Integrate cancer genomics data in genetic studies and diagnosis of developmental disorders

将癌症基因组学数据整合到遗传研究和发育障碍的诊断中

• 批准号: 9311160
• 负责人: Yufeng Shen
• 金额: $ 33.27万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-16 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311160
• 关键词: Affect; Autistic Disorder; Biological Assay; Catalogs; Cell physiology; Childhood; Collaborations; Communities; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Detection; Diagnosis; Disease; EP300 gene; Ensure; Epilepsy; Family; Genes; Genetic; Genetic study; Genome; Genomics; Germ-Line Mutation; Goals; Growth; Inherited; Intellectual functioning disability; International; Laboratories; Malignant Neoplasms; Mendelian disorder; Methods; Missense Mutation; Molecular; Mutation; Neurodevelopmental Disorder; Newborn Infant; PTEN gene; PTPN11 gene; Patients; Pattern; Property; Reporting; Research; Sampling; Societies; Somatic Mutation; Structural Congenital Anomalies; Variant; actionable mutation; base; cancer genome; cancer genomics; congenital heart disorder; cost effective; data sharing; developmental disease; dosage; epigenomics; exome sequencing; functional genomics; genetic disorder diagnosis; genetic variant; genome sequencing; genomic data; improved; insight; loss of function; novel; precision oncology; risk variant; software development; targeted treatment; tool; tumor

Project Summary We aim to develop novel computational approaches to improve detection of risk genes and prediction of functional effects of germline mutations in patients with developmental disorders by integrating somatic cancer mutation and functional genomic data. Developmental disorders (DD), including neurodevelopmental disorders (NDD) and structural birth defects, affect ~5% of all newborns and have a significant impact on families and society. In the past few years, large-scale family-based sequencing studies on DD, such as autism and congenital heart disease, have identified a large number of de novo variants potentially implicated in disease. Unlike many other pediatric Mendelian diseases, genetic diagnosis of DD by genome or exome sequencing is more challenging because: (a) the complete catalog of DD genes (likely ~1,000) is not yet available; (b) observed variants are often difficult to interpret due to lack of rapid and cost-effective functional assays. Therefore, improved ability to identify novel risk genes and predict the functional effects of missense variants would significantly improve our ability to diagnose DD and develop targeted therapeutic approaches. Cancer is driven by dysregulation of core cellular processes that are also important to DD, such as proliferation, growth, and differentiation. There are well known genes implicated in both cancer and DD with somatic driver mutations in cancer and highly- penetrant germline de novo variants in DD. We analyzed data from recent large-scale genomic studies of cancer and DD, and found a large number of genes potentially implicated in both diseases, and many of them have similar molecular modes of action across conditions. This indicates that patterns of cancer somatic mutations can provide valuable insights to improve our ability to identify causal variants and genes in patients with DD. To that end, we have these specific aims: Specific Aim 1. Elucidate common genes and variants disrupted in cancer and DD based on somatic mutations in cancer and germline de novo mutations in DD. Specific Aim 2. Infer dosage sensitive genes by integrating mutation data in cancer and developmental disorders with functional genomic data. Specific Aim 3. Software development and data sharing. With the proposed new computational approaches, we will be able to leverage the accumulating cancer somatic mutation data from international cancer precision medicine efforts. In this framework, tumor samples will be natural “laboratories” for large-scale functional assays in cancer driver genes. This strategy will improve the utility of cross-field genomic data, and allow us to better predict functional effects of candidate variants (especially missense variants) in genetic diagnosis and identify novel risk genes for developmental disorders.

项目摘要 我们的目标是开发新的计算方法来改进风险基因的检测和预测 体细胞整合研究生殖系突变对发育障碍患者的功能影响 癌症突变和功能基因组数据。 发育障碍(DD)，包括神经发育障碍(NDD)和结构性出生 缺陷，影响了约5%的新生儿，并对家庭和社会产生了重大影响。在过去的几年里 多年来，关于DD的大规模家庭测序研究，如自闭症和先天性心脏病，已经 确定了大量可能与疾病有关的从头变异。与许多其他儿科不同 对于孟德尔病，通过基因组或外显子组测序进行DD的遗传诊断更具挑战性，因为： (A)尚未获得DD基因的完整目录(可能为~1,000个)；(B)观察到的变异通常 由于缺乏快速和经济有效的功能分析，因此难以解释。因此，提高了 识别新的风险基因并预测错义变异的功能效应将显著改善 我们诊断DD和开发有针对性的治疗方法的能力。癌症是由调节失调引起的 对DD也很重要的核心细胞过程，如增殖、生长和分化。那里 都是众所周知的与癌症和DD有关的基因，在癌症和高度... DD中的穿透性胚系新变种。我们分析了最近大规模基因组研究的数据 癌症和DD，发现了大量可能与这两种疾病有关的基因，其中许多 在不同条件下具有相似的分子作用模式。这表明癌症的体细胞模式 突变可以提供有价值的见解，以提高我们识别患者因果变异和基因的能力 与DD一起。 为此，我们有以下具体目标：具体目标1.阐明共同基因和变异 基于癌症中的体细胞突变和DD中的胚系新突变，在癌症和DD中被破坏。 具体目标2.通过整合癌症和发育过程中的突变数据来推断剂量敏感基因 与功能基因组数据相关的疾病。具体目标3.软件开发和数据共享。 有了拟议的新计算方法，我们将能够利用积累的 来自国际癌症精准医学努力的癌症体细胞突变数据。在这个框架中，肿瘤 样本将成为大规模癌症驱动基因功能分析的天然“实验室”。这一战略 将提高跨领域基因组数据的实用性，并使我们能够更好地预测 基因诊断中的候选变异(特别是错义变异)和识别新的风险基因 发育障碍。