{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T06:00:18Z","timestamp":1785218418315,"version":"3.55.0"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Advances in single-cell and spatial profiling have enabled detailed characterization of heterogeneous samples, but analyzing this data remains challenging in settings involving multiple comparisons. While tools like UMAP and t-SNE are valuable for visualization, their stochastic, parameter-sensitive nature limits their use in longitudinal comparisons, treatment group analysis, and multicenter trials. Although OT was first described in the 19th century, the Sinkhorn algorithm makes it computationally tractable for high-dimensional data. By directly comparing distributions of cellular states, OT provides reproducible measures of change in high-dimensional space. This framework is amenable to integration with machine learning, including deep generative models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>OT was applied to longitudinal data from a phase I trial of tocilizumab for cavitary malignancies (NCT 06016179). The current implementation makes use of expert-guided phenotypic population definitions and their relationships. An OT-based graph representation was created for baseline and follow-up samples. The graph layout was fixed across samples and computed from phenotypic relationships. In this implementation vertex radii are proportional to their relative abundance, allowing for rapid visual assessment of population-level increases and decreases. Graph edge thickness and color encode inter-population similarity based on the optimal transport (Sinkhorn) distance between marker expression distributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This representation enabled rapid identification of populations undergoing substantial change, such as the CD8+\/IFN\u0263+ population, which decreased from 63% to 17% of CD8+ T cells following treatment. Population changes across all fluorescence parameters were encoded in the graph edit distance (GED), which captures changes in population abundance and phenotypic shifts in marker space.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Future implementations can combine this expert-guided approach with unbiased clustering algorithms to enhance scalability and cross-platform harmonization. In our recently initiated clinical trials, we will apply OT to identify key shifts in tumor, immune, and stromal cell states, summarizing patient trajectories and quantitatively supporting predictive models of treatment response. Potential applications include quantifying residual disease after chemotherapy, tracking immune activation during immunotherapy, and linking host\u2013microbiome interactions to disease progression. This approach overcomes the limitations of traditional, local-structure-optimized tools (UMAP or t-SNE) to provide a comprehensive, longitudinal view of tumor evolution and treatment response.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1856896","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:37:28Z","timestamp":1785217048000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimal transport analysis of high-dimensional flow cytometry data in immuno-oncology"],"prefix":"10.3389","volume":"17","author":[{"given":"Abida","family":"Sanjana Shemonti","sequence":"first","affiliation":[{"name":"Miftek Corporation","place":["West Lafayette, IN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin C.","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Pittsburgh School of Medicine","place":["Pittsburgh, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert D.","family":"Donnenberg","sequence":"additional","affiliation":[{"name":"Allegheny Health Network Research Institute","place":["Pittsburgh, PA, United States"]},{"name":"Department of Medicine, Drexel University College of Medicine","place":["Philadelphia, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bartek","family":"Rajwa","sequence":"additional","affiliation":[{"name":"Bindley Bioscience Center, Purdue University","place":["West Lafayette, IN, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick L.","family":"Wagner","sequence":"additional","affiliation":[{"name":"Allegheny Health Network Research Institute","place":["Pittsburgh, PA, United States"]},{"name":"Department of Medicine, Drexel University College of Medicine","place":["Philadelphia, PA, United States"]},{"name":"Allegheny Health Network Cancer Institute, Division of Surgical Oncology","place":["Pittsburgh, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David L.","family":"Bartlett","sequence":"additional","affiliation":[{"name":"Allegheny Health Network Research Institute","place":["Pittsburgh, PA, United States"]},{"name":"Department of Medicine, Drexel University College of Medicine","place":["Philadelphia, PA, United States"]},{"name":"Allegheny Health Network Cancer Institute, Division of Surgical Oncology","place":["Pittsburgh, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bosko","family":"Popov","sequence":"additional","affiliation":[{"name":"University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center","place":["Pittsburgh, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evan