{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:46:33Z","timestamp":1776840393154,"version":"3.51.2"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"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>Identifying neoantigen-specific T-cell receptor (neoTCR) clonotypes is crucial for tracking clinical T-cell dynamics in personalized cancer immunotherapy. Despite advances in experimental and computational approaches for identifying antigen-specific TCR (asTCR) clonotypes, the identification of neoTCR clonotypes remains challenging due to their low frequencies and the limited sensitivity of current methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We introduce NeoTCRseek, an integrated platform that combines extended T-cell culture, deep TCR sequencing, and advanced TCR-clustering tools to enhance neoTCR clonotype identification. NeoTCRseek was developed using a model cytomegalovirus (CMV) antigen and subsequently validated under two neoantigen setups: a single neoantigen and a neoantigen pool representing a multi-antigen context. For each antigen setup, we used three cell sorting-based methods to detect enriched clonotypes and built a validation dataset for asTCR clonotype characterization and NeoTCRseek performance evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>NeoTCR clonotypes exhibited a significantly higher proportion of low-frequency clonotypes (0.01%\u20130.1%) than CMV-specific ones (70.3% vs. 33.3%). Nonetheless, NeoTCRseek achieved a detection limit of 0.01% and high accuracy in both single- and multi-antigen contexts by integrating expanded clonotype detection with co-clustering-based TCR prediction. Compared with the benchmark, NeoTCRseek improved the mean F1 score across the two neoantigen setups from 0.21 to 0.41.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>NeoTCRseek achieves high analytical sensitivity and accuracy in neoTCR clonotype identification and supports multi-antigen analysis, providing an integrated platform for neoTCR clonotype characterization and for tracking clinical T-cell dynamics in personalized cancer immunotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1674427","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:48:48Z","timestamp":1776836928000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["NeoTCRseek: an integrated platform for high-sensitivity identification of neoantigen-specific TCR clonotypes to track clinical T-cell dynamics"],"prefix":"10.3389","volume":"17","author":[{"given":"Bin","family":"Song","sequence":"first","affiliation":[{"name":"College of Life Sciences, University of Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"BGI GenoImmune Therapeutics Inc.","place":["Wuhan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geng","family":"Liu","sequence":"additional","affiliation":[{"name":"BGI GenoImmune Therapeutics Inc.","place":["Wuhan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"BGI GenoImmune Therapeutics Inc.","place":["Wuhan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Life Sciences, University of Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"BGI GenoImmune Therapeutics Inc.","place":["Wuhan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leo J.","family":"Lee","sequence":"additional","affiliation":[{"name":"BGI GenoImmune Therapeutics Inc.","place":["Wuhan, China"]},{"name":"Department of Electrical and Computer Engineering, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto","place":["Toronto, ON, Canada"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1126\/science.aaa4971","article-title":"Neoantigens in cancer immunotherapy","volume":"348","author":"Schumacher","year":"2015","journal-title":"Science"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1038\/ni.3682","article-title":"'Final common pathway' of human cancer immunotherapy: targeting random somatic mutations","volume":"18","author":"Tran","year":"2017","journal-title":"Nat Immunol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1080\/2162402x.2020.1869389","article-title":"Advances in identification and selection of personalized neoantigen\/T-cell pairs for autologous adoptive T cell therapies","volume":"10","author":"Kast","year":"2021","journal-title":"Oncoimmunology"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1038\/s41577-023-00835-3","article-title":"Can we predict T cell specificity with digital biology and machine learning","volume":"23","author":"Hudson","year":"2023","journal-title":"Nat Rev Immunol"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-021-01072-6","article-title":"Sensitive identification of neoantigens and cognate TCRs in human solid tumors","volume":"40","author":"Arnaud","year":"2022","journal-title":"Nat Biotechnol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.crmeth.2023.100600","article-title":"Barcoding intracellular reverse transcription enables high-throughput phenotype-coupled T cell receptor analyses","volume":"3","author":"Jayaraman","year":"2023","journal-title":"Cell Rep Methods"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1126\/science.aan6733","article-title":"Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade","volume":"357","author":"Le","year":"2017","journal-title":"Science"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06063-y","article-title":"Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer","volume":"618","author":"Rojas","year":"2023","journal-title":"Nature"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/bs.mie.2019.10.009","article-title":"High-throughput discovery of cancer-targeting TCRs. 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