Abstract
Conventional histologic diagnosis of rejection in kidney transplants has limited repeatability due to its inherent requirement for subjective assessment of lesions, in a rule-based system that does not acknowledge diagnostic uncertainty. Molecular phenotyping affords opportunities for increased precision and improved disease classification to address the limitations of conventional histologic diagnostic systems and quantify levels of uncertainty. Microarray data from 1,208 kidney transplant biopsies were collected prospectively from 13 centers. Cross-validated classifier scores predicting the presence of antibody-mediated rejection (ABMR), T cell–mediated rejection (TCMR), and 5 related histologic lesions were generated using supervised machine learning methods. These scores were used as input for archetypal analysis, an unsupervised method similar to cluster analysis, to examine the distribution of molecular phenotypes related to rejection. Six archetypes were generated: no rejection, TCMR, 3 associated with ABMR (early-stage, fully developed, and late-stage), and mixed rejection (TCMR plus early-stage ABMR). Each biopsy was assigned 6 scores, one for each archetype, representing a probabilistic assessment of that biopsy based on its rejection-related molecular properties. Viewed as clusters, the archetypes were similar to existing histologic Banff categories, but there was 32% disagreement, much of it probably reflecting the “noise” in the current histologic assessment system. Graft survival was lowest for fully developed and late-stage ABMR, and it was better predicted by molecular archetype scores than histologic diagnoses. The results provide a system for precision molecular assessment of biopsies and a new standard for recalibrating conventional diagnostic systems.
Authors
Jeff Reeve, Georg A. Böhmig, Farsad Eskandary, Gunilla Einecke, Carmen Lefaucheur, Alexandre Loupy, Philip F. Halloran, the MMDx-Kidney study group
This file is in Adobe Acrobat (PDF) format. If you have not installed and configured the Adobe Acrobat Reader on your system.
Having trouble reading a PDF?
PDFs are designed to be printed out and read, but if you prefer to read them online, you may find it easier if you increase the view size to 125%.
Having trouble saving a PDF?
Many versions of the free Acrobat Reader do not allow Save. You must instead save the PDF from the JCI Online page you downloaded it from. PC users: Right-click on the Download link and choose the option that says something like "Save Link As...". Mac users should hold the mouse button down on the link to get these same options.
Having trouble printing a PDF?
- Try printing one page at a time or to a newer printer.
- Try saving the file to disk before printing rather than opening it "on the fly." This requires that you configure your browser to "Save" rather than "Launch Application" for the file type "application/pdf", and can usually be done in the "Helper Applications" options.
- Make sure you are using the latest version of Adobe's Acrobat Reader.
