Identifying neuroblastoma drivers and bringing them to the clinic

识别神经母细胞瘤驱动因素并将其带到诊所

• 批准号: 10197505
• 负责人: STEPHEN X SKAPEK
• 金额: $ 43.74万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-02 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10197505
• 关键词: 11q; 17q; Algorithmic Analysis; Bayesian Method; Biological; Biological Markers; Biology; Biometry; Cell Line; Cells; Child; Clinic; Clinical; Clinical Trials; Clustered Regularly Interspaced Short Palindromic Repeats; Combined Modality Therapy; Communities; Computational algorithm; Copy Number Polymorphism; Data; Development; Disease; Gene Dosage; Gene Expression; Gene Expression Profiling; Genes; Genomic approach; Genomics; Goals; Leadership; Link; MYCN gene; Malignant Childhood Neoplasm; Malignant Neoplasms; Modeling; Molecular; Molecular Biology; Molecular Genetics; Mutate; Mutation; Neuroblastoma; Nucleotides; Oncogenic; Outcome; Parents; Patients; Pattern; Pediatric Oncology Group; Positioning Attribute; Pre-Clinical Model; Preclinical Testing; Proteins; Recurrence; Research; Research Personnel; Rhabdomyosarcoma; Risk; Scientist; Solid; Testing; Translating; Tumor Suppressor Proteins; Validation; Work; base; candidate validation; childhood cancer mortality; cohort; computational pipelines; disorder risk; effective therapy; functional genomics; genomic data; high risk; improved; improved outcome; innovation; insight; molecular targeted therapies; mutant; neuroblastoma cell; new therapeutic target; next generation sequencing; novel; novel strategies; patient derived xenograft model; precision medicine; programs; risk stratification; stem; success; targeted biomarker; tool; tumor; virtual; young adult

Project Summary Our overall goal is to improve outcomes for children with forms of cancer that cannot be eradicated with current therapies. We are focused on neuroblastoma, an especially challenging form of childhood cancer that accounts for a large proportion of childhood cancer deaths each year, and we have assembled a team to explore a new approach in which a novel computational pipeline is applied to existing genomics data and a functional genomics screen to reveal new insights into neuroblastoma biology. We anticipate that new biomarkers for risk stratification and assignment of molecularly targeted therapy will stem from our work. In the US each year, over 700 children and young adults develop neuroblastoma, among the most common solid malignancies in children. Sadly, the chance of cure is low for those with high-risk disease, and this bleak outlook has only modestly improved with the application of multifaceted therapies in recent years. New molecular biology and molecular genetics tools at the close of the last century brought new insights into the underpinnings of neuroblastoma, including the fact that copy-number gain in the MYCN gene is among the most important determinants of biologic risk. However, that has not been translated into better treatment and other molecular derangements contribute to poor chances of survival for children with this disease. Many clinicians, scientists, and patients and their parents anticipated that the more recent genomics revolution would usher in “precision” medicine focused on the mutant forms of proteins anticipated to drive the disease. That promise has not been fully realized in cancers like neuroblastoma that lack highly-recurrent, targetable mutations. Our team came together to explore a new approach to help close this gap. Given the few recurrent mutations in this disease, we are considering neuroblastoma to be a cancer in which normal developmental programs are corrupted by altered gene expression and that the altered gene expression is often “hard-wired” into the cell by gains and losses in the copies of the genes encoding oncogenic drivers and tumor suppressors. We exploring the capacity for a new computational algorithm to identify those cancer drivers/suppressors using existing genomic datasets. Second, we propose to use a focused but high-throughput cell-based screen to quickly provide functional validation of the candidate neuroblastoma drivers. Finally, we are using this information to develop a new biologically-based tool for assigning risk and guiding treatment assignment for children with neuroblastoma. If successful, we can extend this developmental model to other forms of childhood cancer.

项目摘要 我们的总体目标是改善目前无法根除的癌症儿童的预后。 治疗我们专注于神经母细胞瘤，这是一种特别具有挑战性的儿童癌症， 每年儿童癌症死亡的很大一部分，我们已经组建了一个团队来探索一个新的 一种新的计算管道应用于现有基因组学数据和功能基因组学的方法， 屏幕揭示了对神经母细胞瘤生物学的新见解。我们预计，用于风险分层的新生物标志物 分子靶向治疗的研究和分配将源于我们的工作。 在美国，每年有超过700名儿童和年轻人患上神经母细胞瘤，这是最常见的实体瘤之一。 儿童恶性肿瘤可悲的是，治愈的机会是低的那些高风险的疾病，这种暗淡的前景 近年来，随着多方面疗法的应用，这种情况只得到了适度的改善。新的分子生物学 和分子遗传学工具在上个世纪末带来了新的见解的基础， 神经母细胞瘤，包括MYCN基因的拷贝数增加是最重要的 生物风险的决定因素。然而，这并没有转化为更好的治疗和其他分子 神经错乱导致患这种疾病的儿童生存机会很小。很多临床医生，科学家， 病人和他们的父母预期，最近的基因组学革命将带来“精确”， 医学主要集中在预期会导致这种疾病的蛋白质的突变形式上。这一承诺并没有 在像神经母细胞瘤这样缺乏高度复发性靶向突变的癌症中完全实现。 我们的团队走到一起，探索一种新的方法来帮助缩小这一差距。考虑到少数几个周期性突变， 这种疾病，我们认为神经母细胞瘤是一种癌症，其中正常的发育程序， 被改变的基因表达所破坏，并且改变的基因表达通常通过以下方式“硬连接”到细胞中： 编码致癌驱动因子和肿瘤抑制因子的基因拷贝的获得和损失。我们在探索 一种新的计算算法的能力，以确定这些癌症的驱动器/抑制器使用现有的 基因组数据集。第二，我们建议使用一种聚焦但高通量的基于细胞的筛选， 提供候选神经母细胞瘤驱动程序的功能验证。最后，我们利用这些信息， 开发一种新的基于生物学的工具，用于为患有以下疾病的儿童分配风险和指导治疗分配 神经母细胞瘤如果成功，我们可以将这种发展模式扩展到其他形式的儿童癌症。