Precision Risk Stratification in Kidney Transplant Patients - EU-TRAIN

Kidney Transplantation Clinical Trial

— EU-TRAIN  

Official title:

The EUropean TRAnsplantation and INnovation Consortium for Risk Stratification in Kidney Transplant Patients

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03652402
• Other study ID #: K170203J
• Secondary ID: IDRCB 2018-A0073
• Status: Completed
• Start date: November 27, 2018
• Est. completion date: October 28, 2021

Study information

• Verified date: June 2024
• Source: Assistance Publique - Hôpitaux de Paris
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Main objective: To design a precision risk stratification system that predicts individual risk of rejection

Description:

Allograft rejection is still a major threat to allografts with thousands of allografts failing every year worldwide due to organ rejection, with immediate consequences for the patients in terms of mortality and morbidity. With a prevalence around 20% in the first-year post transplant, rejection also carries high economic impact representing a 72% increase in transplantation price and downstream cost because of return on dialysis (60,000€ health care increase per patient). The rough estimates from 200,000 kidney allograft failures per year equals to 12 billion extra cost for the health care system per year.

Various explanations may be involved: 1) The current stratification system relies on various elements of the follow up of standard of care after kidney transplantation (histology, immunology) taken separately and, 2) Current therapeutic strategy is a "one fit for all" approach. Literature data show that therapeutics are not individualized, with 98% of patients having the same immunosuppressive regimens without analysis of response to therapy; 3) Lack of integration of omics in the stratification process. Today, the only approaches used to monitor the advent of immune-mediated allograft damage are nonspecific markers such as serum creatinine level and proteinuria which are not integrated in a dynamic approach. Some kidney transplant programs have implemented surveillance allograft biopsies, but they lack specificity and sensitivity and do not provide etiopathology of the underlying process. This impairs the risk stratification process.

In this project, leading European scientific teams, which have created relevant population cohorts and expertise, have joined forces to allow for large-scale (>5,000 patients) risk prediction studies in the field of kidney transplantation. The overall goal of the EUropean TRAnsplantation and INnovation consortium (EU-TRAIN) is to prevent kidney allograft failure and improve allograft survival by informing clinical decision and delivering optimised interventions to patients at individual level. The project aims to improve the current gold standard for risk stratification and prognosis among kidney transplant recipients.

The members of the EU-TRAIN consortium have invested heavily in the last decade to create large highly detailed European kidney transplant cohorts and to achieve best level scientific expertise in the assessment of innovative biomarkers and rejection reclassification on the basis of disease mechanism using gene expression. Ground-breaking concrete results have already been obtained that have changed patient care and transplant medicine guidelines: This is underlined by highly cited publications in the leading specialised journals and also in journals aimed at a popular audience that underlines the systemic nature of this approach, (NEJM (n=10), Lancet (n=2), BMJ (n=1), JASN (n=18)). Using this approach, the investigators have recently identified new forms of allograft rejection, resulting in changes in the most recent international allograft Banff classification and reclassifying rejection diagnosis and disease stage. This research strategy also led to recently demonstrate the clinical relevance of new non-invasive biomarkers for defining the pathogenicity of anti-HLA antibodies and allograft loss risk assessment and incorporate gene expression measurements in allograft rejection risk stratification.

The EU-TRAIN project will further elevate these cohorts synergistically by adding data on novel biomarkers, so far underdeveloped in kidney transplant research, in particular genomics and immunological data. A comprehensive integration strategy of these exceptionally large and complete cohorts constitutes a quantum leap in transplant research, and offers a unique opportunity, out of reach so far, to design strategies for truly personalised medicine.

The expected benefit for participants and society will be to reduce the financial burden of graft rejection for society.

500 new transplanted patients in the 7 clinical transplant sites will be included in the prospective multicentre EU-TRAIN cohort with centralised analysis of samples in CHUN (blood mRNA), ICS (blood cellular assays), Charité (non-HLA antibodies and blood endothelial targets), AP-HP (blood anti-HLA DSA), and INSERM (Biopsy mRNA).

Vulnerable participants excluded.

Schedule for the study:

• inclusion period: 12 months

• participation period (treatment - follow-up): 12 months

• total duration of the study: 24 months

Exclusion period for participation in other studies, and justification: the participation to other minimal risks and constraints studies and observational non-interventional studies is allowed during this study. There is no exclusion period at the end of study. The participation to other interventional and observational non-interventional studies is allowed after the end of the study.

