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.