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This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.<\/jats:p>","DOI":"10.1093\/bib\/bbae421","type":"journal-article","created":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:33:41Z","timestamp":1724459621000},"source":"Crossref","is-referenced-by-count":32,"title":["Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8388-008X","authenticated-orcid":false,"given":"Haoyang","family":"Mi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"}]},{"given":"Shamilene","family":"Sivagnanam","sequence":"additional","affiliation":[{"name":"The Knight Cancer Institute, Oregon Health and Science University , Portland, OR 97201, United States"},{"name":"Department of Cell , Development and Cancer Biology, , Portland, OR 97201, United States"},{"name":"Oregon Health and Science University , Development and Cancer Biology, , Portland, OR 97201, United States"}]},{"given":"Won Jin","family":"Ho","sequence":"additional","affiliation":[{"name":"Department of Oncology, Johns Hopkins University School of Medicine , MD 21205, United States"},{"name":"Convergence Institute, Johns Hopkins University , Baltimore, MD 21205, United States"}]},{"given":"Shuming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"}]},{"given":"Daniel","family":"Bergman","sequence":"additional","affiliation":[{"name":"Department of Oncology, Johns Hopkins University School of Medicine , MD 21205, United States"},{"name":"Convergence Institute, Johns Hopkins University , Baltimore, MD 21205, United States"}]},{"given":"Atul","family":"Deshpande","sequence":"additional","affiliation":[{"name":"Department of Oncology, Johns Hopkins University School of Medicine , MD 21205, United States"},{"name":"Convergence Institute, Johns Hopkins University , Baltimore, MD 21205, United States"},{"name":"Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"}]},{"given":"Alexander S","family":"Baras","sequence":"additional","affiliation":[{"name":"Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"},{"name":"Department of Pathology, Johns Hopkins University School of Medicine , MD 21205, United States"},{"name":"The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"}]},{"given":"Elizabeth M","family":"Jaffee","sequence":"additional","affiliation":[{"name":"Department of Oncology, Johns Hopkins University School of Medicine , MD 21205, United States"},{"name":"Convergence Institute, Johns Hopkins University , Baltimore, MD 21205, United States"},{"name":"Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"}]},{"given":"Lisa M","family":"Coussens","sequence":"additional","affiliation":[{"name":"The Knight Cancer Institute, Oregon Health and Science University , Portland, OR 97201, United States"},{"name":"Department of Cell , Development and Cancer Biology, , Portland, OR 97201, United States"},{"name":"Oregon Health and Science University , Development and Cancer Biology, , Portland, OR 97201, United States"},{"name":"Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University , Portland, OR 97201, United States"}]},{"given":"Elana J","family":"Fertig","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"},{"name":"Department of Oncology, Johns Hopkins University School of Medicine , MD 21205, United States"},{"name":"Convergence Institute, Johns Hopkins University , Baltimore, MD 21205, United States"},{"name":"Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"},{"name":"Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering , Baltimore, MD 21218, United States"}]},{"given":"Aleksander S","family":"Popel","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine , Baltimore, MD 21205, United States"},{"name":"Department of Oncology, Johns Hopkins University School of Medicine , MD 21205, United 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archives","volume":"9","author":"Pantanowitz","year":"2018","journal-title":"J Pathol Inform"},{"key":"2024082322284442900_ref4","first-page":"23","article-title":"Whole slide imaging in pathology: advantages, limitations, and emerging perspectives","volume":"7","author":"Farahani","year":"2015","journal-title":"Pathol Lab Med Int"},{"key":"2024082322284442900_ref5","doi-asserted-by":"crossref","DOI":"10.1126\/sciimmunol.aaf6925","article-title":"In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide","volume":"1","author":"Remark","year":"2016","journal-title":"Sci Immunol"},{"key":"2024082322284442900_ref6","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.cell.2018.07.010","article-title":"Deep profiling of mouse splenic architecture with CODEX multiplexed 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dermatology: advancing the learning paradigm","volume":"245","author":"Salvi","year":"2024","journal-title":"Expert Syst Appl"},{"key":"2024082322284442900_ref44","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/978-3-030-33843-5_15","volume-title":"Machine Learning for Medical Image