Deep Phenotyping Children with Congenital Anomalies and Cancer Enrolled in Project:EveryChild

项目：EveryChild 对患有先天性异常和癌症的儿童进行深度表型分析

• 批准号: 10435096
• 负责人: Allison Heath
• 金额: $ 17.97万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10435096
• 关键词: Acute Lymphocytic Leukemia; Address; Adolescent; Biological; Biology; Cancer Histology; Case Report Form; Characteristics; Child; Childhood; Choanal Atresia; Chromosome abnormality; Clinical; Clinical Data; Communities; Computers; Congenital Abnormality; Congenital Heart Defects; Counseling; Data; Data Collection; Data Set; Development; Disease; Down Syndrome; Eligibility Determination; Enrollment; Epidemiologist; Epidemiology; Family; Fast Healthcare Interoperability Resources; Foundations; Future; Genetic; Genomics; Informatics; Infrastructure; Ingestion; Lead; Link; Live Birth; Malignant Childhood Neoplasm; Malignant Neoplasms; Medical Records; Modeling; Molecular; Molecular Profiling; Outcome Study; Pathogenicity; Pathway interactions; Patients; Pediatric Oncology Group; Phenotype; Principal Investigator; Process; Questionnaires; Registries; Research; Resources; Risk; Risk Factors; Sampling; Scientist; Screening for cancer; Services; Structural Congenital Anomalies; Surveys; Syndrome; Translating; Treatment outcome; Variant; Work; cancer predisposition; cancer risk; clinical phenotype; clinical translation; cohort; congenital anomaly; data and analysis portal; data ecosystem; data integration; data resource; developmental disease; endophenotype; experience; genome sequencing; genomic data; insight; instrument; multidisciplinary; neoplasm registry; novel; novel strategies; phenotypic data; population based; proband; programs; screening; support tools; tool; whole genome

ABSTRACT One of the strongest risk factors for cancer in children and adolescents is being born with a congenital anomaly— this is true both for chromosomal abnormalities (e.g., Down syndrome) and non-chromosomal birth defects (e.g., non-syndromic congenital heart defects), as validated in our registry linkage study of over 10 million live births. A vital next step in this work is to assemble sufficiently large cohorts of families and children with congenital anomalies and cancer that include: 1) comprehensive phenotypic and clinical information, which can be used to ascertain syndromic characteristics, endophenotypes, and treatment outcomes; and 2) well-annotated biological samples, which can be used for wide-ranging molecular assessments. Without these cohorts and these data, it is difficult to translate findings from registry linkage studies into novel insights concerning disrupted development and cancer predisposition. Furthermore, providing a platform for the integration of these data will increase the power for detecting the pathways underlying congenital anomalies and cancer. Two important resources for addressing these gaps are: 1) Project:EveryChild (PEC); and 2) the Gabriella Miller Kids First Pediatric Data Resource Center (KF-DRC). PEC is the Children’s Oncology Group (COG) registry, eligibility screening, biology, and outcome study (APEC14B1) that has been open since 2015 and currently includes >25,000 children. The DRC is the platform for the Kids First program in terms of aggregation of curated genomic and phenotypic data from structural birth defects and childhood cancer cohorts, as well as a central portal where these data and analysis tools are accessible to the research community. The objective of this application is to deeply characterize the phenotypes of children with congenital anomalies and cancer enrolled in COG PEC and integrate these data in the KF-DRC. Our hypothesis is that the processes and informatic framework we develop will strengthen future Kids First datasets and their impact by providing an implementation template for obtaining curated phenotypic data from PEC to combine with genomic data being generated in COG as part of Kids First and the Childhood Cancer Data Initiative (CCDI). We are uniquely poised to lead this effort with deep experience in both PEC and the DRC. To meet our objective, we propose the following aims: 1) Collect extensive phenotypic and clinical data from children with congenital anomalies and cancer enrolled in Project:EveryChild; and 2) Integrate phenotypic and clinical data from Project:EveryChild into the Gabriella Miller Kids First Pediatric Data Resource Center. This study will generate a robust resource for Kids First by providing deep phenotypic data on children with congenital anomalies and cancer, as well as serving as a scalable template for obtaining and curating phenotypic data for cohorts of children in COG undergoing comprehensive molecular profiling as part of Kids First and the CCDI. Finally, this work may ultimately inform novel strategies for cancer screening, counseling, and treatment for at-risk children.

摘要 儿童和青少年患癌症的最大风险因素之一是先天性异常- 这对于染色体异常（例如，唐氏综合征）和非染色体出生缺陷（例如， 非综合征性先天性心脏缺陷），这在我们对超过1000万例活产婴儿的登记关联研究中得到了验证。 这项工作的一个重要的下一步是收集足够大的先天性心脏病家庭和儿童群体， 异常和癌症，包括：1）全面的表型和临床信息，可用于 确定综合征特征，内在表型和治疗结果; 2）注释良好的生物学 样品，可用于广泛的分子评估。如果没有这些群体和数据， 很难将登记处联系研究的结果转化为有关中断发展的新见解 和癌症易感性。此外，为这些数据的整合提供一个平台， 检测先天性异常和癌症潜在途径的能力。两个重要的资源 解决这些差距是：1）项目：每个儿童（PEC）和2）加布里埃拉米勒儿童第一儿科 数据资源中心（KF-DRC）。PEC是儿童肿瘤组（COG）登记，资格筛选， 生物学和结果研究（APEC 14 B1）自2015年以来一直开放，目前包括> 25，000名儿童。 DRC是Kids First计划的平台，用于整合策划的基因组和表型 来自结构性出生缺陷和儿童癌症队列的数据，以及这些数据和 研究界可以使用分析工具。本申请的目的是深入 描述入组COG PEC的先天性异常和癌症儿童的表型， 将这些数据整合到KF-DRC中。我们的假设是，我们开发的过程和信息框架 将通过提供一个实施模板， 作为Kids First的一部分，PEC的精选表型数据将联合收割机与COG中生成的基因组数据相结合 儿童癌症数据倡议（CCDI）我们有着独特的优势，以丰富的经验领导这一努力。 在刚果民主共和国和PEC。为了达到这一目的，我们提出了以下目标：1）收集广泛的表型 以及参加Project：EveryChild的先天性异常和癌症儿童的临床数据;以及2） 将来自Project：EveryChild的表型和临床数据整合到Gabriella米勒Kids First儿科数据中 资源中心。这项研究将为Kids First提供一个强大的资源， 儿童先天性异常和癌症，以及作为一个可扩展的模板， 为COG中接受全面分子分析的儿童队列管理表型数据， 儿童第一和中纪委最后，这项工作可能最终为癌症筛查提供新的策略， 为有风险的儿童提供咨询和治疗