Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised Clustering.
acute allograft rejection
kidney biopsy
kidney transplantation
transplant outcomes
transplant pathology
Journal
Journal of the American Society of Nephrology : JASN
ISSN: 1533-3450
Titre abrégé: J Am Soc Nephrol
Pays: United States
ID NLM: 9013836
Informations de publication
Date de publication:
03 05 2021
03 05 2021
Historique:
received:
06
10
2020
accepted:
04
01
2021
pubmed:
11
3
2021
medline:
2
10
2021
entrez:
10
3
2021
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 33687976
pii: 00001751-202105000-00014
doi: 10.1681/ASN.2020101418
pmc: PMC8259675
doi:
Types de publication
Journal Article
Observational Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1084-1096Commentaires et corrections
Type : CommentIn
Type : CommentIn
Type : CommentIn
Informations de copyright
Copyright © 2021 by the American Society of Nephrology.
Références
Solez K, Axelsen RA, Benediktsson H, Burdick JF, Cohen AH, Colvin RB, et al.: International standardization of criteria for the histologic diagnosis of renal allograft rejection: The Banff working classification of kidney transplant pathology. Kidney Int 44: 411–422, 1993 8377384
Haas M, Loupy A, Lefaucheur C, Roufosse C, Glotz D, Seron D, et al.: The Banff 2017 Kidney Meeting Report: Revised diagnostic criteria for chronic active T cell-mediated rejection, antibody-mediated rejection, and prospects for integrative endpoints for next-generation clinical trials. Am J Transplant 18: 293–307, 2018 29243394
Loupy A, Haas M, Roufosse C, Naesens M, Adam B, Afrouzian M, et al.: The Banff 2019 Kidney Meeting Report (I): Updates on and clarification of criteria for T cell- and antibody-mediated rejection. Am J Transplant 20: 2318–2331, 2020 32463180
Roufosse C, Simmonds N, Clahsen-van Groningen M, Haas M, Henriksen KJ, Horsfield C, et al.: A 2018 reference guide to the banff classification of renal allograft pathology. Transplantation 102: 1795–1814, 2018 30028786
Racusen LC, Colvin RB, Solez K, Mihatsch MJ, Halloran PF, Campbell PM, et al.: Antibody-mediated rejection criteria - an addition to the Banff 97 classification of renal allograft rejection. Am J Transplant 3: 708–714, 2003 12780562
Haas M, Sis B, Racusen LC, Solez K, Glotz D, Colvin RB, et al.; Banff meeting report writing committee: Banff 2013 meeting report: Inclusion of c4d-negative antibody-mediated rejection and antibody-associated arterial lesions [published correction appears in Am J Transplant 15: 2784, 2015 10.1111/ajt.13517]. Am J Transplant 14: 272–283, 2014 24472190
Loupy A, Haas M, Solez K, Racusen L, Glotz D, Seron D, et al.: The Banff 2015 kidney meeting report: Current challenges in rejection classification and prospects for adopting molecular pathology. Am J Transplant 17: 28–41, 2017 27862883
Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Ed., New York Springer Science & Business Media, 2009
Bullinger L, Döhner K, Bair E, Fröhling S, Schlenk RF, Tibshirani R, et al.: Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med 350: 1605–1616, 2004 15084693
Bair E, Tibshirani R: Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol 2: E108, 2004 15094809
Kickingereder P, Burth S, Wick A, Götz M, Eidel O, Schlemmer HP, et al.: Radiomic profiling of glioblastoma: Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280: 880–889, 2016 27326665
Senev A, Coemans M, Lerut E, Van Sandt V, Daniëls L, Kuypers D, et al.: Histological picture of antibody-mediated rejection without donor-specific anti-HLA antibodies: Clinical presentation and implications for outcome. Am J Transplant 19: 763–780, 2019 30107078
Coemans M, Van Loon E, Lerut E, Gillard P, Sprangers B, Senev A, et al.: Occurrence of diabetic nephropathy after renal transplantation despite intensive glycemic control: An observational cohort study. Diabetes Care 42: 625–634, 2019 10.2337/dc18-1936 30765434
doi: 10.2337/dc18-1936
Senev A, Lerut E, Van Sandt V, Coemans M, Callemeyn J, Sprangers B, et al.: Specificity, strength, and evolution of pretransplant donor-specific HLA antibodies determine outcome after kidney transplantation. Am J Transplant 19: 3100–3113, 2019 31062492
Strehl A, Ghosh J: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3: 583–617, 2003
Monti S, Tamayo P, Mesirov J, Golub T: Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn 52: 91–118, 2003
Şenbabaoğlu Y, Michailidis G, Li JZ: Critical limitations of consensus clustering in class discovery. Sci Rep 4: 6207, 2014 25158761
Royston P, Parmar MK: Restricted mean survival time: An alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Med Res Methodol 13: 152, 2013 24314264
van, Rossum G: Python tutorial, Amsterdam, Centrum voor Wiskunde en Informatica (CWI), 1995
Koenig A, Chen CC, Marçais A, Barba T, Mathias V, Sicard A, et al.: Missing self triggers NK cell-mediated chronic vascular rejection of solid organ transplants. Nat Commun 10: 5350, 2019 31767837
Bestard O, Grinyó J: Refinement of humoral rejection effector mechanisms to identify specific pathogenic histological lesions with different graft outcomes. Am J Transplant 19: 952–953, 2019 30411840
Callemeyn J, Lerut E, de Loor H, Arijs I, Thaunat O, Koenig A, et al.: Transcriptional changes in kidney allografts with histology of antibody-mediated rejection without anti-HLA donor-specific antibodies. J Am Soc Nephrol 31: 2168–2183, 2020 32641395
Madill-Thomsen K, Perkowska-Ptasińska A, Böhmig GA, Eskandary F, Einecke G, Gupta G, et al.; MMDx-Kideny Study Group: Discrepancy analysis comparing molecular and histology diagnoses in kidney transplant biopsies. Am J Transplant 20: 1341–1350, 2019
Loupy A, Aubert O, Orandi BJ, Naesens M, Bouatou Y, Raynaud M, et al.: Prediction system for risk of allograft loss in patients receiving kidney transplants: International derivation and validation study. BMJ 366: l4923, 2019 31530561
Furness PN, Taub N, Assmann KJ, Banfi G, Cosyns JP, Dorman AM, et al.: International variation in histologic grading is large, and persistent feedback does not improve reproducibility. Am J Surg Pathol 27: 805–810, 2003 12766585
Smith B, Cornell LD, Smith M, Cortese C, Geiger X, Alexander MP, et al.: A method to reduce variability in scoring antibody-mediated rejection in renal allografts: Implications for clinical trials - a retrospective study. Transpl Int 32: 173–183, 2019 30179275
Sicard A, Meas-Yedid V, Rabeyrin M, Koenig A, Ducreux S, Dijoud F, et al.: Computer-assisted topological analysis of renal allograft inflammation adds to risk evaluation at diagnosis of humoral rejection. Kidney Int 92: 214–226, 2017 28318622
Fröhlich H, Balling R, Beerenwinkel N, Kohlbacher O, Kumar S, Lengauer T, et al.: From hype to reality: Data science enabling personalized medicine. BMC Med 16: 150, 2018 30145981