Reconstruction","author":"Cai","year":"2019"},{"key":"2024082322284442900_ref45","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1109\/TBME.2014.2303294","article-title":"A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution","volume":"61","author":"Khan","year":"2014","journal-title":"IEEE Trans Biomed Eng"},{"key":"2024082322284442900_ref46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.mie.2019.05.039","article-title":"High-dimensional multiplexed immunohistochemical characterization of immune contexture in human cancers","volume":"635","author":"Banik","year":"2020","journal-title":"Methods Enzymol"},{"key":"2024082322284442900_ref47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-022-03368-y","article-title":"A framework for multiplex imaging optimization and reproducible analysis","volume":"5","author":"Eng","year":"2022","journal-title":"Commun Biol"},{"key":"2024082322284442900_ref48","doi-asserted-by":"crossref","first-page":"4613","DOI":"10.1093\/bioinformatics\/btac544","article-title":"Stitching and registering highly multiplexed whole-slide images of tissues and tumors using ASHLAR","volume":"38","author":"Muhlich","year":"2022","journal-title":"Bioinformatics"},{"key":"2024082322284442900_ref49","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105799","article-title":"DeepHistReg: unsupervised deep learning registration framework for differently stained histology samples","volume":"198","author":"Wodzinski","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"2024082322284442900_ref50","doi-asserted-by":"crossref","first-page":"1892","DOI":"10.3390\/app11041892","article-title":"Accurate and robust alignment of differently stained histologic images based on greedy diffeomorphic registration","volume":"11","author":"Venet","year":"2021","journal-title":"Appl Sci (Basel)"},{"key":"2024082322284442900_ref51","doi-asserted-by":"crossref","first-page":"279","DOI":"10.4103\/jomfp.JOMFP_125_15","article-title":"A review of artifacts in histopathology","volume":"22","author":"Taqi","year":"2018","journal-title":"J Oral Maxillofac Pathol"},{"key":"2024082322284442900_ref52","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/42.796284","article-title":"Nonrigid registration using free-form deformations: application to breast MR images","volume":"18","author":"Rueckert","year":"1999","journal-title":"IEEE Trans Med Imaging"},{"key":"2024082322284442900_ref53","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1007\/978-3-031-43987-2_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023","author":"He","year":"2023"},{"key":"2024082322284442900_ref54","doi-asserted-by":"crossref","first-page":"3487","DOI":"10.1109\/TMI.2023.3288940","article-title":"Artifact detection and restoration in histology images with stain-style and structural preservation","volume":"42","author":"Ke","year":"2023","journal-title":"IEEE Trans Med Imaging"},{"key":"2024082322284442900_ref55","doi-asserted-by":"crossref","first-page":"4305","DOI":"10.1007\/s00371-022-02592-1","article-title":"Multi-scale self-attention generative adversarial network for pathology image restoration","volume":"39","author":"Liang","year":"2023","journal-title":"Vis Comput"},{"key":"2024082322284442900_ref56","article-title":"Quality control for single cell analysis of high-plex tissue profiles using CyLinter","author":"Baker","year":"2024","journal-title":"bioRxiv"},{"key":"2024082322284442900_ref57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1200\/CCI.18.00157","article-title":"HistoQC: An open-source quality control tool for digital pathology slides","volume":"3","author":"Janowczyk","year":"2019","journal-title":"JCO Clin Cancer Inform"},{"key":"2024082322284442900_ref58","doi-asserted-by":"crossref","first-page":"350","DOI":"10.3389\/fmed.2018.00350","article-title":"Tissue intrinsic fluorescence spectra-based digital pathology of liver fibrosis by marker-controlled segmentation","volume":"5","author":"Saitou","year":"2018","journal-title":"Front Med (Lausanne)"},{"key":"2024082322284442900_ref59","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1111\/his.12935","article-title":"What to do with high autofluorescence background in pancreatic tissues \u2013 an efficient Sudan black B quenching method for specific immunofluorescence labelling","volume":"69","author":"Erben","year":"2016","journal-title":"Histopathology"},{"key":"2024082322284442900_ref60","article-title":"Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes","volume":"7","author":"Lin","journal-title":"Elife"},{"key":"2024082322284442900_ref61","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.ajur.2018.11.006","article-title":"If this is true, what does it imply? 