Periodic Reporting for period 4 - EU-TRAIN (The EUropean TRAnsplantation and INnovation (EU-TRAIN) consortium for improving diagnosis and risk stratification in kidney transplant patients)
Période du rapport: 2022-07-01 au 2023-06-30
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, representing at least 12 billion € extra costs per year.
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 aims at developing a risk stratification system in kidney transplantation (the EU-TRACER system) by integrating multidimensional data from patients’ follow-up.
The project is structured into 8 work packages (WP), each managed by a WP leader from a participating centre, 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 through meetings with partners and scientific boards (WP7) and dissemination of the results and intellectual property management (WP8).
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 aims at developing a risk stratification system in kidney transplantation (the EU-TRACER system) by integrating multidimensional data from patients’ follow-up.
The project is structured into 8 work packages (WP), each managed by a WP leader from a participating centre, 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 through meetings with partners and scientific boards (WP7) and dissemination of the results and intellectual property management (WP8).
WP1
The EU train prospective cohort, a minimal risk and constraint study, recruited 550 patients. Last patient was recruited at M30, last visit of last patient occurred at M45. After completing data entries and clearing all queries regarding wrong entry or missing data, close out visit were done in each centres. Data were transferred to P01 for cleaning and statistical analysis.
WP2
The different recruiting centers collected biological samples over the three time points (D0 M3 M12) and clinical indication visits until the end of 2021. This enabled the analytical platforms to evaluate the quality of the samples provided by each recruiting center. All the samples were tested for anti HLA-DSA, PBMCs responses, non-HLA antibody presence and gene expression in blood mRNA. mRNA expression has also been analysed in biopsy tissue. From all these samples, the EUTRAIN database was generated.
WP3
The EU-tracer application is a clinical decision support system developed for kidney transplantation. It has a 3-layered infrastructure: 1- the user interface (VISUALIZE), 2- the computation integrator (COMPUTE) and 3- the distributed database (STORE). The user interface has been designed using modern ergonomic principle of patient centricity with no manual entries of decisional data required. The EU-tracer prototype has been created and works with different data sources in Kidney transplantation. The consortium took the necessary measures to secure the data flow between the different layers.
WP4
Functional and phenotypical heterogeneity models as well as classifiers have been finalized using data from study #1 and are now fully functional. Non-invasive biomarkers from study #1 showed minimal additional value when added to the risk stratification system developed to predict allograft rejection using standard of care parameters alone (SOC model). In order to reinforce the statistical power the system, the donor-derived cell-free DNA non-invasive biomarker has thus been added to the pool of tested biomarkers in the Impact study #2.
WP5
The EU-TRACER design of the application website for data reporting to patients and physicians has been updated with clinical, IT company and research groups.
The randomized clinical trial (Impact trial) was approved by the respective IRBs of each participating sites. Patient recruitment has been completed as expected with no deviation in all sites, and patients follow-up is ongoing according to the protocol.
The EU-TRACER patient-reported outcomes to derive quality-of-life estimates was finalized. From immunosuppressant initiation to maintenance therapy, health utility indexes of kidney transplant recipients were followed and quantified using validated elicitation instruments (visual analogical scales and Euro-QoL-5 Dimensions (EQ5D)) at D0, M3, and M12.
WP6
Study documents, eCRF, flowcharts, SOP and monitoring plan necessary for the cohort study (study 1) and randomized clinical trial (study 2) implementation. Local teams were trained regarding the study, SOP, eCFR and monitoring plan for both studies. In study 1, monitoring reports were reinforced and data filling and quality check performed more often in order to solve some data quality insufficiency and finalize study 1. More regular monitoring of data and study protocol was implemented in study 2 to avoid these insufficiencies.
WP8
So far, two EU-TRAIN-ESOT symposiums have taken place during the two previous ESOT congresses, one in 2019 Copenhagen (Denmark) and the second one in 2021 in Milano (Italy). These focused symposiums not only introduced the consortium, and main objectives but hey also but they also focused on main methodological and statistical pitfalls clinical researchers can face in the field of transplantation. A statistical course was also organized with ESOT within the TLJ 3.0 ESOT winter congress in Prague, Czech Republic in 2022.
The EU train prospective cohort, a minimal risk and constraint study, recruited 550 patients. Last patient was recruited at M30, last visit of last patient occurred at M45. After completing data entries and clearing all queries regarding wrong entry or missing data, close out visit were done in each centres. Data were transferred to P01 for cleaning and statistical analysis.
WP2
The different recruiting centers collected biological samples over the three time points (D0 M3 M12) and clinical indication visits until the end of 2021. This enabled the analytical platforms to evaluate the quality of the samples provided by each recruiting center. All the samples were tested for anti HLA-DSA, PBMCs responses, non-HLA antibody presence and gene expression in blood mRNA. mRNA expression has also been analysed in biopsy tissue. From all these samples, the EUTRAIN database was generated.
