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

• Published in: The Journal of pathology Vol. 253; no. 3; pp. 279 - 291
• Main Authors: Foers, Andrew D, Shoukat, M Saad, Welsh, Oliver E, Donovan, Killian, Petry, Russell, Evans, Shelley C, FitzPatrick, Michael EB, Collins, Nadine, Klenerman, Paul, Fowler, Anna, Soilleux, Elizabeth J
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01-03-2021; Wiley Subscription Services, Inc
• Subjects: Adaptive immunity; Adult; Biopsy; CD4 antigen; CD8 antigen; Celiac disease; Celiac Disease - diagnosis; Celiac Disease - immunology; clustering; coeliac disease; Diagnosis; Diet, Gluten-Free; DNA sequencing; duodenum; Female; Genomics; Gluten; Humans; Immune response; Inflammation; Intestine; Intestine, Small - immunology; Learning algorithms; Lymphoma; Machine Learning; Male; Middle Aged; Original Paper; Original Papers; Pathology; Receptors, Antigen, T-Cell, gamma-delta - immunology; T cell receptors; TRD; TRG; T‐cell receptor repertoire; T‐lymphocyte; coeliac disease; gluten; TRD; TRG; T-lymphocyte; T-cell receptor repertoire; clustering; machine learning; duodenum
• ISSN: 0022-3417; 1096-9896
• DOI: 10.1002/path.5592

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 histological abnormality if insufficient gluten is consumed. CeD pathogenesis involves both CD4+ T‐cell‐mediated gluten recognition and CD8+ and γδ T‐cell‐mediated inflammation, with a previous study demonstrating a permanent change in γδ T‐cell populations in CeD. We leveraged this understanding and explored the diagnostic utility of bulk T‐cell receptor (TCR) sequencing in assessing duodenal biopsies in CeD. Genomic DNA extracted from duodenal biopsies underwent sequencing for TCR‐δ (TRD) (CeD, n = 11; non‐CeD, n = 11) and TCR‐γ (TRG) (CeD, n = 33; non‐CeD, n = 21). We developed a novel machine learning‐based analysis of the TCR repertoire, clustering samples by diagnosis. Leave‐one‐out cross‐validation (LOOCV) was performed to validate the classification algorithm. Using TRD repertoire, 100% (22/22) of duodenal biopsies were correctly classified, with a LOOCV accuracy of 91%. Using TCR‐γ (TRG) repertoire, 94.4% (51/54) of duodenal biopsies were correctly classified, with LOOCV of 87%. Duodenal biopsy TRG repertoire analysis permitted accurate classification of biopsies from patients with CeD following a strict gluten‐free diet for at least 6 months, who would be misclassified by current tests. This result reflects permanent changes to the duodenal γδ TCR repertoire in CeD, even in the absence of gluten consumption. Our method could complement or replace histopathological diagnosis in CeD and might have particular clinical utility in the diagnostic testing of patients unable to tolerate dietary gluten, and for assessing duodenal biopsies with equivocal features. This approach is generalisable to any TCR/BCR locus and any sequencing platform, with potential to predict diagnosis or prognosis in conditions mediated or modulated by the adaptive immune response. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.