Recruitment

Recruitment Status
Not yet recruiting
Estimated Enrollment
Same as current

Summary

Conditions
  • Graft Rejection
  • Humoral Immunity
  • Kidney Transplantation
  • Proteomics
Type
Interventional
Phase
Not Applicable
Design
Allocation: N/AIntervention Model: Single Group AssignmentMasking: None (Open Label)Primary Purpose: Diagnostic

Participation Requirements

Age
Between 18 years and 125 years
Gender
Both males and females

Description

Antibody-mediated rejection (ABMR) is due to pathogenic antibodies produced by the donor (donor-specific antibodies, DSA) that are directed against Human Leukocyte Antigens (HLA) or other antigens (non HLA) of the graft. ABMR is currently the leading cause of long-term kidney allograft failure. Hist...

Antibody-mediated rejection (ABMR) is due to pathogenic antibodies produced by the donor (donor-specific antibodies, DSA) that are directed against Human Leukocyte Antigens (HLA) or other antigens (non HLA) of the graft. ABMR is currently the leading cause of long-term kidney allograft failure. Histological lesions of microvascular inflammation (MVI) are the hallmark criteria of ABMR according to the 2019 Banff classification. Lack of reproducibility in the scoring of MVI by pathologists is still an issue of the diagnosis of ABMR in routine practice, while the understood pathophysiological mechanisms of MVI (anti-HLA DSA, DSA against non HLA antigens and/or NK cell-mediated) are poorly assessed in practice, possibly explaining the wide variability of treatment efficacy. In a prior study, the investigators confirmed the value of mass spectrometry for the analysis of the glomerular proteome during ABMR, compared to the one of stable grafts, from FFPE biopsies. The investigators identified 82 proteins, particularly involved in leukocyte activation and the interferons pathways, in accordance with transcriptomic approaches. Five proteins were validated by immunohistochemistry. The investigators now propose to analyze kidney allograft FFPE biopsies of 92 patients by mass spectrometry, including 32 with MVI (with and without anti-HLA DSA) and 60 with relevant differential diagnoses. The main objective is to assess the diagnostic performances of tissue proteic signatures designed by machine-learning methods for the diagnosis of microvascular inflammation, the reference standard being the 2019 Banff classification. One of the secondary objectives includes the comparison of the protein profile of MVI with and without anti-HLA DSA, but also the proteomic analysis of 60 urine samples from the same population, in order to assess the performances of mass spectrometry in the non-invasive diagnosis of MVI in kidney transplantation.

Tracking Information

NCT #
NCT04851145
Collaborators
Not Provided
Investigators
Not Provided