Personalizing Post-Polypectomy Surveillance for Colorectal Cancer Prevention

项目总结和摘要 筛查是降低结直肠癌（CRC）发病率和死亡率的既定方法。然而，在这方面， 尽管指南支持CRC筛查启动（即，45年），相对而言，人们对该怎么做知之甚少 在癌前息肉被检测到并切除后。这一点尤其令人担忧，因为超过40%的 接受CRC筛查的个体被发现患有癌前息肉，然后被指示 每3-10年进行一次频繁的结肠镜检查（称为监测），以降低CRC风险。电流 指南采用风险分层方案，仅根据息肉将患者分为高风险或低风险 从他们的第一次结肠镜检查的特征。然而，基于息肉的风险分层方法是不精确的， 其预测随后的晚期乳腺癌的敏感性和特异性分别为59-81%和43- 58%， 息肉切除后的肿瘤。因此，我们目前基于指南的风险分层方法都错过了高风险 可能受益于早期监测的个人，并使许多低风险个人受到不必要的 结肠镜检查及其相关危害。我们小组和其他人最近的研究已经确定了几个 临床和遗传（即，多基因风险评分）与CRC相关的风险因素;这些可能进一步优化风险 结直肠癌筛查和息肉切除后分层，但仍研究不足。对于R 01提案，我们 我将首先开发和验证一个实用的，临床上有用的风险预测工具，包括详细的 息肉特征和其他已知在CRC风险中发挥重要作用的重要预测因素，例如 临床和遗传（即，多基因风险评分）风险因素（目的1和2）。第二，我们将确定最佳的 根据目标1-2中定义的个体风险估计，制定CRC监测策略，并评估成本- 不同风险分层监测策略与当前指南推荐的有效性比较 基于息肉的监测战略（目标3）。这一目标将利用我们正在进行的合作， 建立，国际公认的微观模拟模型（MISCAN-Colon），告知美国政府 工作队的建议。最后，我们将获得患者，临床医生和服务提供者的观点， 这些新的综合风险预测方法，以优化潜在的采用，并评估潜在的 实施障碍和促进因素（目标4）。这一目标将结合小组经验， 确定执行的态度和障碍的方法和技术。总体目标将利用 来自一个非常大的当代社区队列和一个独立的 队列进行验证。这些队列的详细数据包括全基因组基因型阵列， 筛查、病理和临床数据以及监测结果。这项研究可以大大改变人们的生活方式 我们每年为超过700万名被诊断患有癌前息肉的患者提供护理， 使用一种新的、新颖的、全面的、以患者为中心的风险预测模型进行息肉切除术监测， 优化息肉切除术后的监测，以降低CRC的发病率和死亡率。

PROJECT SUMMARY AND ABSTRACT Screening is an established method for decreasing colorectal cancer (CRC) incidence and mortality. However, despite guidance supporting CRC screening initiation (i.e., 45 years), relatively little is known about what to do after a precancerous polyp is detected and removed. This is particularly concerning given that over 40% of individuals who undergo CRC screening are found to have a precancerous polyp and then instructed to undergo frequent colonoscopies (termed surveillance) every 3-10 years for CRC risk reduction. Current guidelines utilize a risk-stratification scheme that categorizes patients as high or low risk based only on polyp characteristics from their initial colonoscopy. However, polyp-based risk stratification methods are imprecise, with a sensitivity and specificity of 59-81% and 43-58%, respectively, for predicting subsequent advanced neoplasia after polyp removal. Thus, our current guideline-based risk stratification methods both miss high-risk individuals who may benefit from early surveillance and subject many low-risk individuals to unnecessary colonoscopies and its associated harms. Recent studies from our group and others have identified several clinical and genetic (i.e., polygenic risk score) risk factors associated with CRC; these may further optimize risk stratification following CRC screening and polyp removal, but remain understudied. For this R01 proposal, we will first develop and validate a practical, clinically useful risk prediction tool that incorporates both detailed polyp characteristics and other important predictors known to play an important role in CRC risk, such as clinical and genetic (i.e., polygenic risk score) risk factors (Aims 1 and 2). Second, we will identify optimal strategies for CRC surveillance given individual risk estimates defined in Aims 1-2 and evaluate the cost- effectiveness of different risk-stratified surveillance strategies compared to current guideline recommended polyp-based surveillance strategies (Aim 3). This Aim will leverage our ongoing collaboration with an established, internationally recognized micro-simulation model (MISCAN-Colon) that informs U.S. Preventative Task Force recommendations. Lastly, we will gain patient, clinician, and service provider’s perspectives on these novel comprehensive risk prediction methods, to optimize potential adoption, and assess potential implementation barriers and facilitators (Aim 4). This aim will incorporate group experiences with mixed methods techniques to identify attitudes and barriers of implementation. The overall aims will leverage comprehensive data from an extremely large contemporary community-based cohort and an independent cohort for validation. These cohorts’ detailed data include genome-wide genotype arrays coupled with prior screening, pathologic and clinical data, and surveillance outcomes. This study can substantially transform how we manage care for over 7 million patients diagnosed annually with precancerous polyps, personalize post- polypectomy surveillance using a new, novel, comprehensive, patient-centered risk prediction model, and optimize post-polypectomy surveillance to reduce CRC incidence and mortality.