Improving the contribution of histopathology in kidney diseases From diagnosis to prognosis

Although the renal biopsy still remains the gold standard for diagnostics in renal disease (after kidney transplantation), we have shown that technical limitations may challenge this diagnostic process. We have also shown that using granular cohorts of patients with extensive clinical, histological and immunological data, we could probabilistically subclassify patients into distinct clinicopathological groups that allowed for stratification according to the risk of allograft loss in different clinical scenarios. In the era of big data, digitization of electronic health records, standardized data collection methodologies and the various molecular analyses, there will be new challenges and opportunities to further advance the multidisciplinary care of our renal transplant patients. Future work should focus on integrating data sources from different subspecialties that are responsible for the care of renal transplant recipients (nephrology, immunology, pathology). This will be further completed by the integration of molecular and, more generally, omics data (transcriptomics, proteomics). Using data science expertise and machine learning will foster the development of combined diagnostic and risk stratification systems that can be further used in the design of next-generation clinical trials in order to bring novel therapies in the field.