The EUropean TRAnsplantation and INnovation (EU-TRAIN) consortium for improving diagnosis and risk stratification in kidney transplant patients

Project Information

EU-TRAIN

Grant agreement ID: 754995

DOI

10.3030/754995

Project closed

EC signature date 27 November 2017

Start date 1 January 2018

End date 30 June 2024

Funded under

• SOCIETAL CHALLENGES - Health, demographic change and well-being

Total cost

€ 6 641 546,00

EU contribution

€ 6 641 546,00

Coordinated by

INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE
France

Periodic Reporting for period 5 - EU-TRAIN (The EUropean TRAnsplantation and INnovation (EU-TRAIN) consortium for improving diagnosis and risk stratification in kidney transplant patients)

Reporting period: 2023-07-01 to 2024-06-30

Summary of the context and overall objectives of the project

End stage renal diseases affect more than 3,900,000 patients worldwide with approximately 6% growth rate in 2016. Kidney transplantation is currently the best therapeutic option with around 1,500,000 patients living with a kidney transplant. However, long-term graft survival has not been improved in the past 15 years and rejection is a major cause of graft loss.
There is a major unmet need: a lack of robust tools to stratify the risk of rejection, graft function decline and graft loss.
To tackle this issue, the EU-TRAIN project aimed at developing a risk stratification system in kidney transplantation (the EU-TRACER system) by integrating multidimensional data from patients’ follow-up.
The project was structured into 8 work packages (WPs), and focused on:

• Building a prospective transplant cohort of kidney transplant patients (WP1);
• Processing samples from patients included in the cohort of WP1 and analyse them to discover biomarkers of the allograft state (WP2);
• Building an infrastructure gathering all the patients’ information (medical as well as from their samples) retrieved in WP1 and WP2 – EU-TRACER-hom-ics (WP3);
• Combining all these data to create algorithms allowing to predict patients with a higher risk profile for allograft loss – EU-TRACER algorithms (WP4);
• Building an online interface for the physicians and patients to follow the predictions about a patient’s risk profile, and test it in a real-life setting (EU-TRACER web application) (WP5).

Remaining WPs concern the logistics of the studies (WP6), the management and coordination (WP7), and dissemination of the results and intellectual property management (WP8).

Conclusions of the action:
The EU-TRAIN project made significant progress in creating a comprehensive risk stratification system for kidney transplantation, addressing the unmet need for tools to predict rejection and graft loss. Key achievements include building a large transplant cohort, discovering biomarkers, advancing the EU-TRACER system and algorithms, and translating these innovations into practical tools for clinicians and patients. The project has achieved breakthroughs in precision diagnostics, paving the way for personalized medicine and improved patient outcomes in kidney transplantation.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The EU-TRAIN project has made significant progress, particularly through the successful execution of its first clinical trial. This trial recruited 550 kidney transplant patients. Biological samples were systematically collected at several critical time points: on the day of transplantation, three months post-transplant, twelve months post-transplant, and during any necessary clinical visits until 2021. These samples were analyzed to discover various key factors of organ rejection (non-invasive biomarkers), including specific antibodies related to Human-Leukocyte-Antigens (HLA), immune responses from peripheral blood mononuclear cells, non-HLA antibodies, and messenger RNA expression in both blood and biopsy tissues. This comprehensive analysis resulted in the creation of the EU-TRAIN database.

The data analysis focused on identifying variability among patients and developing predictive models for transplant rejection. The initial risk stratification system, which used standard clinical parameters alongside non-invasive biomarkers studied in the first trial, found that these biomarkers did not significantly enhance rejection predictions compared to standard of care alone. Consequently, donor-derived cell-free DNA (dd-cfDNA) was integrated into the predictive algorithm as a new potential non-invasive biomarker for further testing in the subsequent randomized controlled trial, named "Impact." The Impact study utilised the EU-TRACER algorithm based on dd-cfDNA levels in the blood at three months post-transplant to guide biopsy decisions. Additionally, patient-reported quality of life outcomes were monitored through health utility indices at various time points.

A clinical decision support system known as EU-TRACER-hom-ics was developed. This innovative system comprises three essential components: a user-friendly interface for clinicians, a computational integrator for data processing, and a secure distributed database for data storage. It allows for seamless integration of multiple data sources without manual entry, ensuring efficient data flow and high security. The EU-TRACER website was also enhanced to facilitate data reporting for clinicians and patients.

The Project Management Team oversaw financial management, deliverables, and communication with the European Commission. Throughout the last reporting period, the team guided the periodic report, organized a review meeting in October 2024, and ensured adherence to all relevant regulatory requirements.

Moreover, the consortium actively promoted non-invasive diagnostic tools for transplant rejection, including a final workshop in June 2024, fostering collaboration with patient associations and industry stakeholders.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The EU-TRAIN project has made significant strides in improving kidney transplant monitoring by addressing the lack of effective tools to predict rejection and manage patient care.
Several significant findings were achieved, bringing new hope for better patient outcomes and less invasive methods.
1. Cell-free DNA (dd-cfDNA). One of the project’s landmark achievements has been the validation of the dd-cfDNA in the EU-TRAIN Impact trial as biomarker for kidney transplant rejection detection without needing a biopsy. This test, will soon be available as a commercial diagnostic tool, offering a safer alternative to traditional biopsy procedures.
2. Cytomegalovirus (CMV) Immune-Risk Score: The project has also introduced a method to assess the risk of cytomegalovirus infection in transplant patients. By evaluating CMV-specific immune cells, this test enables healthcare providers to identify and effectively manage patients at higher risk of infection, thus improving patient safety and care.
3. Natural Killer T-cells and Cytomegalovirus (CMV) Protection: Using advanced technology, the project identified Natural Killer T-cells as crucial immune components that offer protection against CMV infection in kidney transplant recipients. This discovery presents new avenues for enhancing transplant care by focusing on the immune response.
4. Monitoring Key Immune Cells: The team developed methods to track specific immune cells, such as donor-reactive T (DRT) cells and memory B cells (mBCs), both before and after transplantation. This helps monitor patients for early signs of rejection, enabling timely medical intervention.
5. Automated Kidney Biopsy Analysis: An innovative, automated system for analyzing kidney biopsy samples was tested. This system uses artificial intelligence to classify biopsy results with high accuracy, reducing human error. This tool will be commercialized to improve transplant evaluations.
6. Distributing Computing and Data Analysis for Kidney Transplant Prediction: This project developed a GDPR-compliant distributed computing infrastructure for real-time data analysis and predictive modeling in kidney transplantation. This innovative approach balances comprehensive data analysis with patient privacy, advancing precision medicine in kidney transplantation.

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