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Ed"},{"key":"2024082322284442900_ref85","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1158\/2159-8290.CD-20-0841","article-title":"Leukocyte heterogeneity in pancreatic ductal adenocarcinoma: phenotypic and spatial features associated with clinical outcome","volume":"11","author":"Liudahl","year":"2021","journal-title":"Cancer Discov"},{"key":"2024082322284442900_ref86","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.celrep.2017.03.037","article-title":"Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis","volume":"19","author":"Tsujikawa","year":"2017","journal-title":"Cell Rep"},{"key":"2024082322284442900_ref87","doi-asserted-by":"crossref","first-page":"eabi5072","DOI":"10.1126\/sciimmunol.abi5072","article-title":"Spatially mapping the immune landscape of melanoma using imaging mass cytometry","volume":"7","author":"Moldoveanu","year":"2022","journal-title":"Sci 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Res"},{"key":"2024082322284442900_ref104","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1093\/annonc\/mdv560","article-title":"Strategies for clinical implementation of TNM-Immunoscore in resected nonsmall-cell lung cancer","volume":"27","author":"Donnem","year":"2016","journal-title":"Ann Oncol"},{"key":"2024082322284442900_ref105","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1186\/s12967-016-1029-z","article-title":"Immunoscore and Immunoprofiling in cancer: an update from the melanoma and immunotherapy bridge 2015","volume":"14","author":"Galon","year":"2016","journal-title":"J Transl Med"},{"key":"2024082322284442900_ref106","doi-asserted-by":"crossref","DOI":"10.1200\/jco.2014.32.15_suppl.e20020","article-title":"Immunoscore as new possible approach for the classification of melanoma","volume":"32","author":"Capone","year":"2014","journal-title":"JCO"},{"key":"2024082322284442900_ref107","first-page":"1","article-title":"Spatially variant immune 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Commun"},{"key":"2024082322284442900_ref99","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1158\/1078-0432.CCR-13-2830","article-title":"Prognostic and predictive values of the Immunoscore in patients with rectal cancer","volume":"20","author":"Anitei","year":"2014","journal-title":"Clin Cancer Res"},{"key":"2024082322284442900_ref119","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.tibtech.2021.11.006","article-title":"Quantification of tumor heterogeneity: from data acquisition to metric generation","volume":"40","author":"Kashyap","year":"2022","journal-title":"Trends Biotechnol"},{"key":"2024082322284442900_ref120","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.ccell.2020.03.007","article-title":"Intratumor heterogeneity: the Rosetta stone of therapy resistance","volume":"37","author":"Marusyk","year":"2020","journal-title":"Cancer 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Ecol"},{"key":"2024082322284442900_ref127","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10651-017-0383-1","article-title":"A new approach to spatial entropy measures","volume":"25","author":"Altieri","year":"2018","journal-title":"Environ Ecol Stat"},{"key":"2024082322284442900_ref128","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/11556114_14","volume-title":"Spatial Information Theory","author":"Claramunt","year":"2005"},{"key":"2024082322284442900_ref129","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0188878","article-title":"Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures","volume":"12","author":"Graf","year":"2017","journal-title":"PloS One"},{"key":"2024082322284442900_ref130","doi-asserted-by":"crossref","first-page":"3151","DOI":"10.1093\/bioinformatics\/btac303","article-title":"ATHENA: analysis of tumor heterogeneity from spatial omics 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(Basel)"},{"key":"2024082322284442900_ref134","doi-asserted-by":"crossref","DOI":"10.1016\/j.modpat.2023.100326","article-title":"Pathomic features reveal immune and molecular evolution from lung preneoplasia to invasive adenocarcinoma","volume":"36","author":"Chen","year":"2023","journal-title":"Mod Pathol"},{"key":"2024082322284442900_ref135","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1093\/bioinformatics\/btaa965","article-title":"Quantification of spatial tumor heterogeneity in immunohistochemistry staining images","volume":"37","author":"Chervoneva","year":"2021","journal-title":"Bioinformatics"},{"key":"2024082322284442900_ref136","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.964716","article-title":"Spatial heterogeneity of cancer associated protein expression in immunohistochemically stained images as an improved prognostic biomarker","volume":"12","author":"Failmezger","year":"2022","journal-title":"Front Oncol"},{"key":"2024082322284442900_ref137","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1001789","article-title":"Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis","volume":"12","author":"Schwarz","year":"2015","journal-title":"PLoS Med"},{"key":"2024082322284442900_ref139","doi-asserted-by":"crossref","first-page":"i140","DOI":"10.1093\/bioinformatics\/btad245","article-title":"Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis","volume":"39","author":"Dayao","year":"2023","journal-title":"Bioinformatics"},{"key":"2024082322284442900_ref140","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1343","article-title":"Density-based