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: 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
• 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

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.

项目摘要 目前还没有诊断工具来准确预测小儿神经母细胞瘤疾病的结果， 基于疾病的机制本质和儿童肿瘤的基因组信息。神经母细胞瘤是 一种交感神经系统（SNS）的实体癌性肿瘤，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侵袭性的 放疗和化疗往往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， 个别儿童将有助于有针对性地进行化疗，并限制过度治疗， 生活质量和婴儿存活率。 我们对这个问题的解决方案是开发一种预测人工智能算法（PredictNeuroB），并使用 从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，确定关键 疾病驱动因素，并将结果与当前基于临床诊断的算法进行比较。PredictNeuroB基于 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中的关键作用 发展我们发表的模型预测早期神经母细胞瘤（0- 2岁的婴儿）使用 77名儿童的基因组信息比任何当前的临床预后更准确（Kasemeier-Kulesa et 例如，2018年）。在这项研究中，我们建议加强我们的模型的预测能力，为更广泛的一类， 通过应用Gabriella米勒儿童的患者数据（年龄、疾病分期、染色体状态、MYCN扩增） 第一个神经母细胞瘤数据库。此外，我们将对算法进行计算机扰动，以确定 能够改变神经母细胞瘤结局状态的关键驱动因素。在我们的研究结束时，通过使用 更大的患者来源数据集与相关的临床和疾病结局信息，我们希望我们的 PredictNeuroB模型将被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。