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

Gabriella Miller Kids First 儿科研究数据在神经母细胞瘤预测模型中的应用

• 批准号: 10757183
• 负责人: PAUL KULESA
• 金额: $ 15.65万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-23 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10757183
• 关键词: Application; Gabriella; Miller; Kids; First

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.

项目总结