clustering","volume":"10","author":"Campello","year":"2020","journal-title":"WIREs Data Mining Knowl Discov"},{"key":"2024082322284442900_ref141","first-page":"226","volume-title":"Proceedings of the Second International Conference on Knowledge Discovery and 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(Basel)"},{"key":"2024082322284442900_ref145","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/labinvest.2014.155","article-title":"Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology","volume":"95","author":"Heindl","year":"2015","journal-title":"Lab Invest"},{"key":"2024082322284442900_ref146","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10549-020-05752-w","article-title":"Prognostic potential of automated Ki67 evaluation in breast cancer: different hot spot definitions versus true global score","volume":"183","author":"Robertson","year":"2020","journal-title":"Breast Cancer Res Treat"},{"key":"2024082322284442900_ref147","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing messages between data 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patients","volume":"10","author":"Wang","year":"2021","journal-title":"Onco Targets Ther"},{"key":"2024082322284442900_ref157","doi-asserted-by":"crossref","first-page":"4675","DOI":"10.1182\/bloodadvances.2022007493","article-title":"Single-cell spatial analysis of tumor immune architecture in diffuse large B-cell lymphoma","volume":"6","author":"Colombo","year":"2022","journal-title":"Blood Adv"},{"key":"2024082322284442900_ref158","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.celrep.2018.03.086","article-title":"Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images","volume":"23","author":"Saltz","year":"2018","journal-title":"Cell Rep"},{"key":"2024082322284442900_ref159","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1158\/0008-5472.CAN-19-2268","article-title":"Topological tumor graphs: a graph-based spatial model to infer stromal recruitment for immunosuppression in melanoma 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Methods"},{"key":"2024082322284442900_ref138","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1038\/s41592-022-01650-9","article-title":"CODA: quantitative 3D reconstruction of large tissues at cellular resolution","volume":"19","author":"Kiemen","year":"2022","journal-title":"Nat Methods"},{"key":"2024082322284442900_ref163","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1086\/282436","article-title":"Measurement of \u2018overlap\u2019 in comparative ecological studies","volume":"100","author":"Horn","year":"1966","journal-title":"Am Nat"},{"key":"2024082322284442900_ref164","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1111\/j.2517-6161.1977.tb01615.x","article-title":"Modelling spatial patterns","volume":"39","author":"Ripley","year":"1977","journal-title":"J R Stat Soc B Methodol"},{"key":"2024082322284442900_ref165","doi-asserted-by":"crossref","first-page":"3627","DOI":"10.1091\/mbc.e16-07-0478","article-title":"Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data","volume":"27","author":"Pageon","year":"2016","journal-title":"MBoC"},{"key":"2024082322284442900_ref166","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1038\/s41467-023-37822-0","article-title":"Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments","volume":"14","author":"Feng","year":"2023","journal-title":"Nat Commun"},{"key":"2024082322284442900_ref167","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1038\/s41416-022-01822-6","article-title":"Prognostic significance of spatial and density analysis of T lymphocytes in colorectal cancer","volume":"127","author":"Elomaa","year":"2022","journal-title":"Br J Cancer"},{"key":"2024082322284442900_ref168","doi-asserted-by":"crossref","first-page":"4326","DOI":"10.1158\/1078-0432.CCR-20-0071","article-title":"Prognostic significance of immune cell populations identified by machine learning in colorectal cancer using routine hematoxylin and eosin\u2013stained sections","volume":"26","author":"V\u00e4yrynen","year":"2020","journal-title":"Clin Cancer Res"},{"key":"2024082322284442900_ref169","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms15095","article-title":"Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer","volume":"8","author":"Carstens","year":"2017","journal-title":"Nat Commun"},{"key":"2024082322284442900_ref170","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0245415","article-title":"Spatial clustering of CD68+ tumor associated macrophages with tumor cells is associated with worse overall survival in metastatic clear cell renal cell carcinoma","volume":"16","author":"Chakiryan","year":"2021","journal-title":"PloS One"},{"key":"2024082322284442900_ref171","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1158\/1078-0432.CCR-20-3141","article-title":"Composition, spatial 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Cancer"},{"key":"2024082322284442900_ref174","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41523-022-00419-9","article-title":"Spatial interplay of tissue hypoxia