{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:00:59Z","timestamp":1770840059649,"version":"3.50.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:00:00Z","timestamp":1725321600000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP20am0101108"],"award-info":[{"award-number":["JP20am0101108"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning\u2013based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well. However, when using the conventional \u201cclonotype cluster\u201d representation of AIRs, individuals within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This study aimed to address the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmune disease, and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean AUC of 0.893 when applied to new individuals, outperforming clonotype cluster\u2013based classifiers (AUC 0.714) and the best-performing published classifier (AUC 0.777). Surprisingly, for cancer patients, we observed that \u201chealthy-biased\u201d AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores &amp;gt;75), suggesting an overlooked reservoir of cancer-targeting immune cells that could be identified by PCOs.<\/jats:p>","DOI":"10.1093\/bib\/bbae431","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T14:14:53Z","timestamp":1725372893000},"source":"Crossref","is-referenced-by-count":3,"title":["Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors"],"prefix":"10.1093","volume":"25","author":[{"given":"Zichang","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Systems Immunology , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hendra S","family":"Ismanto","sequence":"additional","affiliation":[{"name":"Department of Systems Immunology , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Department of Genome Informatics , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dianita S","family":"Saputri","sequence":"additional","affiliation":[{"name":"Department of Systems Immunology , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Department of Genome Informatics , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soichiro","family":"Haruna","sequence":"additional","affiliation":[{"name":"Department of Genome Informatics , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4704-7072","authenticated-orcid":false,"given":"Guanqun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of information Science , Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Wilamowski","sequence":"additional","affiliation":[{"name":"Department of Genome Informatics , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunsuke","family":"Teraguchi","sequence":"additional","affiliation":[{"name":"Department of Genome Informatics , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Faculty of Data Science , Shiga University 1-1-1 Banba, Hikone, Shiga 522-8522 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayan","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Cogent Labs , 3-2-1 Roppongi, Minato-ku, Tokyo 106-6122 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songling","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Systems Immunology , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Department of Genome Informatics , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4078-0817","authenticated-orcid":false,"given":"Daron M","family":"Standley","sequence":"additional","affiliation":[{"name":"Department of Systems Immunology , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Immunology Frontier Research Institute (IFReC), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Department of Genome Informatics , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"},{"name":"Osaka University , Research Institute for Microbial Diseases (RIMD), , 3-1 Yamadaoka, Suita 565-0871 , Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"2024090314142398500_ref1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s12943-022-01543-7","article-title":"Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments","volume":"21","author":"Lone","year":"2022","journal-title":"Mol Cancer"},{"key":"2024090314142398500_ref2","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1039\/C7LC00955K","article-title":"Machine learning to detect signatures of disease in liquid biopsies - a user's guide","volume":"18","author":"Ko","year":"2018","journal-title":"Lab Chip"},{"key":"2024090314142398500_ref3","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/nature22383","article-title":"Quantifiable predictive features define epitope-specific T cell receptor repertoires","volume":"547","author":"Dash","year":"2017","journal-title":"Nature"},{"key":"2024090314142398500_ref4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1038\/nature22976","article-title":"Identifying specificity groups in the T cell receptor repertoire","volume":"547","author":"Glanville","year":"2017","journal-title":"Nature"},{"key":"2024090314142398500_ref5","doi-asserted-by":"crossref","first-page":"14275","DOI":"10.1038\/s41598-021-93608-8","article-title":"Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires","volume":"11","author":"Sidhom","year":"2021","journal-title":"Sci Rep"},{"key":"2024090314142398500_ref6","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1039\/C9ME00021F","article-title":"Functional