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-no

SUBMITTER: Schmidt-Barbo P 

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

REPOSITORIES: biostudies-literature