and T-cell regulation in ductal carcinoma in situ","volume":"8","author":"Sobhani","year":"2022","journal-title":"NPJ Breast Cancer"},{"key":"2024082322284442900_ref175","doi-asserted-by":"crossref","first-page":"1137561","DOI":"10.3389\/fimmu.2023.1137561","article-title":"Prognostic value of various immune cells and Immunoscore in triple-negative breast cancer","volume":"14","author":"Ren","year":"2023","journal-title":"Front Immunol"},{"key":"2024082322284442900_ref176","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.cels.2023.03.004","article-title":"Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces","volume":"14","author":"Deshpande","year":"2023","journal-title":"Cell Syst"},{"key":"2024082322284442900_ref177","doi-asserted-by":"crossref","first-page":"3796","DOI":"10.1093\/bioinformatics\/btab569","article-title":"Assessing heterogeneity in spatial data using the HTA index with applications to spatial transcriptomics and imaging","volume":"37","author":"Levy-Jurgenson","year":"2021","journal-title":"Bioinformatics"},{"key":"2024082322284442900_ref178","doi-asserted-by":"crossref","first-page":"4734","DOI":"10.1016\/j.cell.2021.08.003","article-title":"Spatially organized multicellular immune hubs in human colorectal cancer","volume":"184","author":"Pelka","year":"2021","journal-title":"Cell"},{"key":"2024082322284442900_ref179","article-title":"Spatial architecture of high-grade glioma reveals tumor heterogeneity within distinct domains","volume":"5","author":"Moffet","year":"2023","journal-title":"Neuro-Oncol Adv"},{"key":"2024082322284442900_ref180","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-26974-6","article-title":"Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma","volume":"12","author":"Phillips","year":"2021","journal-title":"Nat Commun"},{"key":"2024082322284442900_ref181","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1038\/s41586-019-1876-x","article-title":"The single-cell pathology landscape of breast cancer","volume":"578","author":"Jackson","year":"2020","journal-title":"Nature"},{"key":"2024082322284442900_ref182","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2022.892250","article-title":"Multi-scale spatial analysis of the tumor microenvironment reveals features of Cabozantinib and nivolumab efficacy in hepatocellular carcinoma","volume":"13","author":"Mi","year":"2022","journal-title":"Front Immunol"},{"key":"2024082322284442900_ref183","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41586-022-05680-3","article-title":"Single-cell spatial immune landscapes of primary and metastatic brain tumours","volume":"614","author":"Karimi","year":"2023","journal-title":"Nature"},{"key":"2024082322284442900_ref184","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.cels.2021.09.012","article-title":"Tissue schematics map the specialization of immune tissue motifs and their appropriation by tumors","volume":"13","author":"Bhate","year":"2022","journal-title":"Cell Syst"},{"key":"2024082322284442900_ref185","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1089\/cmb.2019.0340","article-title":"Modeling multiplexed images with spatial-LDA reveals novel tissue microenvironments","volume":"27","author":"Chen","year":"2020","journal-title":"J Comput Biol"},{"key":"2024082322284442900_ref186","doi-asserted-by":"crossref","first-page":"7203","DOI":"10.1038\/s41467-022-34879-1","article-title":"Spatially aware dimension reduction for spatial transcriptomics","volume":"13","author":"Shang","year":"2022","journal-title":"Nat Commun"},{"key":"2024082322284442900_ref187","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1038\/s41467-022-29439-6","article-title":"Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder","volume":"13","author":"Dong","year":"2022","journal-title":"Nat Commun"},{"key":"2024082322284442900_ref188","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1111\/biom.13727","article-title":"A Bayesian multivariate mixture model for high throughput spatial 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Oncol"},{"key":"2024082322284442900_ref192","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1158\/2767-9764.CRC-21-0146","article-title":"Resolving the heterogeneous tumor-centric cellular neighborhood through multiplexed, spatial paracrine interactions in the setting of immune checkpoint blockade","volume":"2","author":"Maus","year":"2022","journal-title":"Cancer Res Commun"},{"key":"2024082322284442900_ref193","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1038\/ng.3624","article-title":"Tensor decomposition for multiple-tissue gene expression experiments","volume":"48","author":"Hore","year":"2016","journal-title":"Nat Genet"},{"key":"2024082322284442900_ref194","doi-asserted-by":"crossref","DOI":"10.2202\/1544-6115.1470","article-title":"Extensions of sparse canonical correlation analysis with applications to genomic data","volume":"8","author":"Witten","year":"2009","journal-title":"Stat Appl Genet Mol Biol"},{"key":"2024082322284442900_ref195","article-title":"graph2vec: learning distributed representations of graphs","author":"Narayanan","year":"2017"},{"key":"2024082322284442900_ref196","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41551-020-00681-x","article-title":"Harnessing non-destructive 3D pathology","volume":"5","author":"Liu","year":"2021","journal-title":"Nat Biomed Eng"},{"key":"2024082322284442900_ref197","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1126\/science.1191776","article-title":"Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain","volume":"330","author":"Li","year":"2010","journal-title":"Science"},{"key":"2024082322284442900_ref198","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41551-017-0084","article-title":"Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens","volume":"1","author":"Glaser","year":"2017","journal-title":"Nat 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Rep"},{"key":"2024082322284442900_ref203","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1038\/nature12107","article-title":"Structural and molecular interrogation of intact biological systems","volume":"497","author":"Chung","year":"2013","journal-title":"Nature"},{"key":"2024082322284442900_ref204","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1038\/nprot.2015.111","article-title":"Efficient processing and analysis of large-scale light-sheet microscopy data","volume":"10","author":"Amat","year":"2015","journal-title":"Nat Protoc"},{"key":"2024082322284442900_ref205","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s00345-018-2202-1","article-title":"Histopathology: ditch the slides, because digital and 3D are on show","volume":"36","author":"Jansen","year":"2018","journal-title":"World J Urol"},{"key":"2024082322284442900_ref201","article-title":"Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial","volume":"84","author":"Zhang","journal-title":"Cancer Res"},{"key":"2024082322284442900_ref206","doi-asserted-by":"crossref","first-page":"4875","DOI":"10.1158\/0008-5472.CAN-12-2217","article-title":"Intratumor heterogeneity: evolution through space and time","volume":"72","author":"Swanton","year":"2012","journal-title":"Cancer Res"},{"key":"2024082322284442900_ref207","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-022-00636-3","article-title":"Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy","volume":"5","author":"Hutchinson","year":"2022","journal-title":"NPJ Digit Med"},{"key":"2024082322284442900_ref208","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1038\/s41586-023-06498-3","article-title":"Spatial predictors of immunotherapy response in triple-negative breast cancer","volume":"621","author":"Wang","year":"2023","journal-title":"Nature"},{"key":"2024082322284442900_ref209","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1158\/1078-0432.CCR-23-2250","article-title":"Phase II study of Eribulin plus pembrolizumab in metastatic soft tissue sarcomas: clinical outcomes and biological correlates","volume":"30","author":"Haddox","year":"2024","journal-title":"Clin Cancer Res"},{"key":"2024082322284442900_ref210","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1200\/JCO.23.00580","article-title":"Genomic and immunophenotypic landscape of acquired resistance to PD-(L)1 blockade in non\u2013small-cell lung cancer","volume":"42","author":"Ricciuti","year":"2024","journal-title":"JCO"},{"key":"2024082322284442900_ref211","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.3390\/cancers13153751","article-title":"A spatial quantitative systems pharmacology platform spQSP-IO for simulations of tumor\u2014immune interactions and effects of checkpoint inhibitor immunotherapy","volume":"13","author":"Gong","year":"2021","journal-title":"Cancer"},{"key":"2024082322284442900_ref212","doi-asserted-by":"crossref","first-page":"2750","DOI":"10.3390\/cancers15102750","article-title":"Quantifying Intratumoral heterogeneity and Immunoarchitecture generated In-silico by a spatial quantitative systems pharmacology model","volume":"15","author":"Nikfar","year":"2023","journal-title":"Cancer"},{"key":"2024082322284442900_ref213","doi-asserted-by":"crossref","DOI":"10.1101\/2023.09.17.557982","article-title":"Digitize your biology! Modeling multicellular systems through interpretable cell behavior","author":"Johnson","year":"2023"},{"key":"2024082322284442900_ref214","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1010254","article-title":"Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model","volume":"18","author":"Ruiz-Martinez","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2024082322284442900_ref215","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009094","article-title":"Topological data analysis distinguishes parameter regimes in the Anderson-chaplain model of angiogenesis","volume":"17","author":"Nardini","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2024082322284442900_ref216","doi-asserted-by":"crossref","DOI":"10.1016\/j.mbs.2024.109158","article-title":"Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling","volume":"370","author":"Nguyen","year":"2024","journal-title":"Math