Number of enrolments expected per site and per month :

• Necker Hospital, Paris: 10 patients / month

• Saint-Louis Hospital, Paris: 6 patients / month

• CHU Nantes: 10 patients / month

• Charité-Universitätsmedizin, Campus Virchow Klinikum, Berlin: 4 patients / month

• Charité-Universitätsmedizin, Campus Mitte, Berlin: 3 patients / month

• Bellvitge University Hospital, Barcelona: 7 patients / month

• Geneva University Hospitals, Geneva: 2 patients / month

• Vall d'Hebron Hospital, Barcelona : 6 patients / month

• Kremlin Bicêtre Hospital, Paris : 4 patients / month

Recruitment information / eligibility

• Status: Completed
• Enrollment: 558
• Est. completion date: October 28, 2021
• Est. primary completion date: October 28, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 100 Years

Eligibility

Inclusion Criteria:

• Men and women, Age = 18 years old at the time of transplantation.

• Patients receiving a living or deceased donor kidney allograft.

• Patients who signed the informed consent form and willing to comply with study procedures.

• Female patients of child-bearing potential must have a negative pregnancy test (serum beta-hCG) and must be practicing an effective, reliable and medically approved contraceptive regimen

Exclusion Criteria:

• History of multi-organ transplant (interference with rejection natural history).

• Participant is unable or unwilling to comply with study procedures (including foreign language speakers who are not assisted by a native language speaker).

• Vulnerable participants (minors, protected adults, legally detained)

Related Conditions & MeSH terms

• Kidney Transplant Rejection
• Kidney Transplantation
• Prognosis
• Risk Stratification

Intervention

Procedure:
• kidney transplantation
samples additional to the standard of care' (SOC)will be taken at each visit (Day 0, Month 3, Month12, clinical indication)

Locations

Country	Name	City	State
France	Hôpital du Kremlin Bicêtre	Le Kremlin-Bicêtre	Paris
France	CHU Nantes	Nantes
France	Hôpital Necker	Paris
France	Hopital Saint Louis	Paris	Ile De France
Germany	Hospital La Charité Campus Virchow	Berlin
Germany	Hospital La Charité	Berlin-Mitte	Berlin
Spain	Hospital Bellvitge	Barcelona
Spain	Hospital Vall d'Hebron	Barcelona
Switzerland	Hôpitaux Universitaires de Genève	Geneva

Sponsors (1)

Lead Sponsor	Collaborator
Assistance Publique - Hôpitaux de Paris

Countries where clinical trial is conducted

France,  Germany,  Spain,  Switzerland, 

References & Publications (15)

Ba R, Geffard E, Douillard V, Simon F, Mesnard L, Vince N, Gourraud PA, Limou S. Surfing the Big Data Wave: Omics Data Challenges in Transplantation. Transplantation. 2022 Feb 1;106(2):e114-e125. doi: 10.1097/TP.0000000000003992. — View Citation

Brinas F, Danger R, Brouard S. TCL1A, B Cell Regulation and Tolerance in Renal Transplantation. Cells. 2021 Jun 1;10(6):1367. doi: 10.3390/cells10061367. — View Citation

Danger R, Feseha Y, Brouard S. The Pseudokinase TRIB1 in Immune Cells and Associated Disorders. Cancers (Basel). 2022 Feb 17;14(4):1011. doi: 10.3390/cancers14041011. — View Citation

Danger R, Le Berre L, Cadoux M, Kerleau C, Papuchon E, Mai HL, Nguyen TV, Guerif P, Morelon E, Thaunat O, Legendre C, Anglicheau D, Lefaucheur C, Couzi L, Del Bello A, Kamar N, Le Quintrec M, Goutaudier V, Renaudin K, Giral M, Brouard S; DIVAT Consortium. — View Citation

Danger R, Moiteaux Q, Feseha Y, Geffard E, Ramstein G, Brouard S. FaDA: A web application for regular laboratory data analyses. PLoS One. 2021 Dec 20;16(12):e0261083. doi: 10.1371/journal.pone.0261083. eCollection 2021. — View Citation

Divard G, Raynaud M, Tatapudi VS, Abdalla B, Bailly E, Assayag M, Binois Y, Cohen R, Zhang H, Ulloa C, Linhares K, Tedesco HS, Legendre C, Jouven X, Montgomery RA, Lefaucheur C, Aubert O, Loupy A. Comparison of artificial intelligence and human-based pred — View Citation

