A somatic hypermutation-based machine learning model stratifies individuals with Crohn's disease and controls.

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. While 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 dataset 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 dataset from blood samples of adults with CD vs. 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.