# Application of Gabriella Miller Kids First Pediatric Research Data to a Predictive Model of Neuroblastoma

> **NIH NIH R03** · UNIVERSITY OF NOTRE DAME · 2022 · $156,500

## Abstract

Project Summary
There is currently no diagnostic tool to accurately predict pediatric neuroblastoma disease outcome that is
based on the mechanistic nature of the disease and genomic information of a child’s tumor. Neuroblastoma is
a solid, cancerous tumor of the sympathetic nervous system (SNS) that accounts for half of all cancers in
infants younger than 1 year. Uncertainties in the trajectory of disease progression has led to aggressive
radiation and chemotherapy treatments that often result in long-term developmental disabilities for children.
Determination of the critical drivers of neuroblastoma initiation and assessment of their interactions for an
individual child would help target chemotherapy and limit over-treatment, possibly resulting in an increased
quality of life and infant survival.
Our solution to this problem is to develop a predictive artificial intelligence algorithm (PredictNeuroB) and use
genomic input from a child’s tumor to test its predictive strengths to predict disease progression, identify critical
disease drivers and compare results to current clinical statistical-based algorithms. PredictNeuroB is based on
the network interactions of receptor tyrosine kinase (RTK) developmental signals and is supported by our
discovery of a critical role for trkB and its ligand brain-derived neurotrophic factor (BDNF) during SNS
development. Our published model’s prediction of early stage neuroblastoma (for infants 0-2yrs old) using
genomic information of 77 children is more accurate than any current clinical prognostic (Kasemeier-Kulesa et
al., 2018). In this study, we propose to strengthen the predictive capability of our model for a broader class of
patient data (age, stage of disease, chromosome status, MYCN amplification) by applying Gabriella Miller Kids
First neuroblastoma databases. Further, we will perform in silico perturbations of the algorithm to determine
critical drivers capable of altering neuroblastoma outcome states. At the conclusion of our study, by using a
larger set of patient-derived data with associated clinical and disease outcome information, we expect our
PredictNeuroB model will prove highly predictive for a broad class of neuroblastoma patients and support
clinical decision making in disease treatment and targeted drug therapies.

## Key facts

- **NIH application ID:** 10757183
- **Project number:** 7R03HD105079-03
- **Recipient organization:** UNIVERSITY OF NOTRE DAME
- **Principal Investigator:** PAUL KULESA
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $156,500
- **Award type:** 7
- **Project period:** 2022-12-23 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10757183

## Citation

> US National Institutes of Health, RePORTER application 10757183, Application of Gabriella Miller Kids First Pediatric Research Data to a Predictive Model of Neuroblastoma (7R03HD105079-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10757183. Licensed CC0.

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