Classification of intestinal T cell receptor repertoires using machine learning methods can identify patients with coeliac disease regardless of dietary gluten status

In coeliac disease (CeD), immune-mediated small intestinal damage is precipitated by gluten, leading to variable symptoms and complications, occasionally including aggressive T-cell lymphoma. Diagnosis, based primarily on histopathological examination of duodenal biopsies, is confounded by poor concordance between pathologists and minimal abnormalities if insufficient gluten is consumed. We explored the diagnostic utility of bulk T-cell receptor (TCR) sequencing in assessing duodenal biopsies in CeD, building a novel algorithm. Our algorithm correctly classified 100% (22/22) duodenal biopsies using TCR-D (TRD) repertoire from genomic DNA template, with a leave-one-out cross-validation (LOOCV) accuracy of 91%. Using TCR-G (TRG) repertoire, 94.4% (51/54) duodenal biopsies were correctly classified, with LOOCV of 87%. Importantly, analysis of TRG repertoires from duodenal biopsies permitted accurate classification of biopsies from patients with CeD following a strict gluten-free diet, who would be misclassified by current tests. This method has the potential to complement or replace histopathological diagnosis in coeliac disease.