Normalization of nCounter Gene Expression Data Alters Molecular Diagnostics in Kidney Transplantation.

Abstract

The Banff 2022 classification endorses intragraft gene-expression profiling using the Banff Human Organ Transplant (B-HOT) consensus gene panel for rejection diagnosis. However, lack of standardized analytical pipelines, including data normalization, limits clinical implementation, with its impact on diagnostic performance yet to be determined. We evaluated ten normalization methods in 868 kidney allograft biopsies from nine European and North American centers, all Banff-graded and B-HOT profiled on nCounter, comprising a derivation (n=441), internal (n=186) and external (n=241) validation cohorts. Each method was assessed through its downstream impact on: (i) gene count stability, (ii) differential expression and cross-platform concordance with RNA-seq data, and (iii) discrimination and calibration of predictive models for antibody- (AMR) and T cell-mediated rejection (TCMR). Most methods improved count stability and showed high concordance with RNA-seq for overall gene expression. They also produced robust differential expression signatures consistent with those detected by RNA-seq, except for RUVSeq and RCRNorm, which identified fewer differentially expressed genes and showed lower concordance. In the overall validation cohort (n=427), diagnostic performance was consistently high across nSolver-based approaches, nanostringr, NanoStringDiff, MetaNorm, and RCRNormFast (AMR AUROC 0.88-0.91; AUPRC 0.86-0.89; TCMR AUROC 0.90-0.92; AUPRC 0.78-0.83). Performance declined with RCRNorm (AMR AUROC/AUPRC 0.55/0.41; TCMR 0.53/0.18) and, for TCMR, with RUVSeq (AUROC 0.84-0.85; AUPRC 0.64-0.65). Calibration was satisfactory for most methods, except for RCRNorm and for TCMR models after RUVSeq. Normalization choice significantly impacted gene expression profiles and diagnostic classifier performance. Most methods, including nSolver-based pipelines, achieved robust discrimination for both AMR and TCMR. Complex methods, including RCRNorm, and RUVSeq for TCMR, reduced performance, with simpler approaches consistently outperforming them for B-HOT-based molecular diagnostics.