clustering of B cell receptors using sequence and structural features","volume":"4","author":"Xu","year":"2019","journal-title":"Mol Syst Des Eng"},{"key":"2024090314142398500_ref7","doi-asserted-by":"crossref","first-page":"2675","DOI":"10.4049\/jimmunol.2200063","article-title":"A deep learning model for accurate diagnosis of infection using antibody repertoires","volume":"208","author":"Chen","year":"2022","journal-title":"J Immunol"},{"key":"2024090314142398500_ref8","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1002\/path.5592","article-title":"Classification of intestinal T-cell receptor repertoires using machine learning methods can identify patients with coeliac disease regardless of dietary gluten status","volume":"253","author":"Foers","year":"2021","journal-title":"J Pathol"},{"key":"2024090314142398500_ref9","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2021.680687","article-title":"Immune2vec: embedding B\/T cell receptor sequences in R (N) using natural language processing","volume":"12","author":"Ostrovsky-Berman","year":"2021","journal-title":"Front Immunol"},{"key":"2024090314142398500_ref10","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/s42003-023-04447-4","article-title":"Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity","volume":"6","author":"Park","year":"2023","journal-title":"Commun Biol"},{"key":"2024090314142398500_ref11","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2021.627813","article-title":"Machine learning analysis of naive B-cell receptor repertoires stratifies celiac disease patients and controls","volume":"12","author":"Shemesh","year":"2021","journal-title":"Front Immunol"},{"key":"2024090314142398500_ref12","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1093\/bioinformatics\/btw771","article-title":"Feature selection using a one dimensional naive Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires","volume":"33","author":"Cinelli","year":"2017","journal-title":"Bioinformatics"},{"key":"2024090314142398500_ref13","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.3389\/fimmu.2018.03004","article-title":"Antibody repertoire analysis of hepatitis C virus infections identifies immune signatures associated with spontaneous clearance","volume":"9","author":"Eliyahu","year":"2018","journal-title":"Front Immunol"},{"key":"2024090314142398500_ref14","doi-asserted-by":"crossref","DOI":"10.1101\/2020.04.12.038158","article-title":"Modern hopfield networks and attention for immune repertoire 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antibodies","volume":"13","author":"Richardson","year":"2021","journal-title":"MAbs"},{"key":"2024090314142398500_ref24","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1038\/s41467-019-09278-8","article-title":"Large-scale network analysis reveals the sequence space architecture of antibody repertoires","volume":"10","author":"Miho","year":"2019","journal-title":"Nat Commun"},{"key":"2024090314142398500_ref25","doi-asserted-by":"crossref","first-page":"e1010652","DOI":"10.1371\/journal.pgen.1010652","article-title":"Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals","volume":"19","author":"Ruiz Ortega","year":"2023","journal-title":"PLoS Genet"},{"key":"2024090314142398500_ref26","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"CD-HIT: accelerated for clustering the next-generation sequencing data","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"2024090314142398500_ref27","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1038\/nbt.3988","article-title":"MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets","volume":"35","author":"Steinegger","year":"2017","journal-title":"Nat Biotechnol"},{"key":"2024090314142398500_ref28","doi-asserted-by":"crossref","DOI":"10.1128\/msystems.00722-23","article-title":"Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences","volume":"8","author":"Saputri","year":"2023","journal-title":"mSystems"},{"key":"2024090314142398500_ref29","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1038\/s42256-021-00413-z","article-title":"The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires","volume":"3","author":"Pavlovic","year":"2021","journal-title":"Nat Mach 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to promote NSCLC proliferation","volume":"8","author":"Han","year":"2021","journal-title":"Front Mol Biosci"},{"key":"2024090314142398500_ref40","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.jaut.2018.10.018","article-title":"The landscape and diagnostic potential of T and B cell repertoire in immunoglobulin a nephropathy","volume":"97","author":"Huang","year":"2019","journal-title":"J Autoimmun"},{"key":"2024090314142398500_ref41","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1136\/annrheumdis-2019-215442","article-title":"T cell receptor beta repertoires as novel diagnostic markers for systemic lupus erythematosus and rheumatoid arthritis","volume":"78","author":"Liu","year":"2019","journal-title":"Ann Rheum Dis"},{"key":"2024090314142398500_ref42","doi-asserted-by":"crossref","first-page":"3333","DOI":"10.1016\/j.cell.2023.06.020","article-title":"Targeting of multiple tumor-associated antigens by individual T cell receptors during successful cancer 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