Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised Clustering

Abstract

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.

Keywords: acute allograft rejection; kidney biopsy; kidney transplantation; transplant outcomes; transplant pathology.

Graphical abstract

Figure 1.

Distribution of the individual acute lesion scores in the different clusters, and postbiopsy Kaplan-Meier graft survival curves relative to cluster 1 of the derivation cohort (n=3510 biopsies). Biopsies included in cluster 1 were dominated by 0 scores (more than 90% have 0 score in all lesions, except for t [t0 in 72.9%] and C4d [C4d0 in 89.0%]). High g scores drove cluster 2 (56.4% g2 and 43.6% g3; no biopsies with g0 or g1). Compared with cluster 1, biopsies in cluster 2 had a higher proportion of score 1 or 2 acute lesions other than g. High t and i scores dominated cluster 3 biopsies (48.9% t2, 29.9% t3; 49.4% i2; and 48.5% i3). Biopsies in cluster 3 also had a higher proportion of score 1 and 2 acute lesion scores compared with cluster 1, but no g score 2 or higher. Cluster 4 was similar to cluster 1 and was dominated by low acute lesion scores. The main differences besides the presence of DSA was the higher proportion of g (g1 in 16.3% in cluster 4 versus 6.6% in cluster 1; g2 4.9% in cluster 4 versus 0.0% in cluster 1; P≤0.0001), a higher proportion of ptc (ptc1 in 11.4% in cluster 4 versus 4.2% in cluster 1; ptc2 in 8.5% in cluster 4 versus 1.2% in cluster 1; ptc3 in 0.3% in cluster 4 versus 0.1% in cluster 1; P≤0.0001), and a higher proportion of C4d (C4d1 in 13.4% in cluster 4 versus 9.5% in cluster 1; C4d2 in 3.3% in cluster 4 versus 0.5% in cluster 1; C4d3 in 11.1% in cluster 4 versus 1.0% in cluster 1, P≤0.0001). Cluster 5, similarly to cluster 2, was dominated by high g scores (27.4% g2 and 70.5% g3) and did not contain biopsies without g. As in cluster 2, we noted a higher proportion of score 1 and 2 acute lesions (ptc, t, i, v) compared with the cluster 1. Finally, in the presence of DSA, high t and i scores determined cluster 6 (42.4% t2, 22.7% t3; 40.9% i2; and 56.1% i3), and frequent presence of g and ptc. P values refer to HR from the Cox models. C4d_ptc, C4d deposition in peritubular capillaries; thrombi, thrombotic microangiopathy.

Figure 2.

Visualization of the Banff classification and the six clusters on the whole set of kidney transplant biopsies. (A) PCA of the 3510 derivation cohort biopsies calculated from the acute lesion scores and DSA status, overlaid with the six clusters obtained from the semisupervised reclassification pipeline (left panel) and according to the Banff 2019 classification (right panel). Due to the distance-based approach of k-mean, the clusters obtained have a visually better separation than the Banff classification on two-dimensional plots. (B) Polar plot of the 3510 biopsies, with the radius representing the sum of re-weighted acute lesions scores, scaled to the unit interval (from 0 to 1), and the theta angle being directly related to the phenotype using the second semisupervised principal component, namely, the second component of PCA after weighting the lesions scores, overlaid with the six clusters obtained from the semisupervised reclassification pipeline (left panel) and according to the Banff 2019 classification (right panel).

Figure 3.

Association with graft survival in the polar plot visualization tool. (A) Association of the polar plot radius with graft survival in the derivation cohort. We stratified the 3510 biopsies along the radius axis in five strata and plotted the corresponding Kaplan-Meier survival curves. This demonstrates that the radius of the polar plot, which represents the extent of inflammation (the sum of the reweighted acute lesions scores, scaled to the unit interval from 0 to 1) is positively associated with the risk of graft failure. The different levels of inflammation correspond to the following radius: “No inflammation”: radius 0.00–0.04; “Minimal inflammation”: radius 0.04–0.10; “Mild inflammation”: radius 0.10–0.24; “Moderate to severe inflammation”: radius 0.24–42; and “Very severe inflammation”: radius 0.42–1.00. (B) Estimated graft survival probability at 5 years postbiopsy, calculated from the nearest neighborhood with k=40 (left panel) with corresponding calibration curve (right panel).