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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结