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Assessing rejection-related disease in kidney transplant biopsies based on
archetypal analysis of molecular phenotypes
#MMPMID28614805
Reeve J
; Böhmig GA
; Eskandary F
; Einecke G
; Lefaucheur C
; Loupy A
; Halloran PF
JCI Insight
2017[Jun]; 2
(12
): ? PMID28614805
show ga
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.
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