A somatic hypermutation–based machine learning model stratifies individuals with Crohn’s disease and controls

Modi Safra, Lael Werner, Ayelet Peres, Pazit Polak, Naomi Salamon, Michael Schvimer, Batia Weiss, Iris Barshack, Dror S. Shouval, Gur Yaari

Research output: Contribution to journalArticlepeer-review

Abstract

Crohn’s disease (CD) is a chronic relapsing–remitting inflammatory disorder of the gastrointestinal tract that is characterized by altered innate and adaptive immune function. Although massively parallel sequencing studies of the T cell receptor repertoire identified oligoclonal expansion of unique clones, much less is known about the B cell receptor (BCR) repertoire in CD. Here, we present a novel BCR repertoire sequencing data set from ileal biopsies from pediatric patients with CD and controls, and identify CD-specific somatic hypermutation (SHM) patterns, revealed by a machine learning (ML) algorithm trained on BCR repertoire sequences. Moreover, ML classification of a different data set from blood samples of adults with CD versus controls identified that V gene usage, clusters, or mutation frequencies yielded excellent results in classifying the disease (F1 > 90%). In summary, we show that an ML algorithm enables the classification of CD based on unique BCR repertoire features with high accuracy.

Original languageEnglish
Pages (from-to)71-79
Number of pages9
JournalGenome Research
Volume33
Issue number1
DOIs
StatePublished - Jan 2023

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