{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T08:11:47Z","timestamp":1768637507019,"version":"3.49.0"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Lyda Hill Department of Bioinformatics and NCI","award":["1R01CA245318-01A1"],"award-info":[{"award-number":["1R01CA245318-01A1"]}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Summary<\/jats:title><jats:p>T cells participate directly in the body's immune response to cancer, allowing immunotherapy treatments to effectively recognize and target cancer cells. We previously developed DeepCAT to demonstrate that T cells serve as a biomarker of immune response in cancer patients and can be utilized as a diagnostic tool to differentiate healthy and cancer patient samples. However, DeepCAT\u2019s reliance on tumor bulk RNA-seq samples as training data limited its further performance improvement. Here, we benchmarked a new approach, AutoCAT, to predict tumor-associated TCRs from targeted TCR-seq data as a new form of input for DeepCAT, and observed the same level of predictive accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Source code is freely available at https:\/\/github.com\/cew88\/AutoCAT, and data is available at 10.5281\/zenodo.5176884.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab661","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T19:14:43Z","timestamp":1631560483000},"page":"589-591","source":"Crossref","is-referenced-by-count":6,"title":["AutoCAT: automated cancer-associated TCRs discovery from TCR-seq data"],"prefix":"10.1093","volume":"38","author":[{"given":"Christina","family":"Wong","sequence":"first","affiliation":[{"name":"Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center , Dallas, TX 75390, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8617-900X","authenticated-orcid":false,"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center , Dallas, TX 75390, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"2023033004311088900_","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1038\/s41587-020-0656-3","article-title":"Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases","volume":"39","author":"Barennes","year":"2021","journal-title":"Nat. Biotechnol"},{"key":"2023033004311088900_","doi-asserted-by":"crossref","first-page":"E10409","DOI":"10.1073\/pnas.1713863114","article-title":"T cell receptor sequencing of early-stage breast cancer tumors identifies altered clonal structure of the T cell repertoire","volume":"114","author":"Beausang","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023033004311088900_","doi-asserted-by":"crossref","first-page":"eaaz3738","DOI":"10.1126\/scitranslmed.aaz3738","article-title":"De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection","volume":"12","author":"Beshnova","year":"2020","journal-title":"Sci. Transl. Med"},{"key":"2023033004311088900_","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1146\/annurev-immunol-042718-041757","article-title":"Using T cell receptor repertoires to understand the principles of adaptive immune recognition","volume":"37","author":"Bradley","year":"2019","journal-title":"Annu. Rev. 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Genet"},{"key":"2023033004311088900_","doi-asserted-by":"crossref","first-page":"4865","DOI":"10.1093\/bioinformatics\/btab446","article-title":"ClusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity","volume":"37","author":"Valkiers","year":"2021","journal-title":"Bioinformatics (Oxford, England)"},{"key":"2023033004311088900_","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1158\/1078-0432.CCR-19-3249","article-title":"Investigation of antigen-specific T-cell receptor clusters in human cancers","volume":"26","author":"Zhang","year":"2020","journal-title":"Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res"},{"key":"2023033004311088900_","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.1038\/s41467-021-25006-7","article-title":"Ultrafast TCR clustering and multi-disease repertoire classification by isometric transformation","volume":"12","author":"Zhang","year":"2021","journal-title":"Nat. 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