Data-driven derivation and validation of novel phenotypes for acute kidney transplant rejection using semi-supervised clustering

Thibaut Vaulet, Gillian Divard, Olivier Thaunat, Evelyne Lerut, Aleksandar Senev, Olivier Aubert, Elisabet van Loon, Jasper Callemeyn, Marie Paule Emonds, Amaryllis van Craenenbroeck, Katrien de Vusser, Ben Sprangers, Maud Rabeyrin, Valérie Dubois, Dirk Kuypers, Maarten de Vos, Alexandre Loupy, Bart de Moor, Maarten Naesens*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Background Over the past decades, an international group of experts iteratively developed a consensus classification of kidney transplant rejection phenotypes, known as the Banff classification. Data-driven clustering of kidney transplant histologic data could simplify the complex and discretionary rules of the Banff classification, while improving the association with graft failure. Methods The data consisted of a training set of 3510 kidney-transplant biopsies from an observational cohort of 936 recipients. Independent validation of the results was performed on an external set of 3835 biopsies from 1989 patients. On the basis of acute histologic lesion scores and the presence of donor-specific HLA antibodies, stable clustering was achieved on the basis of a consensus of 400 different clustering partitions. Additional information on kidney-transplant failure was introduced with a weighted Euclidean distance. Results Based on the proportion of ambiguous clustering, six clinically meaningful cluster phenotypes were identified. There was significant overlap with the existing Banff classification (adjusted rand index, 0.48). However, the data-driven approach eliminated intermediate and mixed phenotypes and created acute rejection clusters that are each significantly associated with graft failure. Finally, a novel visualization tool presents disease phenotypes and severity in a continuous manner, as a complement to the discrete clusters. Conclusions A semisupervised clustering approach for the identification of clinically meaningful novel phenotypes of kidney transplant rejection has been developed and validated. The approach has the potential to offer a more quantitative evaluation of rejection subtypes and severity, especially in situations in which the current histologic categorization is ambiguous.

Original languageEnglish (US)
Pages (from-to)1084-1096
Number of pages13
JournalJournal of the American Society of Nephrology
Issue number5
StatePublished - May 2021

ASJC Scopus subject areas

  • General Medicine


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