T.","family":"Alicuben","sequence":"additional","affiliation":[{"name":"University of Pittsburgh School of Medicine","place":["Pittsburgh, PA, United States"]},{"name":"University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center","place":["Pittsburgh, PA, United States"]},{"name":"Department of Cardiothoracic Surgery, University of Pittsburgh School of Medicine","place":["Pittsburgh, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vera S.","family":"Donnenberg","sequence":"additional","affiliation":[{"name":"University of Pittsburgh School of Medicine","place":["Pittsburgh, PA, United States"]},{"name":"University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center","place":["Pittsburgh, PA, United States"]},{"name":"Department of Cardiothoracic Surgery, University of Pittsburgh School of Medicine","place":["Pittsburgh, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2023.1157697","article-title":"A maladaptive pleural environment suppresses preexisting anti-tumor activity of pleural infiltrating T cells","volume":"14","author":"Donnenberg","year":"2023","journal-title":"Front Immunol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1404373","article-title":"A common secretomic signature across epithelial cancers metastatic to the pleura supports IL-6 axis therapeutic targeting","volume":"15","author":"Donnenberg","year":"2024","journal-title":"Front Immunol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.soi.2024.100045","article-title":"Intra-pleural and intra-peritoneal tocilizumab therapy for managing Malignant pleural effusions and ascites: The Regional Immuno-Oncology Trial (RIOT)-2 study protocol","volume":"1","author":"Park","year":"2024","journal-title":"Surg Oncol Insight"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/s0145-2126(19)30222-x","article-title":"The secretome of Malignant pleural effusions: Clues to targets of therapy","volume":"85","author":"Donnenberg","year":"2019","journal-title":"Leukemia Res"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.846235","article-title":"Localized intra-cavitary therapy to drive systemic anti-tumor immunity","volume":"13","author":"Donnenberg","year":"2022","journal-title":"Front Immunol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-023-14553-6","article-title":"Characterizing the immune environment in peritoneal carcinomatosis: Insights for novel immunotherapy strategies","volume":"31","author":"Wagner","year":"2023","journal-title":"Ann Surg Oncol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1007\/s00262-021-02886-8","article-title":"TIGIT promotes CD8+T cells exhaustion and predicts poor prognosis of colorectal cancer","volume":"70","author":"Liang","year":"2021","journal-title":"Cancer Immunol Immunother: CII"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1002\/hep.31466","article-title":"Transcriptome profiling identifies TIGIT as a marker of T-cell exhaustion in liver cancer","volume":"73","author":"Ostroumov","year":"2021","journal-title":"Hepatol (Baltimore Md)"},{"key":"B9","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1038\/s41467-023-36310-9","article-title":"Single-cell sequencing of ascites fluid illustrates heterogeneity and therapy-induced evolution during gastric cancer peritoneal metastasis","volume":"14","author":"Huang","year":"2023","journal-title":"Nat Commun"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a041314","article-title":"The emerging role of the single-cell and spatial tumor microenvironment in high-grade serous ovarian cancer","volume":"13","author":"Launonen","year":"2023","journal-title":"Cold Spring Harbor Perspect Med"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1158\/2159-8290.cd-23-1334","article-title":"Lifileucel, an autologous tumor-infiltrating lymphocyte monotherapy, in patients with advanced non-small cell lung cancer resistant to immune checkpoint inhibitors","volume":"14","author":"Schoenfeld","year":"2024","journal-title":"Cancer Discov"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.4314","article-title":"Dimensionality reduction for visualizing single-cell data using UMAP","author":"Becht","year":"2018","journal-title":"Nat Biotechnol"},{"key":"B13","doi-asserted-by":"publisher","first-page":"5416","DOI":"10.1038\/s41467-019-13056-x","article-title":"The art of using t-SNE for single-cell transcriptomics","volume":"10","author":"Kobak","year":"2019","journal-title":"Nat Commun"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1200\/jco.21.02170","article-title":"Breast cancers are immunogenic: Immunologic analyses and a phase II pilot clinical trial using mutation-reactive autologous lymphocytes","volume":"40","author":"Zacharakis","year":"2022","journal-title":"J Clin Oncol Off J Am Soc Clin Oncol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf287","article-title":"Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data","volume":"26","author":"Hackenberg","year":"2025","journal-title":"Briefings Bioinf"},{"key":"B16","doi-asserted-by":"publisher","first-page":"5037","DOI":"10.1038\/s41467-025-60434-9","article-title":"Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective","volume":"16","author":"Liu","year":"2025","journal-title":"Nat Commun"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.3390\/e24111634","article-title":"Sparse