model for prediction of tumor mutational burden in lower-grade gliomas, Cancers, 14, 3492, 10.3390/cancers14143492 Huang, 2022, Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning, Bioinformatics, 10.1093/bioinformatics/btac641 Galon, 2020, Tumor immunology and tumor evolution: intertwined histories, Immunity, 52, 55, 10.1016/j.immuni.2019.12.018 Pagès, 2018, International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study, Lancet, 391, 2128, 10.1016/S0140-6736(18)30789-X Van den Eynde, 2018, The link between the multiverse of immune microenvironments in metastases and the survival of colorectal cancer patients, Cancer Cell, 34, 1012, 10.1016/j.ccell.2018.11.003 Bruni, 2020, The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy, Nat. Rev. Cancer, 20, 662, 10.1038/s41568-020-0285-7 Scholler, 2022, Tumor immune contexture is a determinant of anti-CD19 CAR T cell efficacy in large B cell lymphoma, Nat. Med., 28, 1872, 10.1038/s41591-022-01916-x Helmink, 2020, B cells and tertiary lymphoid structures promote immunotherapy response, Nature, 577, 549, 10.1038/s41586-019-1922-8 Cabrita, 2020, Tertiary lymphoid structures improve immunotherapy and survival in melanoma, Nature, 577, 561, 10.1038/s41586-019-1914-8 Petitprez, 2020, B cells are associated with survival and immunotherapy response in sarcoma, Nature, 577, 556, 10.1038/s41586-019-1906-8 Reiman, 2019, Integrating RNA expression and visual features for immune infiltrate prediction, 284 Zaitsev, 2022, Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes, Cancer Cell, 40, 879, 10.1016/j.ccell.2022.07.006 Newman, 2019, Determining cell type abundance and expression from bulk tissues with digital cytometry, Nat. Biotechnol., 37, 773, 10.1038/s41587-019-0114-2 Menden, 2020, Deep learning–based cell composition analysis from tissue expression profiles, Sci. Adv., 6, 10.1126/sciadv.aba2619 Chakravarthy, 2018, Pan-cancer deconvolution of tumour composition using DNA methylation, Nat. Commun., 9, 13 Levy, 2020, MethylNet: an automated and modular deep learning approach for DNA methylation analysis, BMC Bioinform., 21, 15, 10.1186/s12859-020-3443-8 Lau, 2019, RNA sequencing of the tumor microenvironment in precision cancer immunotherapy, Trends Cancer, 5, 149, 10.1016/j.trecan.2019.02.006 Fassler, 2020, Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images, Diagn. Pathol., 15, 11 Saltz, 2018, Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images, Cell Rep., 23, 181, 10.1016/j.celrep.2018.03.086 Chen, 2018, Profiling tumor infiltrating immune cells with CIBERSORT, 243 Peterson, 2022, Recent advances and challenges in cancer immunotherapy, Cancers, 14, 3972, 10.3390/cancers14163972 Akbar, 2019, Automated and manual quantification of tumour cellularity in digital slides for tumour burden assessment, Sci. Rep., 9, 1, 10.1038/s41598-019-50568-4 A. Rakhlin, A. Tiulpin, A.A. Shvets, A.A. Kalinin, V.I. Iglovikov, S. Nikolenko, Breast tumor cellularity assessment using deep neural networks, in: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019. 〈https://doi.org/10.1109/ICCVW.2019.00048〉. Diao, 2021, Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes, Nat. Commun., 12, 15, 10.1038/s41467-021-21896-9 Peng, 2022, Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies, Brief. Bioinform., 23, 10.1093/bib/bbac234 Wu, 2022, Single-cell characterization of malignant phenotypes and microenvironment alteration in retinoblastoma, Cell Death Dis., 13, 1, 10.1038/s41419-022-04904-8 Dohmen, 2022, Identifying tumor cells at the single-cell level using machine learning, Genome Biol., 23, 23, 10.1186/s13059-022-02683-1 He, 2020, DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning, Genome Biol., 21, 28, 10.1186/s13059-020-02083-3 Nerurkar, 2020, Transcriptional spatial profiling of cancer tissues in the era of immunotherapy: the potential and promise, Cancers, 12, 2572, 10.3390/cancers12092572 Larroquette, 2022, Spatial transcriptomics of macrophage infiltration in non-small cell lung cancer reveals determinants of sensitivity and resistance to anti-PD1/PD-L1 antibodies, J. Immunother. Cancer, 10, 10.1136/jitc-2021-003890 Lewis, 2021, Spatial omics and multiplexed imaging to explore cancer biology, Nat. Methods, 18, 997, 10.1038/s41592-021-01203-6 Choi, 2022, Deep learning-based tumor microenvironment cell types mapping from H&E images of lung adenocarcinoma using spatial transcriptomic data, Cancer Res., 82 Zubair, 