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

基本信息

批准号：
10193881
负责人：
PAUL KULESA
金额：
$ 16.5万
依托单位：
STOWERS INSTITUTE FOR MEDICAL RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10193881
关键词：
Age Algorithms Apoptosis Artificial Intelligence Brain-Derived Neurotrophic Factor Cancerous Chemotherapy and/or radiation Child Childhood Chromosomes Classification Clinical Computer Models Data Databases Development Developmental Disabilities Developmental Gene Diagnostic Disease Disease Outcome Disease Progression Future Genes Genomics Goals Image In Vitro Individual Infant Laboratories Lead Ligands MYCN gene Malignant Childhood Neoplasm Malignant Neoplasms Modeling Molecular Nature Neuroblastoma Neurons Outcome Output Pathogenesis Patient risk Patient-Focused Outcomes Patients Pediatric Research Predictive Value Prognostic Marker Protein Tyrosine Kinase Proto-Oncogenes Publishing Quality of life Receptor Protein-Tyrosine Kinases Regulation Research Role Running Sampling Signal Transduction Solid Speed Staging System Sympathetic Nervous System Testing Tumor Markers Uncertainty angiogenesis base cell motility chemotherapy clinical decision support clinical decision-making experimental study high risk improved in silico in vivo intelligent algorithm models and simulation nervous system development new therapeutic target novel outcome prediction overtreatment patient subsets personalized predictions population based predictive modeling prognostic prognostic tool receptor risk variant surgical risk targeted treatment tool treatment optimization treatment strategy tumor

项目摘要

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.

项目概要目前还没有诊断工具可以准确预测小儿神经母细胞瘤疾病的结果基于疾病的机制性质和儿童肿瘤的基因组信息。交感神经系统 (SNS) 的一种实体癌性肿瘤，占所有癌症的一半 1岁以下的婴儿疾病进展轨迹的不确定性导致了攻击性。放疗和化疗通常会导致儿童长期发育障碍。确定神经母细胞瘤发生的关键驱动因素并评估其相互作用个别儿童将有助于靶向化疗并限制过度治疗，这可能导致增加生活质量和婴儿存活率。我们解决这个问题的方法是开发预测人工智能算法（PredictNeuroB）并使用来自儿童肿瘤的基因组输入，以测试其预测能力，以预测疾病进展，识别关键疾病驱动因素并将结果与当前基于临床统计的算法进行比较。受体酪氨酸激酶（RTK）发育信号的网络相互作用，并得到我们的支持发现 trkB 及其配体脑源性神经营养因子 (BDNF) 在 SNS 过程中的关键作用我们发布的模型对早期神经母细胞瘤（针对 0-2 岁的婴儿）的预测。 77 名儿童的基因组信息比任何当前的临床预后都更准确（Kasemeier-Kulesa 等，2017）。 al., 2018）在这项研究中，我们建议加强我们的模型对更广泛类别的预测能力。通过应用 Gabriella Miller Kids 获取患者数据（年龄、疾病阶段、染色体状态、MYCN 扩增）此外，我们将在计算机上对算法进行扰动来确定。在我们的研究结论中，通过使用能够改变神经母细胞瘤结果状态的关键驱动因素。更大的患者衍生数据以及相关的临床和疾病结果信息，我们期望我们的 PredictNeuroB 模型将证明对广泛的神经母细胞瘤患者具有高度预测性并支持疾病治疗和靶向药物治疗的临床决策。