Building Data Science Tools for Genetic Models of Colorectal Cancer Progression and Risk

为结直肠癌进展和风险的遗传模型构建数据科学工具

• 批准号: 10368281
• 负责人: Elizabeth R Hauser
• 金额: --
• 依托单位: DURHAM VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10368281
• 关键词: Age; Algorithms; Big Data; Biological; Biological Specimen Banks; Biometry; Cancer Etiology; Catalogs; Cessation of life; Clinical; Clinical Data; Colonic Adenoma; Colonoscopy; Colorectal Cancer; Colorectal Neoplasms; Computational Biology; Computing Methodologies; Data; Data Science; Data Scientist; Data Set; Databases; Development; Disease; Evaluation of Risk Factors; Excision; Exposure to; Frequencies; Future; Genetic; Genetic Markers; Genetic Models; Genetic Risk; Genomics; Goals; Grant; Guidelines; Healthcare Systems; Histopathology; Human Genetics; Incidence; Individual; Joints; Libraries; Longitudinal Studies; Longitudinal cohort; Medical Records; Modality; Modeling; Mutation; Pathology; Phenotype; Pilot Projects; Polyps; Precancerous Polyp; Prevention; Preventive; Recording of previous events; Research; Research Personnel; Research Support; Resources; Risk; Risk Assessment; Risk Factors; Safety; Sampling; Somatic Mutation; Specimen; Statistical Methods; Statistical Models; Testing; Time; Tissues; United States; Universities; Veterans; Work; analytical tool; biobank; cancer risk; clinical practice; clinical risk; cohort; colon cancer screening; colorectal cancer prevention; colorectal cancer progression; colorectal cancer risk; colorectal cancer screening; computerized tools; cooperative study; data integration; data quality; data resource; data tools; design; experience; follow-up; genetic analysis; genetic information; genetic risk factor; genomic biomarker; genomic data; high risk; high risk population; improved; military veteran; mortality; mortality risk; novel; personalized medicine; phenomics; polygenic risk score; prevent; prognostic; programs; progression risk; prospective; research study; risk prediction; risk prediction model; risk stratification; screening; tissue resource; tool; tv watching

Colorectal cancer (CRC) is the 2nd leading cause of cancer death in the United States. Screening for CRC with colonoscopy reduces incidence and mortality. The VA Cooperative Studies Program #380 “Prospective Evaluation of Risk Factors for Colonic Adenomas (>1cm) in Asymptomatic Subjects” was one of the first studies to demonstrate the safety of screening colonoscopy and highlight the magnitude of benefit through the removal of precancerous polyps for the prevention of CRC. However, there is considerable variability in individual risk of CRC that could impact age at initiation of CRC screening, screening modality and frequency of follow-up. Yet, guidelines do not recognize this variability, performing too much colonoscopy screening and surveillance in low risk individuals and not providing enough or timely screening and surveillance in high-risk individuals. Genetic and genomic data offer a promising strategy to improve CRC risk prediction and better target CRC screening resources. However, what is missing in these genomic risk calculations is recognition of the timing of certain genetic changes and of those changes in CRC precursors in a clinically meaningful time course to predict the timing of future CRC. Longitudinal studies of CRC precursors and progression, identification of genetic risk factors for progression, and incorporation into personalized risk models can only be accomplished through development of an integrated data resource and application of new statistical models. We have begun to develop the resources to allow the large- scale analyses needed to develop comprehensive clinical and genetic risk models. This proposal is built on our work CSP#380 which incorporates a longitudinal research program of 3121 Veterans who underwent screening colonoscopy between 1994 and 1997 and have been followed for 20 years. We have used the CSP#380 research database to apply emerging statistical models for longitudinal cohorts that incorporate the clinical information from each colonoscopy, allowing estimates of informative follow-up times while taking the competing risk of mortality over time into account. We have also extended the biorepository for CSP#380 to include pathology specimens obtained at colonoscopy to provide a longitudinal tissue resource. The goal of this proposal is to extend the approach and models developed in CSP#380 to the VA Colonoscopy Cohort (VACC), which includes all Veterans with exposure to colonoscopy in the VA. We will perform extensive testing in CSP#380 to provide estimates of data quality. This much larger VA data set will allow more thorough discovery and testing of genetic factors in CRC risk models. This ambitious project will combine development of a curated phenotype library with histopathology results generated from VA medical records with the joint longitudinal models applied to CSP#380. We will begin to explore how to incorporate genetic information into these models through pilot studies in CSP#380 with the future plan of applying these models to additional VA datasets.

结直肠癌（CRC）是美国癌症死亡的第二大原因。筛查 结肠镜检查可降低CRC的发病率和死亡率。合作研究项目#380 “无症状受试者中结肠腺瘤（> 1 cm）风险因素的前瞻性评价” 是最早证明结肠镜检查安全性的研究之一，并强调了 通过切除癌前息肉预防CRC的益处。 然而，CRC的个体风险存在相当大的差异，这可能影响开始治疗时的年龄。 CRC筛查、筛查方式和随访频率。然而，指导方针不承认 这种可变性，在低风险个体中进行过多的结肠镜筛查和监测， 没有对高危人群提供足够或及时的筛查和监测。遗传和 基因组数据为改善CRC风险预测和更好地靶向CRC提供了一种有前途的策略 筛选资源。然而，在这些基因组风险计算中缺少的是对基因突变的认识。 某些遗传变化的时间和CRC前体中的这些变化在临床上有意义。 时间进程，以预测未来CRC的时间。CRC前体的纵向研究， 疾病进展，识别疾病进展的遗传风险因素，并纳入个性化治疗方案。 风险模型只能通过开发集成数据资源来完成， 应用新的统计模型。我们已经开始开发资源，让大- 规模分析需要制定全面的临床和遗传风险模型。这项建议是 建立在我们的工作CSP#380，其中包括3121退伍军人的纵向研究计划， 在1994年至1997年期间接受了筛查性结肠镜检查，并随访了20年。我们 我使用CSP#380研究数据库应用新兴的纵向统计模型， 纳入每次结肠镜检查的临床信息的队列，允许估计 信息随访时间，同时考虑随着时间的推移死亡率的竞争风险。我们 还扩展了CSP #380的生物储存库，以包括在以下地点获得的病理标本： 结肠镜检查以提供纵向组织资源。该提案的目的是扩大 CSP#380中开发的VA结肠镜检查队列（VACC）方法和模型，包括 所有退伍军人都在退伍军人事务部接受过结肠镜检查我们将在CSP#380中进行广泛的测试 以提供数据质量的估计。这个更大的VA数据集将允许更彻底的发现 和CRC风险模型中遗传因素的测试。这个雄心勃勃的项目将联合收割机的发展 组织病理学结果来自VA医疗记录， 应用于CSP#380的关节纵向模型。我们将开始探索如何将遗传 通过CSP#380中的试点研究，将信息纳入这些模型，并计划在未来应用这些模型。 模型到其他VA数据集。