Molecular Transcriptomics in Diagnosing Pediatric Kidney Transplant Rejection: the PANDA-Kids-ATLAS Study

Molecular signatures (molecular classifiers) will be identified through bulk transcriptomic analysis utilizing the validated Banff Human Organ Transplant (B-HOT) gene panel, consisting of 770 rejection- and tolerance-related genes. Formalin-fixed, paraffin-embedded (FFPE) biopsy samples from pediatric kidney transplant recipients will be processed and analyzed using the NanoString nCounter® platform. Specifically, molecular classifiers distinguishing classical antibody-mediated rejection (AMR), T-cell mediated rejection (TCMR), and novel Banff 2022 antibody-mediated rejection-related categories-including microvascular inflammation with donor-specific antibodies and negative C4d staining (MVI+DSA-C4d-) and probable antibody-mediated rejection (pABMR)-will be quantified and reported. Classifier results will be summarized as normalized gene expression profiles, enabling clear discrimination among different categories of rejection and non-rejection biopsies.

An archetype-based diagnostic framework will be generated by integrating transcriptomic molecular classifiers (quantified using NanoString nCounter® platform and Banff Human Organ Transplant (B-HOT) gene panel) with clinical parameters. Clinical parameters include patient demographics (age, sex), transplant characteristics, and clinical outcomes (e.g., serum creatinine, estimated glomerular filtration rate (eGFR), proteinuria, biopsy indication). The integrated diagnostic system will provide archetype-based patient profiles to enhance diagnostic precision and personalized clinical management. Aggregation will be performed using multidimensional modeling techniques (principal component analysis, cluster analyses) and classification algorithms (logistic regression, random forest), providing composite diagnostic archetypes.

Unit of measure: Composite diagnostic archetype (multidimensional categorical profile)

An archetype-based multidimensional diagnostic framework will be developed by combining transcriptomic molecular classifiers (from the NanoString nCounter® platform and B-HOT gene panel) with biological and immunological parameters. Specifically, this will include immunological markers such as donor-specific antibodies (DSA), C4d staining, complement factors, and inflammatory biomarkers (e.g., cytokines, chemokines, immune cell subset analysis). The integration will be conducted through bioinformatics approaches and supervised machine learning models, culminating in archetypal patient classification based on immune and biological profiles.

Unit of measure: Composite immuno-biological archetype (multidimensional categorical profile)

The correlation between archetype-based diagnostic profiles (derived from molecular, clinical, biological, and immunological data integration) and kidney allograft clinical outcomes will be assessed.

Clinical outcomes include: 1) Incidence of acute rejection episodes (percentage [%] of patients experiencing biopsy-proven acute rejection, confirmed via Banff 2022 criteria). 2) Allograft survival rate (% graft survival, defined as a functioning graft without dialysis dependence or retransplantation). 3) Allograft function decline (rate of decline in eGFR [mL/min/1.73 m²/year], calculated from serum creatinine using the Schwartz pediatric formula).

Each of these clinical outcomes will be individually correlated with the diagnostic archetype profiles using statistical analyses such as Kaplan-Meier survival curves, Cox proportional hazard models (for allograft survival and rejection-free survival), and regression analysis (for eGFR decline).