Our global aim is to accelerate the translation of immunological and gene expression discoveries into the clinical field by filling the gap between basic science and applied biomedical researches. We will provide mechanistically driven pathogenesis of alloantibodies from in vitro cell models to murine models transferred to their clinical consequences in large prospective human cohorts in term of allograft injury, risk of failure and therapeutic strategies. We will also determine relevant and non-invasive biomarkers and identify rejection gene expression signatures in allografts.
We are developing a personalized approach of transplant medicine that will integrate multidimensional information deriving from classical histology and biology, clinical science together with novel information coming from ground breaking technologies in immunology, molecular biology, genetics and biomarkers. The present project has the ambitious but realistic goal to provide transplant clinicians with innovative and accessible tools for early prediction of individual risk of allograft rejection and transplant loss and offering them the possibility to personalize clinical management and treatment. The final product proposed by the present project is to build an integrated diagnostic system called the “iBox”.
The iBox will permit to make correlations with the conventional features of rejection and transplant pathology, improve the diagnostic accuracy, and provide mechanistic insights into exploring new pathways and operational biological processes involved in transplant rejection. This will also set the stage for personalized transplant medicine by providing end points for clinical trials with insights for therapy and patient clinical management.
The main objectives of our approach in transplantation and the iBox generation will be:
1. Address transplant phenotypes including pathophysiology across organs.
2. Reclassify the disease states.
3. Correct the conventional diagnostic system.
4. Provide a new diagnostic system.
5. Set the stage for response to therapy and clinical trials.
6. Improve transplant outcomes.
7. Engineer a reporting system.
8. Calibrate the read-outs to get real time clinical meaning.