Kidney Rejection Transplant
Conditions
Keywords
transcriptomics, pediatric kidney transplantation, allograft rejection, precision diagnosis, molecular classifiers
Brief summary
Children with kidney failure have markedly increased mortality and face repeated transplantation over their lifetime due to limited allograft half-life (12-15 years). Current biopsy-based diagnoses of rejection (using Banff 2022 criteria) suffer from variability and limited sensitivity. PANDA-Kids-ATLAS will analyze up to 600 pediatric FFPE kidney biopsies across multiple centres using the Banff Human Organ Transplant (B-HOT) NanoString panel to develop and validate molecular classifiers of AMR, TCMR and related phenotypes. A secure REDCap database will integrate molecular, pathological and clinical data, aiming to improve early detection, personalize therapy, and enhance long-term graft survival and patient quality of life.
Detailed description
The study builds a deeply phenotyped international cohort of pediatric transplant patients (\<21 years) with both retrospective (2014-present) and prospective (through Dec 2027) biopsy sampling. Four diagnostic baskets (classical AMR/TCMR; probable ABMR/MVI; other injury; normal) will each contribute equal numbers of cases for classifier validation (Part A) and real-world prevalence samples for outcome association (Part B). FFPE blocks will be centrally reviewed via Banff 2022 automated and expert pathologist interpretation, then processed by NanoString nCounter® using the 770-gene B-HOT panel. Stratified random sampling, robust QC, and integration with clinical/immunological parameters in REDCap will underpin molecular classifier development and validation. Follow-up includes clinical outcomes and graft function monitoring.
Interventions
None listed
Sponsors
Paris Cardiovascular Research Center (Inserm U970)
Paris Translational Research Center for Organ Transplantation
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
0 Years to 21 Years
Healthy volunteers
Yes
Inclusion criteria
* Age ≤ 21 years * Single kidney transplant recipients (deceased or living donor) * Written informed consent from patients/guardians
Exclusion criteria
* Multi-organ transplantation history * Biopsies with insufficient tissue
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Identification of Molecular Classifiers for Kidney Allograft Rejection Using Banff Human Organ Transplant (B-HOT) Gene Panel via NanoString nCounter® | 3 years | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Integration of Molecular Classifiers with Clinical Parameters into an Archetype-based Diagnostic System for Kidney Allograft Rejection | 3 years | 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) |
| Integration of Molecular Classifiers with Biological and Immunological Parameters into an Archetype-based Diagnostic System for Kidney Allograft Rejection | 3 years | 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) |
Other
| Measure | Time frame | Description |
|---|---|---|
| Correlation of Archetype-based Diagnostic Profiles with Kidney Allograft Clinical Outcomes | 3 years | 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). |
Countries
France
Contacts
Primary ContactEvgenia Preka, MD, PhDc
Outcome results
None listed