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

项目摘要 我们的目标是开发新的计算方法，以提高风险基因的检测和预测 生殖系突变在发育障碍患者中的功能效应， 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，影响到所有新生儿的约5%，并对家庭和社会产生重大影响。过去几 多年来，对DD（如自闭症和先天性心脏病）的大规模基于家庭的测序研究， 鉴定了大量可能与疾病有关的从头变异。与许多其他儿科 孟德尔疾病，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)DD基因的完整目录（可能约1，000个）尚不可用;（B）观察到的变异体通常 由于缺乏快速和具有成本效益的功能测定，难以解释。因此，提高能力， 识别新的风险基因并预测错义变异的功能效应将显着提高 我们诊断DD和开发靶向治疗方法的能力。癌症是由以下因素引起的： 核心细胞过程对DD也很重要，如增殖、生长和分化。那里 是与癌症和DD有关的众所周知的基因，在癌症中具有体细胞驱动突变， DD中的渗透性生殖系从头变异。我们分析了最近大规模基因组研究的数据， 癌症和DD，并发现了大量的基因可能涉及这两种疾病，其中许多 在不同条件下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中致病变异和基因的能力 关于DD 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中的种系从头突变， 具体目标2。通过整合癌症和发育中的突变数据推断剂量敏感基因 功能性基因组数据的疾病。具体目标3。软件开发和数据共享。 有了新的计算方法，我们将能够利用累积的 癌症体细胞突变数据来自国际癌症精准医学努力。在这个框架下，肿瘤 样本将成为癌症驱动基因大规模功能分析的天然“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功能性影响， 基因诊断中的候选变异（尤其是错义变异），并确定新的风险基因， 发育障碍

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.