Ed-Driouch C, Cheneau F, Simon F, Pasquier G, Combes B, Kerbrat A, Le Page E, Limou S, Vince N, Laplaud DA, Mars F, Dumas C, Edan G, Gourraud PA. Multiple sclerosis clinical decision support system based on projection to reference datasets. Ann Clin Trans — View Citation

Ed-Driouch C, Mars F, Gourraud PA, Dumas C. Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence. Sensors (Basel). 2022 Oct 29;22(21):8313. doi: 10. — View Citation

Girardin FR, Cohen K, Schwenkglenks M, Durand-Zaleski I. Editorial: Pharmacoeconomics in the era of health technology assessment and outcomes research to prioritize resource use, innovation and investment. Front Pharmacol. 2023 May 16;14:1210002. doi: 10. — View Citation

Girardin FR, Nicolet A, Bestard O, Lefaucheur C, Budde K, Halleck F, Brouard S, Giral M, Gourraud PA, Horcholle B, Villard J, Marti J, Loupy A. Immunosuppressant drugs and quality-of-life outcomes in kidney transplant recipients: An international cohort s — View Citation

Kerouac S, Taggart ME, Lescop J, Fortin MF. Dimensions of health in violent families. Health Care Women Int. 1986;7(6):413-26. doi: 10.1080/07399338609515756. No abstract available. — View Citation

Massart A, Danger R, Olsen C, Emond MJ, Viklicky O, Jacquemin V, Soblet J, Duerinckx S, Croes D, Perazzolo C, Hruba P, Daneels D, Caljon B, Sever MS, Pascual J, Miglinas M; Renal Tolerance Investigators; Pirson I, Ghisdal L, Smits G, Giral M, Abramowicz D — View Citation

Montero N, Farouk S, Gandolfini I, Crespo E, Jarque M, Meneghini M, Torija A, Maggiore U, Cravedi P, Bestard O. Pretransplant Donor-specific IFNgamma ELISPOT as a Predictor of Graft Rejection: A Diagnostic Test Accuracy Meta-analysis. Transplant Direct. 2 — View Citation

Tripathi N, Danger R, Chesneau M, Brouard S, Laurent AD. Structural insights into the catalytic mechanism of granzyme B upon substrate and inhibitor binding. J Mol Graph Model. 2022 Jul;114:108167. doi: 10.1016/j.jmgm.2022.108167. Epub 2022 Mar 22. — View Citation

Yoo D, Goutaudier V, Divard G, Gueguen J, Astor BC, Aubert O, Raynaud M, Demir Z, Hogan J, Weng P, Smith J, Garro R, Warady BA, Zahr RS, Sablik M, Twombley K, Couzi L, Berney T, Boyer O, Duong-Van-Huyen JP, Giral M, Alsadi A, Gourraud PA, Morelon E, Le Qu — View Citation