WP3
The EU-tracer application is a clinical decision support system developed for kidney transplantation. It has a 3-layered infrastructure: 1- the user interface (VISUALIZE), 2- the computation integrator (COMPUTE) and 3- the distributed database (STORE). The user interface has been designed using modern ergonomic principle of patient centricity with no manual entries of decisional data required. The EU-tracer prototype has been created and works with different data sources in Kidney transplantation. The consortium took the necessary measures to secure the data flow between the different layers.
WP4
Functional and phenotypical heterogeneity models as well as classifiers have been finalized using data from study #1 and are now fully functional. Non-invasive biomarkers from study #1 showed minimal additional value when added to the risk stratification system developed to predict allograft rejection using standard of care parameters alone (SOC model). In order to reinforce the statistical power the system, the donor-derived cell-free DNA non-invasive biomarker has thus been added to the pool of tested biomarkers in the Impact study #2.
WP5
The EU-TRACER design of the application website for data reporting to patients and physicians has been updated with clinical, IT company and research groups.
The randomized clinical trial (Impact trial) was approved by the respective IRBs of each participating sites. Patient recruitment has been completed as expected with no deviation in all sites, and patients follow-up is ongoing according to the protocol.
The EU-TRACER patient-reported outcomes to derive quality-of-life estimates was finalized. From immunosuppressant initiation to maintenance therapy, health utility indexes of kidney transplant recipients were followed and quantified using validated elicitation instruments (visual analogical scales and Euro-QoL-5 Dimensions (EQ5D)) at D0, M3, and M12.
WP6
Study documents, eCRF, flowcharts, SOP and monitoring plan necessary for the cohort study (study 1) and randomized clinical trial (study 2) implementation. Local teams were trained regarding the study, SOP, eCFR and monitoring plan for both studies. In study 1, monitoring reports were reinforced and data filling and quality check performed more often in order to solve some data quality insufficiency and finalize study 1. More regular monitoring of data and study protocol was implemented in study 2 to avoid these insufficiencies.
WP8
So far, two EU-TRAIN-ESOT symposiums have taken place during the two previous ESOT congresses, one in 2019 Copenhagen (Denmark) and the second one in 2021 in Milano (Italy). These focused symposiums not only introduced the consortium, and main objectives but hey also but they also focused on main methodological and statistical pitfalls clinical researchers can face in the field of transplantation. A statistical course was also organized with ESOT within the TLJ 3.0 ESOT winter congress in Prague, Czech Republic in 2022.
The current state of the art lacks risk stratification tools in kidney transplantation. The current failure of randomised control trials evaluating interventions to treat allograft rejection relies on imperfect tools for patient inclusion in balanced arms as well as the lack of surrogate endpoints for evaluating the response to therapy and predicting long term kidney allograft outcome.
To solve limitations of the current system to evaluate the response to therapy and predict long term kidney allograft outcome, and to achieve breakthroughs with significant clinical and societal impact, it is crucial to combine kidney transplant population cohorts that are unselected, prospective and extensively phenotyped. The strength of the EU-TRAIN project will be to fully exploit the synergistic value of the data associated with this cohort by joining the complementary expertise of leading European researchers in the relevant fields.
EU-TRAIN is primed to achieve breakthroughs in numerous areas beyond the state of the art. Some examples include the following:
• Precision diagnosis
• Adapted prognostication algorithms based on combination of traditional models with multidimensional risk stratification methodology
• Next generation clinical trials and response to therapy evaluation
The potential of bringing this novel approach to guide diagnosis/prognosis of a disease process using new algorithmic tools for decision-making in patient’s follow-up is huge. Indeed, it will represent a major step forward towards implementing Personalized Medicine in kidney Transplantation while at the same time empowering patients and physicians for improving disease outcomes.
To solve limitations of the current system to evaluate the response to therapy and predict long term kidney allograft outcome, and to achieve breakthroughs with significant clinical and societal impact, it is crucial to combine kidney transplant population cohorts that are unselected, prospective and extensively phenotyped. The strength of the EU-TRAIN project will be to fully exploit the synergistic value of the data associated with this cohort by joining the complementary expertise of leading European researchers in the relevant fields.
EU-TRAIN is primed to achieve breakthroughs in numerous areas beyond the state of the art. Some examples include the following:
• Precision diagnosis
• Adapted prognostication algorithms based on combination of traditional models with multidimensional risk stratification methodology
• Next generation clinical trials and response to therapy evaluation
The potential of bringing this novel approach to guide diagnosis/prognosis of a disease process using new algorithmic tools for decision-making in patient’s follow-up is huge. Indeed, it will represent a major step forward towards implementing Personalized Medicine in kidney Transplantation while at the same time empowering patients and physicians for improving disease outcomes.