ROCKET

ROCKET

Reclassification using OmiCs integration in KidnEy Transplantation (ROCKET)

Contact

Prof. Dr. med. Dr.-Ing. Michael Marschollek

Project partners

Hannover Medical School, Germany
TU Dresden, Germany
KU Leuven, Belgium
INSERM, Paris, France
Limoges University, France

Funding

ERACoSysMed JTC-2

Summary

Kidney transplantation is the optimal treatment for patients with end-stage renal disease (ESD) as it improves quality of life, socio-economic rehabilitation and health, compared to dialysis. Less than 1/3 of ESD patients live with a functioning transplant, due to organ shortage and limited graft survival. The challenge is to prolong graft survival but timely recognition and reliable diagnosis of the multifactorial causes of graft damage is difficult. With current standards of care (monitoring of graft function, biopsy upon impairment), disturbances of the graft are discovered too late and even histology on biopsies leaves many cases unclear. Omics approaches have been explored to define molecular markers for distinct graft injuries and to decipher the underlying pathomechanisms but validation and implementation into the clinic is lacking.

With two large ongoing studies, we will have a repository of >2000 patients at the beginning of 2018, with clinical, histological, and molecular data from different omics platforms (mRNA, miRNA, proteins, peptides in urine and blood), including longitudinal samples. For this project, we will integrate all data into a database. We will establish and validate molecular marker sets for all relevant disease entities of the graft. Using systems biology approaches, prototypical and overlapping immune and non-immune pathways will be reconstructed and modelled from omics data. Reconstructed pathways will be integrated as prior information in a state-of-the-art Bayesian machine learning framework to re-classify the entities and to classify cases that are less-well defined by histology. Graft function, response to treatments, remission or progression of disease will be incorporated to obtain dynamic models and prediction. This approach will refine current standards of diagnosis, increase diagnostic accuracy, improve the understanding of the complexity and pathomechanisms of graft damage and finally, will help to build an expert system based on non-invasive markers for prediction and medical decision making on a personalized level.

Principal investigator is Prof. Dr. Wilfried Gwinner from the Clinic for Nephrology at Hannover Medical School. PLRI contibutes works in the area of model based data management.

Duration

09/2018 - 08/2021

Staff