[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126127-en":3,"doc-seo-126127-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126127,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning - Research article","B cell lymphomas require long training to classify accurately from light microscopy, and B cell receptor (BCR) repertoire patterns along with the immune microenvironment are key discriminators of lymphoma subsets. This study evaluates whether BCR repertoire next-generation sequencing from lymphoma-infiltrated tissues combined with machine learning can support diagnostic subclassification. Random forest and logistic regression models trained on clonal distribution, VDJ gene usage, and physicochemical properties were validated across 620 lymphoma and 291 control samples.","PLOS COMPUTATIONAL BIOLOGY  \nOPEN ACCESS  \nCitation: Schmidt-Barbo P, Kalweit G, Naouar M, Paschold L, Willscher E, Schultheiß C, et al. (2024) Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning. PLoS Comput Biol 20(7): e1011570 . [https://doi](https://doi). org/10 .1371/journal.pcbi.1011570  \nEditor: Stacey D. Finley, University of Southern California, UNITED STATES OF AMERICA  \nReceived: October 5, 2023  \nAccepted: June 7, 2024  \nPublished: July 2, 2024  \nCopyright: © 2024 Schmidt-Barbo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: The data of our analysis can be accessed at the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB66357 ([https://www.ebi](https://www.ebi). [ac.uk/ena/browser/view/PRJEB66357](ac.uk/ena/browser/view/PRJEB66357)) . Corresponding code is available under [https://](https://)[ ](https://)[github.com/paulovic96](github.com/paulovic96) .  \nFunding: This project was funded by the Mertelsmann Foundation (grant to MB) . The funders had no role in study design, data collection  \nRESEARCH ARTICLE  \nDetection of disease-specific signatures in B cell repertoires of lymphomas using machine learning  \nPaul Schmidt-Barbo1,2, Gabriel Kalweit2,3, Mehdi Naouar2,3, Lisa Paschold4, Edith Willscher4, Christoph Schultheiß1, Bruno M¨arkl5, Stefan Dirnhofer6, AlexandarTzankov6, Mascha Binder1,2,7☯ *, Maria Kalweit2,3☯  \n1 Department of Biomedicine, Translational Immuno-Oncology, University Hospital Basel, Basel, Switzerland, 2 Collaborative Research Institute Intelligent Oncology (CRIION), Freiburg, Germany,  \n3 Neurorobotics Lab, University of Freiburg, Freiburg, Germany, 4 Internal Medicine IV, Oncology/ Hematology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany, 5 Pathology, University Hospital Augsburg, Augsburg, Germany, 6 Pathology, University Hospital Basel, Basel, Switzerland,  \n7 Medical Oncology, University Hospital Basel, Basel, Switzerland  \n☯ These authors contributed equally to this work.  \n* mascha.[binder@unibas.ch](binder@unibas.ch)  \nAbstract  \nThe classification of B cell lymphomas—mainly based on light microscopy evaluation by a pathologist—requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples—nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)—alongside with 291 control samples. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL—that has a dominant background of non-malignant bystander cells—a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 samples from all three lymphoma entities and 58 samples from healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differen","cbCaismBOqwTZ3fm","https://ap.wps.com/l/cbCaismBOqwTZ3fm","pdf",2413143,5,1,17,"English","en",105,"# Abstract\n# Author summary\n# Introduction","[{\"question\":\"What data type is used to support lymphoma subclassification in this study?\",\"answer\":\"The study uses B cell receptor (BCR) repertoire next-generation sequencing from lymphoma-infiltrated tissue samples, together with machine learning.\"},{\"question\":\"Which lymphoma entities are modeled and compared?\",\"answer\":\"The models are trained to distinguish among nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL), and chronic lymphocytic leukemia (CLL).\"},{\"question\":\"What performance result is reported for the classification model?\",\"answer\":\"A weighted F1-score of 0.84 is reported on a test cohort that includes samples from all three lymphoma entities and healthy individuals.\"}]","Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning - 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