* Note: There are 15 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Capacity of non-invasive biomarkers and intragraft gene expression profiles combined to standard of care data (HLA system, clinical and biological data)	Prognosis value of non-invasive biomarkers and intragraft gene expression profile changes combined to standard of care data changes (HLA system, clinical and biological data), to identify high versus low risk profiles of rejection as measured by DSA characteristics (Donor-Specific Antibody) by Luminex single antigen assay and non DSA characteristics by functional in vitro assay on endothelial targets, alloreactive T and B cells profiles by ELISPOT, blood mRNA expression by NanoString technologies and gene expression on DNA chips.	Day 0, Month 3, Month 12, clinical indication over 12 months
Secondary	Correlation of blood biomarkers concentration with allograft rejection (rejection assessed by histopathology)	Blood biomarkers measured: HLA and non-HLA DSA characteristics (by Luminex Single Antigen and functional in vitro assay on endothelial targets), alloreactive T and B cell profiles (by ELISPOT) and candidate gene profiles by NanoString technologies: AKR1C3, CD40, CTLA4, ID3, MZB1, TCL1A, TRIB1, TLR4 TUBA4A, WHAZ, CD3E, CD8A, CD4, MS4A1, FOXP3, GZMB, ENTPD1, POU2AAF1, POU2F1, CD9, IL7R, BLK, MMP9, CXCL9, CXCL10, CXCL11, UPK1A, TGFB1, IL2RA, PRF1, TIMP1, PAI1, FN1, TIGIT and 4 reference genes: HPRT1, B2M, GAPDH and ACTB	Day 0, Month 3, Month12, clinical indication over 12 months
Secondary	Correlation of blood biomarker concentrations with allograft function (function measured by the Glomerular Filtration Rate (GFR)).	Blood biomarkers measured: HLA and non-HLA DSA characteristics (by Luminex Single Antigen and functional in vitro assay on endothelial targets), alloreactive T and B cell profiles (by ELISPOT) and candidate gene profiles by NanoString technologies: AKR1C3, CD40, CTLA4, ID3, MZB1, TCL1A, TRIB1, TLR4 TUBA4A, WHAZ, CD3E, CD8A, CD4, MS4A1, FOXP3, GZMB, ENTPD1, POU2AAF1, POU2F1, CD9, IL7R, BLK, MMP9, CXCL9, CXCL10, CXCL11, UPK1A, TGFB1, IL2RA, PRF1, TIMP1, PAI1, FN1, TIGIT and 4 reference genes: HPRT1, B2M, GAPDH and ACTB	Day 0, Month 3, Month12, clinical indication over 12 months
Secondary	Assessment of changes in biomarker levels in serial measurements and association with allograft function (function measured by the GFR)	Blood biomarkers measured: HLA and non-HLA DSA characteristics (by Luminex Single Antigen and functional in vitro assay on endothelial targets), alloreactive T and B cell profiles (by ELISPOT) and candidate gene profiles by NanoString technologies: AKR1C3, CD40, CTLA4, ID3, MZB1, TCL1A, TRIB1, TLR4 TUBA4A, WHAZ, CD3E, CD8A, CD4, MS4A1, FOXP3, GZMB, ENTPD1, POU2AAF1, POU2F1, CD9, IL7R, BLK, MMP9, CXCL9, CXCL10, CXCL11, UPK1A, TGFB1, IL2RA, PRF1, TIMP1, PAI1, FN1, TIGIT and 4 reference genes: HPRT1, B2M, GAPDH and ACTB Biomarkers measured in the biopsy: pangenomic approach of gene expression profiles using Affymetrix DNA chips and comparing gene expression in low- and high-risk patients	Day 0, Month 3, Month12, clinical indication over 12 months
Secondary	Correlation of gene expression in kidney allografts with allograft rejection (rejection assessed by histopathology)	Biomarkers measured in the biopsy: pangenomic approach of gene expression profiles using Affymetrix DNA chips and comparing gene expression in low- and high-risk patients	Day 0, Month 3, Month 12, clinical indication over 12 months
Secondary	Gene expression related risk stratification of response to treatment in kidney allograft rejection (rejection assessed by histopathology)	Blood biomarkers measured: Candidate gene profiles by NanoString technologies: AKR1C3, CD40, CTLA4, ID3, MZB1, TCL1A, TRIB1, TLR4 TUBA4A, WHAZ, CD3E, CD8A, CD4, MS4A1, FOXP3, GZMB, ENTPD1, POU2AAF1, POU2F1, CD9, IL7R, BLK, MMP9, CXCL9, CXCL10, CXCL11, UPK1A, TGFB1, IL2RA, PRF1, TIMP1, PAI1, FN1, TIGIT and 4 reference genes: HPRT1, B2M, GAPDH and ACTB Biomarkers measured in the biopsy: pangenomic approach of gene expression profiles using Affymetrix DNA chips and comparing gene expression in low- and high-risk patients	Day 0, Month 3, Month12, clinical indication over 12 months
Secondary	Assessment of the changes of patient's well-being across time and centres	Assessment of the changes of patient's well-being across time and centres using the results of a self health-questionnaire filled in by each patient at each time point. All items are measured on a scale of 1 to 3. The first 3 items measure mobility, self-care and performance of usual activities, with higher values indicating lower mobility and unability to take care of one's self or to perform usual activities, respectively. The 4th item measures pain and discomfort, with higher values indicating extreme pain or discomfort. The last item measures anxiety and depression, with higher values indicating extreme anxiety or depression. A general self-reported health state measures patient's opinion about his/her own health on a scale of 0 to 100, with a 0 score as the worst state and 100 as the best state.	Day 0, Month 3, Month12, clinical indication over 12 months

External Links

•   Distributing human leukocyte antigen HLA database in histocompatibility: a shift in HLA data governance
•   Secure Distribution of Factor Analysis of Mixed Data (FAMD) and Its Application to Personalized Medicine of Transplanted Patients