regularized optimal transport with deformed q-entropy","volume":"24","author":"Bao","year":"2022","journal-title":"Entropy (Basel Switzerland)"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1101\/2021.03.19.436159","article-title":"Optimal transport improves cell-cell similarity inference in single-cell omics data","volume":"38","author":"Huizing","year":"2022","journal-title":"Bioinf (Oxford England)"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3379571","article-title":"Scalable optimal transport methods in machine learning: A contemporary survey","volume":"10","author":"Khamis","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1014358","article-title":"CARGO: A cytometry analysis framework via regularized graph optimal-transport","author":"Shemonti","year":"2026","journal-title":"PloS Comput Biol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1038\/s41467-024-45891-y","article-title":"Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters","volume":"15","author":"Xia","year":"2024","journal-title":"Nat Commun"},{"key":"B22","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1208\/s12248-015-9857-y","article-title":"Evaluating and reporting the immunogenicity impacts for biological products\u2014a clinical pharmacology perspective","volume":"18","author":"Wang","year":"2016","journal-title":"AAPS J"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.2147\/dddt.s175113","article-title":"Characterization of clinical grade CD19 chimeric antigen receptor T cells produced using automated CliniMACS Prodigy system","volume":"12","author":"Zhang","year":"2018","journal-title":"Drug Des Dev Ther"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.labinv.2025.104190","article-title":"Rigor and reproducibility of spatial transcriptomics performed on clinically sourced human tissues","volume":"105","author":"Smith","year":"2025","journal-title":"Lab Investigation; A J Tech Methods Pathol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/s44320-023-00003-8","article-title":"Detection of patient-level distances from single cell genomics and pathomics data with optimal transport (PILOT)","volume":"20","author":"Joodaki","year":"2024","journal-title":"Mol Syst Biol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0151859","article-title":"Earth mover's distance (EMD): A true metric for comparing biomarker expression levels in cell populations","volume":"11","author":"Orlova","year":"2016","journal-title":"PloS One"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-08453-2","article-title":"Mapping cells through time and space with moscot","volume":"638","author":"Klein","year":"2025","journal-title":"Nature"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1097\/cji.0b013e31824e801f","article-title":"Simplified method of the growth of human tumor infiltrating lymphocytes in gas-permeable flasks to numbers needed for patient treatment","volume":"35","author":"Jin","year":"2012","journal-title":"J Immunother (Hagerstown Md: 1997)"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-61737-950-5_12","article-title":"Rare event detection and analysis in flow cytometry: bone marrow mesenchymal stem cells, breast cancer stem\/progenitor cells in Malignant effusions, and pericytes in disaggregated adipose tissue","volume":"699","author":"Zimmerlin","year":"2011","journal-title":"Methods Mol Biol (Clifton NJ)"},{"key":"B30","doi-asserted-by":"crossref","DOI":"10.4049\/jimmunol.115.6.1592","article-title":"Studies on T cell clonal expansion. I. Suppression of killer T cell production in vivo","volume":"115","author":"Kamat","year":"1975","journal-title":"J Immunol (Baltimore Md: 1950)"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1101\/2025.08.19.670604","article-title":"An optimal transport-based low-dimensional visualization framework for high-parameter flow cytometry","author":"Shemonti","year":"2025"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaf253","article-title":"Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction","volume":"41","author":"Chen","year":"2025","journal-title":"Bioinf (Oxford England)"},{"key":"B33","article-title":"Sinkhorn distances: Lightspeed computation of optimal transportation distances","author":"Cuturi","year":"2013"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2023.3293472","article-title":"An integrated method based on Wasserstein distance and graph for cancer subtype discovery","volume":"20","author":"Cao","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-018-0229-2","article-title":"Deep generative modeling for single-cell transcriptomics","volume":"15","author":"Lopez","year":"2018","journal-title":"Nat Methods"},{"key":"B36","doi-asserted-by":"publisher","first-page":"7419","DOI":"10.1038\/s41467-022-35094-8","article-title":"A unified computational framework for single-cell data integration with optimal transport","volume":"13","author":"Cao","year":"2022","journal-title":"Nat Commun"},{"key":"B37","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s40364-020-00228-x","article-title":"Roles of IFN-\u03b3 in tumor progression and regression: a review","volume":"8","author":"Jorgovanovic","year":"2020","journal-title":"biomark Res"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1074\/jbc.m114.572420","article-title":"T-cell