2022, Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model, Nucleic Acids Res., 50, 10.1093/nar/gkac320 He, 2020, Integrating spatial gene expression and breast tumour morphology via deep learning, Nat. Biomed. Eng., 4, 827, 10.1038/s41551-020-0578-x Bergenstråhle, 2022, Super-resolved spatial transcriptomics by deep data fusion, Nat. Biotechnol., 40, 476, 10.1038/s41587-021-01075-3 Gide, 2018, Primary and acquired resistance to immune checkpoint inhibitors in metastatic melanomaresistance to immunotherapy in melanoma, Clin. Cancer Res., 24, 1260, 10.1158/1078-0432.CCR-17-2267 Kong, 2022, Network-based machine learning approach to predict immunotherapy response in cancer patients, Nat. Commun., 13, 1, 10.1038/s41467-022-31535-6 Xie, 2020, Multifactorial deep learning reveals pan-cancer genomic tumor clusters with distinct immunogenomic landscape and response to immunotherapydeep learning modeling tumor immune landscape, Clin. Cancer Res., 26, 2908, 10.1158/1078-0432.CCR-19-1744 Chowell, 2022, Improved prediction of immune checkpoint blockade efficacy across multiple cancer types, Nat. Biotechnol., 40, 499, 10.1038/s41587-021-01070-8 Sidhom, 2022, Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy, Sci. Adv., 8, 10.1126/sciadv.abq5089 Trebeschi, 2019, Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers, Ann. Oncol., 30, 998, 10.1093/annonc/mdz108 Sun, 2018, A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study, Lancet Oncol., 19, 1180, 10.1016/S1470-2045(18)30413-3 Johannet, 2021, Using machine learning algorithms to predict immunotherapy response in patients with advanced melanomapredicting immunotherapy response in advanced melanoma, Clin. Cancer Res., 27, 131, 10.1158/1078-0432.CCR-20-2415 Bera, 2022, Predicting cancer outcomes with radiomics and artificial intelligence in radiology, Nat. Rev. Clin. Oncol., 19, 132, 10.1038/s41571-021-00560-7 Echle, 2021, Deep learning in cancer pathology: a new generation of clinical biomarkers, Br. J. Cancer, 124, 686, 10.1038/s41416-020-01122-x Vanguri, 2022, Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L) 1 blockade in patients with non-small cell lung cancer, Nat. Cancer, 1 Boehm, 2022, Harnessing multimodal data integration to advance precision oncology, Nat. Rev. Cancer, 22, 114, 10.1038/s41568-021-00408-3 Kleppe, 2021, Designing deep learning studies in cancer diagnostics, Nat. Rev. Cancer, 21, 199, 10.1038/s41568-020-00327-9 Yuan, 2018, Integrated analysis of genetic ancestry and genomic alterations across cancers, Cancer Cell, 34, 549, 10.1016/j.ccell.2018.08.019 Herpers, 2022, Functional patient-derived organoid screenings identify MCLA-158 as a therapeutic EGFR$\times$ LGR5 bispecific antibody with efficacy in epithelial tumors, Nat. Cancer, 3, 418, 10.1038/s43018-022-00359-0 V. Cabannes, From Weakly Supervised Learning to Active Learning, ArXiv Preprint ArXiv:2209.11629, 2022. 〈https://doi.org/10.48550/arXiv.2209.11629〉. Novakovsky, 2022, Obtaining genetics insights from deep learning via explainable artificial intelligence, Nat. Rev. Genet., 1 Murdoch, 2019, Definitions, methods, and applications in interpretable machine learning, Proc. Natl. Acad. Sci. USA, 116, 22071, 10.1073/pnas.1900654116 Arrieta, 2020, Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI, Inf. Fusion, 58, 82, 10.1016/j.inffus.2019.12.012 Wu, 2019, DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity, Front. Immunol., 10, 10.3389/fimmu.2019.02559 Kim, 2018, Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information, Ann. Oncol., 29, 1030, 10.1093/annonc/mdy022 Lu, 2021, Deep learning-based prediction of the T cell receptor-antigen binding specificity, Nat. Mach. Intell., 3, 864, 10.1038/s42256-021-00383-2 Boehm, 2019, Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome, BMC Bioinform., 20, 7, 10.1186/s12859-018-2561-z Buckley, 2022, Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens, Brief. Bioinform., 23, 10.1093/bib/bbac141 Sarkizova, 2020, A large peptidome dataset improves HLA class I epitope prediction across most of the human population, Nat. Biotechnol., 38, 199, 10.1038/s41587-019-0322-9 Montemurro, 2021, NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data, Commun. Biol., 4, 1060, 10.1038/s42003-021-02610-3 Reynisson, 2020, NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data, Nucleic Acids Res., 48, W449, 10.1093/nar/gkaa379 Lu, 2022, dbPepNeo2.0: a database for human tumor neoantigen peptides from mass spectrometry and TCR recognition, Front. Immunol., 13