Biomarkers in Heart Transplantation

Heart transplantation (HTx) continues to be the main therapeutic solution for patients with end-stage heart failure. After surgery, these patients are sensitive to several threats, such as cardiac allograft rejection and infections. Endomyocardial biopsy (EMB), the gold standard for rejection monitoring, shows limitations, such as invasiveness and interobserver variability. I exploited the potential of new biomarkers in HTx to overcome these limits. I aimed to assess two unmet needs: validate a new flow cytometry diagnostic workflow combined with an Artificial Intelligence algorithm for Extracellular Vesicles (EV) surface antigen profiling as a non-invasive approach for an innovative Acute Cellular Rejection (ACR) monitoring platform; characterize the transcriptomic profile of Cytomegalovirus (CMV) infection on EMB to improve its interpretation. For the first study, we implemented a new flow cytometry workflow based on a membrane-sensing peptide capture system to assess the expression of 14 inflammatory EV surface markers. We compared the performance of our system with a commercial kit for EV profiling, and it revealed good reliability and comparable performance. Then, the analysis of our selected 14 markers showed that EV antigen profiling is a robust tool for differentiating patients with severe ACR from non-rejecting patients. In our longitudinal study, we stratified our samples based on time-point. We highlighted that some of our markers increase before the rejection episodes occur (pre-R1 and pre-R2/3). Then, we implemented a machine learning algorithm to perform an adaptive approach, considering every single patient’s EV profiling separately and defining a personalized threshold for rejection. This confirmed the potential predictive value of EV in rejection monitoring with high-quality diagnostic performance: AUC of 0.968 (95% CI 0.948-0.988), accuracy of 93.3%, specificity of 95%, and sensitivity of 75%. We demonstrated that EV surface profiling is a robust companion tool for rejection monitoring, leading to a possible reduction in EMB performed. Moreover, the AI algorithms' inclusion in clinical practice can be useful for physicians in decision-making and personalized therapy adjustment. The second study assessed the transcriptomic profile of CMV infection in cardiac transplanted patients. We profiled, using a microarray platform, mRNA and miRNA on EMBs of 16 patients, divided into a control, a rejection, and an infection group. Focusing on infection vs rejection comparison, we reviewed the role of the 18 differentially expressed mRNAs and the 12 miRNAs with the most significative p-value. We explored the regulatory effects of these miRNAs on the mRNA pathways independently identified in the same samples. Notably, mir-93-5p and mir-345-5p, upregulated in rejection, regulate several genes involved in IL7R and GZMK pathways, which emerged as crucial in CMV infection profiling. These interactions impact cell proliferation, apoptosis, T-cell differentiation, and endothelial activation. In conclusion, we have defined a distinctive molecular profile for infection in post-HTx follow-up, that reveals a key role of IL7R and GZMK in distinguishing infection from ACR. Combining these genes and these miRNA analyses could be effective for discriminating CMV infection from rejection in EMB. In summary, I demonstrated that EV profiling is a robust tool in rejection monitoring, with a predictive value that could be exploited in anticipating rejection diagnosis. Furthermore, I showed that combining mRNA and miRNA analysis can be useful in distinguishing CMV infection in EMB samples. My results highlighted the complexity of rejection pathophysiology, and how this impacts biomarkers discovery research. Besides the limitations, these studies open new opportunities to improve cardiac transplanted patients’ management, reduce the impact of invasive procedures, and ameliorate the therapy posology based on single patient characteristics.