Computational modeling of stem cells predicts clinical trial outcomes for hypoplastic left heart syndrome

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PROJECT SUMMARY/ABSTRACT Congenital heart defects affect an estimated 8 in 1000 births in the US annually. One complex form of congenital heart defects, hypoplastic left heart syndrome (HLHS) is palliated by a series of three surgeries which demands the right ventricle sustain systemic circulation. Despite improved outcomes, HLHS mortality remains high due to right ventricular dysfunction/failure and transplant remains the only curative option. Considering concerns over transplant availability and rejection, stem cells which trigger endogenous repair mechanisms have become an attractive candidate for treating HLHS. Currently, our group is involved in two of the three stem cell clinical trials for HLHS in the US, investigating the use of bone marrow derived- mesenchymal stem cells (MSCs) and cardiac ckit+ progenitor cells (CPCs). However, despite some successes and demonstrated safety in preclinical and clinical trials, large variation in stem cell populations and patient outcomes remains a critical problem. Furthermore, there is a lack of quantitative studies investigating these discrepancies. In this proposal, we will take a system-biology approach to understand the biological molecules, or signals, underlying the reparative effects of stem cells and their paracrine signaling exosomes (30-150nm vesicles containing diverse cargo). We have shown previously that (1) treatment with CPCs and their exosomes produce pro-angiogenic and anti-fibrotic responses in vitro and in vivo, and (2) these responses can be predicted by modeling cellular and exosomal expression patterns using partial least squares regression (PLSR). Considering our involvement in HLHS stem cell clinical trials, we will expand our previous efforts to build a computational model of stem cell content, capable of predicting patient improvements. We will train our model with CPC and CPC exosome sequencing and mass spectrometry (MS) data from our lab’s bank of 44 CPC lines (previously isolated from cardiac biopsies of patients with congenital heart defects). Then, we will sequence and perform MS of the MSCs, CPCs, and their exosomes from the two clinical trials and input these data into the in vitro trained model. We expect our model to predict patient improvements from the clinical trials. Overall, our model will not only generate a predictive, clinical tool, but also identify co-varying signals directly related to these reparative responses for further investigation. In creating a robust, generalizable model, we will gain mechanistic insight of cardiac repair and provide a valuable clinical tool for pediatric stem cell trials. Ultimately, this work will be helpful in providing the best treatment strategies for children with congenital heart defects, like HLHS.