Personalizing Post-Polypectomy Surveillance for Colorectal Cancer Prevention

项目摘要和摘要 筛查是降低结直肠癌(CRC)发病率和死亡率的既定方法。然而， 尽管指导意见支持启动结直肠癌筛查(即45年)，但人们对如何做知之甚少 在发现并切除癌前息肉之后。这尤其令人担忧，因为超过40%的 接受结直肠癌筛查的个人被发现患有癌前息肉，然后被指示 为降低结直肠癌风险，每3-10年进行一次频繁的结肠镜检查(称为监测)。当前 指南采用风险分层方案，仅根据息肉将患者划分为高风险或低风险 他们初次结肠镜检查的特征。然而，基于息肉的风险分层方法并不精确， 预测进展期的敏感性为59-81%，特异性为43-58% 息肉摘除后的肿瘤。因此，我们目前基于指南的风险分层方法都遗漏了高风险 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影响 结肠镜检查及其相关危害。我们小组和其他人最近的研究发现了几个 与结直肠癌相关的临床和遗传风险因素(即多基因风险评分)；这些因素可能会进一步优化风险 结直肠癌筛查和息肉摘除后的分层，但仍未得到充分研究。对于这份R01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，该工具结合了详细的 息肉特征和其他已知在结直肠癌风险中发挥重要作用的重要预测因素，如 临床和遗传(即多基因风险评分)风险因素(目标1和2)。第二，我们将确定最优 给出目标1-2中定义的个人风险估计的CRC监测战略，并评估成本- 不同风险分层监测策略的有效性与当前推荐指南的比较 以息肉为基础的监测战略(目标3)。这一目标将利用我们与 建立了国际公认的微观模拟模型(MISCAN-COLON)，为美国预防 工作队的建议。最后，我们将听取患者、临床医生和服务提供商对 这些新的综合风险预测方法，以优化潜在采用，并评估潜在 执行障碍和促进者(目标4)。这一目标将结合团队体验和混合 方法确定执行的态度和障碍的技术。总体目标将利用 综合数据来自一个非常大的当代社区队列和一个独立的 等待验证的队列。这些队列的详细数据包括全基因组的基因阵列和之前的 筛查、病理和临床数据以及监测结果。这项研究可以从根本上改变 我们每年为700多万被诊断为癌前息肉的患者提供护理，为患者提供个性化的治疗 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，以及 优化息肉切除术后监测，降低结直肠癌发病率和死亡率。

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.