Biosci"},{"key":"2024082322284442900_ref217","doi-asserted-by":"crossref","DOI":"10.3389\/fsysb.2022.928387","article-title":"Drug development digital twins for drug discovery, testing and repurposing: a schema for requirements and development","volume":"2","author":"An","year":"2022","journal-title":"Front Syst Biol"},{"key":"2024082322284442900_ref218","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13073-019-0701-3","article-title":"Digital twins to personalize medicine","volume":"12","author":"Bj\u00f6rnsson","year":"2019","journal-title":"Genome Med"},{"key":"2024082322284442900_ref219","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adg0289","article-title":"A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition","volume":"9","author":"Arulraj","year":"2023","journal-title":"Sci Adv"},{"key":"2024082322284442900_ref220","doi-asserted-by":"crossref","first-page":"1163432","DOI":"10.3389\/fphar.2023.1163432","article-title":"Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager","volume":"14","author":"Anbari","year":"2023","journal-title":"Front Pharmacol"},{"key":"2024082322284442900_ref221","article-title":"Fast graph representation learning with PyTorch geometric","author":"Fey","year":"2019","journal-title":"ArXiv"},{"key":"2024082322284442900_ref222","article-title":"Deep graph library: towards efficient and scalable deep learning on graphs","author":"Wang","year":"2019","journal-title":"ICLR Workshop on Representation Learning on Graphs and Manifolds"},{"key":"2024082322284442900_ref223","first-page":"1025","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Hamilton","year":"2017"},{"key":"2024082322284442900_ref224","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102183","article-title":"Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images","volume":"73","author":"Zhang","year":"2021","journal-title":"Med Image Anal"},{"key":"2024082322284442900_ref225","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1038\/s41592-022-01651-8","article-title":"Annotation of spatially resolved single-cell data with STELLAR","volume":"19","author":"Brbi\u0107","year":"2022","journal-title":"Nat Methods"},{"key":"2024082322284442900_ref226","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1109\/CVPRW50498.2020.00502","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","author":"Adnan","year":"2020"},{"key":"2024082322284442900_ref227","doi-asserted-by":"crossref","DOI":"10.3389\/fphy.2020.00203","article-title":"Classification of cancer types using graph convolutional neural networks","volume":"8","author":"Ramirez","year":"2020","journal-title":"Front Phys"},{"key":"2024082322284442900_ref228","doi-asserted-by":"crossref","first-page":"14938","DOI":"10.1038\/s41598-023-41407-8","article-title":"Enhanced brain tumor classification using graph convolutional neural network architecture","volume":"13","author":"Ravinder","year":"2023","journal-title":"Sci Rep"},{"key":"2024082322284442900_ref229","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102936","article-title":"Multi-cell type and multi-level graph aggregation network for cancer grading in pathology images","volume":"90","author":"Abbas","year":"2023","journal-title":"Med Image Anal"},{"key":"2024082322284442900_ref230","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1038\/s41551-022-00951-w","article-title":"Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens","volume":"6","author":"Wu","year":"2022","journal-title":"Nat Biomed Eng"},{"key":"2024082322284442900_ref231","first-page":"1","article-title":"Cell graph neural networks enable the precise prediction of patient survival in gastric cancer","volume":"6","author":"Wang","year":"2022","journal-title":"NPJ Precis Oncol"},{"key":"2024082322284442900_ref232","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.80547","article-title":"Early stage NSCLS patients\u2019 prognostic prediction with multi-information using transformer and graph neural network model","volume":"11","author":"Lian","year":"2022","journal-title":"Elife"},{"key":"2024082322284442900_ref233","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer 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Microanal"},{"key":"2024082322284442900_ref284","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","article-title":"Cellpose: A generalist algorithm for cellular segmentation","volume":"18","author":"Stringer","year":"2021","journal-title":"Nat Methods"},{"key":"2024082322284442900_ref285","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1038\/s41467-023-37822-0","article-title":"Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments","volume":"14","author":"Feng","year":"2023","journal-title":"Nat Commun"},{"key":"2024082322284442900_ref286","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v012.i06","article-title":"Spatstat: An R package for analyzing spatial point patterns","volume":"12","author":"Baddeley","year":"2005","journal-title":"J Stat 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