Literature DB >> 19519809

Diagnosing rejection in renal transplants: a comparison of molecular- and histopathology-based approaches.

J Reeve1, G Einecke, M Mengel, B Sis, N Kayser, B Kaplan, P F Halloran.   

Abstract

The transcriptome has considerable potential for improving biopsy diagnoses. However, to realize this potential the relationship between the molecular phenotype of disease and histopathology must be established. We assessed 186 consecutive clinically indicated kidney transplant biopsies using microarrays, and built a classifier to distinguish rejection from nonrejection using predictive analysis of microarrays (PAM). Most genes selected by PAM were interferon-gamma-inducible or cytotoxic T-cell associated, for example, CXCL9, CXCL11, GBP1 and INDO. We then compared the PAM diagnoses to those from histopathology, which are based on the Banff diagnostic criteria. Disagreement occurred in approximately 20% of diagnoses, principally because of idiosyncratic limitations in the histopathology scoring system. The problematic diagnosis of 'borderline rejection' was resolved by PAM into two distinct classes, rejection and nonrejection. The diagnostic discrepancies between Banff and PAM in these cases were largely due to the Banff system's requirement for a tubulitis threshold in defining rejection. By examining the discrepancies between gene expression and histopathology, we provide external validation of the main features of the histopathology diagnostic criteria (the Banff consensus system), recommend improvements and outline a pathway for introducing molecular measurements.

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Year:  2009        PMID: 19519809     DOI: 10.1111/j.1600-6143.2009.02694.x

Source DB:  PubMed          Journal:  Am J Transplant        ISSN: 1600-6135            Impact factor:   8.086


  43 in total

1.  A molecular classifier for predicting future graft loss in late kidney transplant biopsies.

Authors:  Gunilla Einecke; Jeff Reeve; Banu Sis; Michael Mengel; Luis Hidalgo; Konrad S Famulski; Arthur Matas; Bert Kasiske; Bruce Kaplan; Philip F Halloran
Journal:  J Clin Invest       Date:  2010-05-24       Impact factor: 14.808

Review 2.  Integrative analysis of -omics data and histologic scoring in renal disease and transplantation: renal histogenomics.

Authors:  Paul Perco; Rainer Oberbauer
Journal:  Semin Nephrol       Date:  2010-09       Impact factor: 5.299

3.  Novel diagnostics in renal transplantation.

Authors:  Niamh Kieran; Kim Muczynski; Vijayakrishna Vk Gadi
Journal:  Chimerism       Date:  2010-10

4.  Reassessing the Significance of Intimal Arteritis in Kidney Transplant Biopsy Specimens.

Authors:  Israel D R Salazar; Maribel Merino López; Jessica Chang; Philip F Halloran
Journal:  J Am Soc Nephrol       Date:  2015-04-27       Impact factor: 10.121

5.  Key driver genes as potential therapeutic targets in renal allograft rejection.

Authors:  Zhengzi Yi; Karen L Keung; Li Li; Min Hu; Bo Lu; Leigh Nicholson; Elvira Jimenez-Vera; Madhav C Menon; Chengguo Wei; Stephen Alexander; Barbara Murphy; Philip J O'Connell; Weijia Zhang
Journal:  JCI Insight       Date:  2020-08-06

Review 6.  Biomarkers to detect rejection after kidney transplantation.

Authors:  Vikas R Dharnidharka; Andrew Malone
Journal:  Pediatr Nephrol       Date:  2017-06-19       Impact factor: 3.714

Review 7.  Moving Biomarkers toward Clinical Implementation in Kidney Transplantation.

Authors:  Madhav C Menon; Barbara Murphy; Peter S Heeger
Journal:  J Am Soc Nephrol       Date:  2017-01-06       Impact factor: 10.121

8.  Relationships among injury, fibrosis, and time in human kidney transplants.

Authors:  Jeffery M Venner; Konrad S Famulski; Jeff Reeve; Jessica Chang; Philip F Halloran
Journal:  JCI Insight       Date:  2016-01-21

9.  The evolution of the Banff classification schema for diagnosing renal allograft rejection and its implications for clinicians.

Authors:  D M Bhowmik; A K Dinda; P Mahanta; S K Agarwal
Journal:  Indian J Nephrol       Date:  2010-01

Review 10.  Molecular assessment of disease states in kidney transplant biopsy samples.

Authors:  Philip F Halloran; Konrad S Famulski; Jeff Reeve
Journal:  Nat Rev Nephrol       Date:  2016-06-27       Impact factor: 28.314

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