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Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

ABSTRACT: The 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 differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model.

SUBMITTER: Schmidt-Barbo P

PROVIDER: S-EPMC11249212 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

Schmidt-Barbo Paul P Kalweit Gabriel G Naouar Mehdi M Paschold Lisa L Willscher Edith E Schultheiß Christoph C Märkl Bruno B Dirnhofer Stefan S Tzankov Alexandar A Binder Mascha M Kalweit Maria M

PLoS computational biology 20240702 7

The 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 subclassificat ...[more]

PMID: 38954728

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Dataset Information

Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

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Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

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