Data-Driven Kidney Transplant Phenotyping as a Histology-Independent Framework for Biomarker Discovery.

ABSTRACT

BACKGROUND: In transplant medicine, clinical decision making largely relies on histology of biopsy specimens. However, histology suffers from low specificity, sensitivity, and reproducibility, leading to suboptimal stratification of patients. We developed a histology-independent immune framework of kidney graft homeostasis and rejection. METHODS: We applied tailored RNA deconvolution for leukocyte enumeration and coregulated gene network analysis to published bulk human kidney transplant RNA transcriptomes as input for unsupervised, high-dimensional phenotype clustering. We used framework-based graft survival analysis to identify a biomarker that was subsequently characterized in independent transplant biopsy specimens. RESULTS: We found seven immune phenotypes that confirm known rejection types and uncovered novel signatures. The molecular phenotypes allow for improved graft survival analysis compared with histology, and identify a high-risk group in nonrejecting transplants. Two fibrosis-related phenotypes with distinct immune features emerged with reduced graft survival. We identified lysyl oxidase-like 2 (LOXL2)-expressing peritubular CD68+ macrophages as a framework-derived biomarker of impaired allograft function. These cells precede graft fibrosis, as demonstrated in longitudinal biopsy specimens, and may be clinically useful as a biomarker for early fibrogenesis. CONCLUSIONS: This study provides a comprehensive, data-driven atlas of human kidney transplant phenotypes and demonstrates its utility to identify novel clinical biomarkers.