Comprehensive Colorectal Cancer Risk Prediction to Inform Personalized Screening

全面的结直肠癌风险预测为个性化筛查提供信息

• 批准号: 9237818
• 负责人: DOUGLAS Allen CORLEY
• 金额: $ 73.84万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-03 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9237818
• 关键词: Adherence; Advisory Committees; Age; Alcohols; California; Cancer Etiology; Cancer Intervention and Surveillance Modeling Network; Case-Control Studies; Cessation of life; Characteristics; Clinical; Colon; Colorectal Adenoma; Colorectal Cancer; Colorectal Neoplasms; Communities; Cost Effectiveness Analysis; Coupled; Data; Decision Modeling; Diet; Early Diagnosis; Early identification; Ensure; Environment; Environmental Risk Factor; Epidemiology; Ethnic Origin; Evaluation; Excision; Family; Family history of; Genes; Genetic; Genetic Research; Genetic Risk; Genetic screening method; Genotype; Guidelines; Health; Individual; Intervention; Knowledge; Life Expectancy; Life Style; Machine Learning; Malignant Neoplasms; Medical Records; Methods; Minority; Modeling; National Cancer Institute; Obesity; Outcome; Participant; Patient Care; Persons; Pharmaceutical Preparations; Physical activity; Physicians; Policy Developments; Population; Predictive Factor; Preventive; Preventive service; Primary Prevention; Prospective cohort; Public Health; Recommendation; Recording of previous events; Research; Resources; Risk; Risk Factors; Sampling; Science; Screening for cancer; Secondary Prevention; Signal Transduction; Smoking; Technology; Testing; Training; Translations; United States; Variant; Women' s Health; Work; adenoma; base; cancer prevention; cancer risk; clinical practice; clinical translation; cohort; colorectal cancer prevention; colorectal cancer screening; community based participatory research; comparative; cost; cost effective; cost effectiveness; demographics; disorder risk; epidemiologic data; epidemiology study; ethnic minority population; functional genomics; gene environment interaction; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic data; health care delivery; member; non-genetic; novel; personalized decision; personalized medicine; personalized screening; population based; precision medicine; predictive modeling; programs; racial and ethnic; rare variant; routine care; screening; socioeconomics; tool; whole genome

PROJECT SUMMARY The guidelines for initiation of colorectal cancer (CRC) screening are currently based on two risk factors: attained age and family history of CRC. Using the principles of precision medicine, we will individually tailor CRC screening recommendations based on the enormous knowledge we now have on genetic and non- genetic factors that predict risk for this disease. This strategy will reduce under and over-utilization of CRC screening, because individual risks vary substantially in the population, and over 80% of all CRC cases occur in those without a positive family history. In Aim 1 we will develop predictive models for CRC, based on genetic (~30M common and rare genotyped or imputed genetic variants) and clinico-epidemiologic variables (over 70 harmonized characteristics), derived from over 40,000 colorectal tumor cases and 46,000 controls. We will identify key predictors, and derive efficient personalized risk-prediction models for early detection of more treatable CRCs as well for CRC prevention, through the identification of advanced colorectal adenomas. In Aim 2 we will calibrate and validate these models in two prospective cohorts of >120,000 participants, including 40,000 minority members, diversity representing racially/ethnically and socioeconomically. These two cohorts (Research Program on Genes, Environment and Health and the Women's Health Initiative Minority cohort) contain genome-wide genotype array and comprehensive risk factor data coupled to data on screening and outcome data, allowing us to test the model's ability to predict risk for CRC and advanced colorectal adenoma across a broadly defined community-based population, and to personalize decision on starting age of screening based on individually specific genetic and environmental risk factors. In Aim 3 we will estimate the population benefit of our risk-stratified screening strategy, based on our risk prediction methods, compared with the current screening recommendations. This comparison will employ the well-tested decision model currently used to inform the United States Preventive Services Task Force CRC screening guidelines. Accomplishing these three aims, our research has the potential to accelerate the translation of a large amount of genetic and epidemiologic research to patient care by predicting advanced adenoma risk, for cancer prevention, and predicting cancer risk, for early cancer detection. Genetic testing is becoming part of routine care and genetic data will increasingly become part of an individual's medical record. Using genetic and non-genetic risk-factor information in clinical and preventive settings is a critical step towards developing precision medicine. Our models will provide recommendations for individually tailored CRC screening and interventions and, because they are personalized, may also increase adherence, maximize the appropriate use of invasive technologies, and guide important next steps towards public health policy development and clinical translation.

项目总结 目前，启动结直肠癌筛查的指南基于两个风险因素： 结直肠癌患者的年龄和家族史。运用精准医学的原理，我们将个别量身定制 结直肠癌筛查建议基于我们现在所拥有的关于遗传和非 预测这种疾病风险的遗传因素。这一战略将减少CRC的使用不足和过度使用 筛查，因为个体风险在人群中有很大差异，超过80%的结直肠癌病例发生 在那些没有阳性家族史的人中。在目标1中，我们将开发CRC的预测模型，基于 遗传(约3000万常见和罕见的基因分型或归因于遗传变异)和临床流行病学变量 (70多个协调特征)，来自40,000多个结直肠肿瘤病例和46,000个对照。 我们将识别关键预测因素，并建立有效的个性化风险预测模型，以便及早发现 通过识别晚期结直肠腺瘤，更多可治疗的结直肠癌也可用于预防结直肠癌。 在目标2中，我们将在两个预期的12万名参与者队列中校准和验证这些模型， 包括40,000名少数族裔成员，在种族/民族和社会经济方面具有多样性。这两个 基因、环境和健康研究方案与妇女健康倡议少数民族 队列)包含全基因组基因阵列和与筛查数据相关联的全面风险因素数据 和结果数据，使我们能够测试该模型预测结直肠癌和晚期结直肠癌风险的能力 广泛定义的以社区为基础的人群中的腺瘤，并个性化决定开始年龄 根据个别特定的遗传和环境风险因素进行筛查。在目标3中，我们将估计 基于我们的风险预测方法，比较了我们的风险分层筛查策略的人群效益 根据目前的筛查建议。此比较将使用经过充分测试的决策模型 目前用于通知美国预防服务工作组CRC筛查指南。 实现这三个目标，我们的研究有可能加速大量的翻译 通过预测晚期腺瘤风险的遗传学和流行病学研究，用于癌症预防， 以及预测癌症风险，以便及早发现癌症。基因检测正在成为常规护理的一部分 基因数据将越来越多地成为个人医疗记录的一部分。使用遗传和非遗传 临床和预防环境中的风险因素信息是提高精确度的关键一步 医药。我们的模型将为个人定制的结直肠癌筛查和干预提供建议 而且，因为它们是个性化的，也可能增加依从性，最大限度地适当使用侵入性 技术，并指导公共卫生政策制定和临床翻译的下一步重要步骤。