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

> **NIH NIH F31** · EMORY UNIVERSITY · 2020 · $45,520

## Abstract

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.

## Key facts

- **NIH application ID:** 10068941
- **Project number:** 1F31HL154725-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Jessica Reggan Hoffman
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $45,520
- **Award type:** 1
- **Project period:** 2020-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10068941

## Citation

> US National Institutes of Health, RePORTER application 10068941, Computational modeling of stem cells predicts clinical trial outcomes for hypoplastic left heart syndrome (1F31HL154725-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10068941. Licensed CC0.

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