immunoglobulin and ITIM domain (TIGIT) receptor\/poliovirus receptor (PVR) ligand engagement suppresses interferon-\u03b3 production of natural killer cells via \u03b2-arrestin 2-mediated negative signaling","volume":"289","author":"Li","year":"2014","journal-title":"J Biol Chem"},{"key":"B39","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/j.ccell.2021.04.014","article-title":"Conserved pan-cancer microenvironment subtypes predict response to immunotherapy","volume":"39","author":"Bagaev","year":"2021","journal-title":"Cancer Cell"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1158\/2159-8290.cd-21-0407","article-title":"A phase I trial of regional mesothelin-targeted CAR T-cell therapy in patients with Malignant pleural disease, in combination with the anti\u2013PD-1 agent pembrolizumab","volume":"11","author":"Adusumilli","year":"2021","journal-title":"Cancer Discov"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-020-00449-x","article-title":"Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy","volume":"18","author":"Gohil","year":"2021","journal-title":"Nat Rev Clin Oncol"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.ccr-22-2103","article-title":"Combination nivolumab, CD137 agonism, and adoptive cell therapy with tumor-infiltrating lymphocytes for patients with metastatic melanoma","volume":"28","author":"Hall","year":"2022","journal-title":"Clin Cancer Research: Off J Am Assoc For Cancer Res"},{"key":"B43","doi-asserted-by":"publisher","first-page":"40495","DOI":"10.1080\/0284186X.2023.2289417","article-title":"NIVO-TIL: combination anti-PD-1 therapy and adoptive T-cell transfer in untreated metastatic melanoma: an exploratory open-label phase I trial","volume":"63","author":"L\u2019Orphelin","year":"2024","journal-title":"Acta Oncol"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1158\/2326-6066.cir-18-0725","article-title":"Blockade of TIGIT\/CD155 signaling reverses T-cell exhaustion and enhances antitumor capability in head and neck squamous cell carcinoma","volume":"7","author":"Wu","year":"2019","journal-title":"Cancer Immunol Res"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.4049\/jimmunol.1003081","article-title":"Cutting edge: TIGIT has T cell-intrinsic inhibitory functions","volume":"186","author":"Joller","year":"2011","journal-title":"J Immunol (Baltimore Md: 1950)"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-01969-x","article-title":"Learning single-cell perturbation responses using neural optimal transport","volume":"20","author":"Bunne","year":"2023","journal-title":"Nat Methods"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adh9879","article-title":"Engineered IL-7 synergizes with IL-12 immunotherapy to prevent T cell exhaustion and promote memory without exacerbating toxicity","volume":"9","author":"Kang","year":"2023","journal-title":"Sci Adv"},{"key":"B48","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1111\/j.1365-2567.2007.02728.x","article-title":"Comparison of MMP-2 and MMP-9 secretion from T helper 0, 1 and 2 lymphocytes alone and in coculture with macrophages","volume":"124","author":"Oviedo-Orta","year":"2008","journal-title":"Immunology"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1038\/s41416-024-02731-6","article-title":"Tumour microenvironment characterisation to stratify patients for hyperthermic intraperitoneal chemotherapy in high-grade serous ovarian cancer (OVHIPEC-1)","volume":"131","author":"Aronson","year":"2024","journal-title":"Br J Cancer"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.1995.501","article-title":"Intrapleural administration of interleukin 2 in pleural mesothelioma: a phase I-II study","volume":"72","author":"Goey","year":"1995","journal-title":"Br J Cancer"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.4049\/jimmunol.2001077","article-title":"Il-7 and il-15 levels reflect the degree of t cell depletion during lymphopenia and are associated with an expansion of effector memory t cells after pediatric hematopoietic stem cell transplantation","volume":"206","author":"Kielsen","year":"2021","journal-title":"J Immunol (Baltimore Md: 1950)"},{"key":"B52","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ymeth.2015.03.012","article-title":"Coping with artifact in the analysis of flow cytometric data","volume":"82","author":"Donnenberg","year":"2015","journal-title":"Methods (San Diego Calif)"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011288","article-title":"The specious art of single-cell genomics","volume":"19","author":"Chari","year":"2023","journal-title":"PloS Comput Biol"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2018.03.012","article-title":"T cell dysfunction in cancer","volume":"33","author":"Thommen","year":"2018","journal-title":"Cancer Cell"}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1856896\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:37:29Z","timestamp":1785217049000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1856896\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":54,"alternative-id":["10.3389\/fimmu.2026.1856896"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2026.1856896","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,28]]},"article-number":"1856896"}}