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.

项目摘要 目前，启动结直肠癌（CRC）筛查的指南基于两个风险因素： 达到年龄和CRC家族史。利用精准医疗的原理，我们将为您量身定制 CRC筛查建议的基础上，我们现在有大量的知识，对遗传和非遗传 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 筛查，因为个体风险在人群中差异很大，超过80%的CRC病例发生在 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30 M常见和罕见基因分型或插补遗传变异）和临床流行病学变量 (over 70个协调特征），来自超过40，000例结直肠肿瘤病例和46，000例对照。 我们将确定关键的预测因子，并推导出有效的个性化风险预测模型，用于早期检测 更多可治疗的CRC以及CRC预防，通过识别晚期结直肠腺瘤。 在目标2中，我们将在两个> 120，000名参与者的前瞻性队列中校准和验证这些模型， 包括40 000名少数民族成员，多样性体现在种族/族裔和社会经济方面。这两 群组（基因、环境与健康研究计划和妇女健康倡议少数群体） 队列）包含全基因组基因型阵列和与筛查数据相结合的综合风险因素数据 和结果数据，使我们能够测试模型预测CRC和晚期结直肠癌风险的能力。 腺瘤在广泛定义的社区人群，并个性化的决定开始年龄 根据个体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 根据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获益 目前的筛查建议。这种比较将采用经过充分测试的决策模型 目前用于告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快大量的翻译， 遗传学和流行病学研究，通过预测晚期腺瘤风险，用于癌症预防， 和预测癌症风险，用于早期癌症检测。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。利用遗传和非遗传 临床和预防环境中的风险因素信息是实现精确性的关键一步 药我们的模型将为个性化的CRC筛查和干预提供建议 而且，因为它们是个性化的，还可以增加依从性，最大限度地适当使用侵入性 这些技术将为公共卫生政策制定和临床转化提供指导。