Colorectal Tumor Risk Prediction in the PLCO Trial

描述（由申请人提供）：尽管结直肠癌（CRC）的发病率略有下降，但它仍然是癌症死亡的第二大原因。矛盾的是，如果通过筛查早期发现，它是最可预防和治疗的肿瘤疾病之一。在美国，内窥镜检查，尤其是结肠镜检查，是高危人群最常用的策略；然而，它是昂贵的，侵入性的，并有风险，导致总体利用率低的人群。目前的筛查建议仅基于年龄、结直肠癌家族史和以前的筛查结果，而结直肠癌的发病率在人群中差异很大，大多数病例发生在没有阳性家族史的人群中。因此，本提案的目标是开发、校准和验证一个综合的风险预测模型，该模型结合了全基因组遗传数据，以及生活方式和环境风险因素，如肥胖、药物、吸烟和饮食，可以为高危人群提供更准确的风险分层。该模型将允许识别和优先考虑具有较高CRC风险的个体进行有针对性的筛查和干预，同时减少对低风险个体的重视。在Aim 1中，我们将在现有的大型结直肠癌遗传与流行病学联盟（GECCO）中开发一个全面的结直肠癌风险预测模型，该模型包括超过14,000例病例和15,300例对照，以及来自全基因组基因分型阵列和全基因组测序的现有详细遗传数据，以及统一的临床和流行病学变量（U01-CA137088, U01-CA164930）。在目标2中，我们将在前列腺、肺、结直肠癌和卵巢癌（PLCO）癌症筛查试验中对该模型进行独立验证。在PLCO中，除了使用丰富的临床和流行病学数据外，我们还将对所有1522例CRC病例、所有2604例晚期腺瘤DNA病例和3000例随机选择的对照组进行基因分型和遗传变异的计算。包括晚期腺瘤将使我们能够独特地评估CRC重要前驱病变的风险预测。将左侧和右侧结直肠癌的风险预测模型进行比较，以考虑柔性乙状结肠镜筛查。在目标3中，我们将在PLCO内部将我们的综合模型与当前的筛选指南和已发表的风险预测模型进行比较，以估计我们的模型与现有指南和其他预测模型相比的改进。我们的建议利用了广泛的努力来发现结直肠癌的遗传和环境风险因素，并提供了在人群中实际应用的潜力。基因检测已经是日常护理的一部分，预计基因数据将越来越多地成为个人医疗记录的一部分。在临床和预防设置中利用遗传和非遗传风险因素信息是开发基于风险分层的更个性化医疗的关键一步。我们的模型可用于定制筛查和预防策略，这些策略更具成本效益，并可能增加依从性。

DESCRIPTION (provided by applicant): Despite slight declines in colorectal cancer (CRC) incidence, it remains the second leading cause of cancer death. Paradoxically, it is among the most preventable and treatable of neoplastic diseases when detected early via screening. In the U.S., endoscopic screening, particularly colonoscopy, is the most commonly used strategy by the population at risk; however, it is costly, invasive, and carries risks, leading to overall low utilization in the population. Current screening recommendations are based only on age, family history of CRC, and previous screening results, whereas incidence of CRC varies substantially in the population and most cases occur in those without a positive family history. Therefore, the goal of this proposal is to develop, calibrate, and validate a comprehensive risk-prediction model incorporating genome-wide genetic data, as well as lifestyle and environmental risk factors, such as obesity, medication, smoking, and diet, which can provide a more accurate risk stratification for those at risk. The model would permit identification and prioritization of individuals at higher CRC risk for targeted screening and intervention, while reducing emphasis for those at low risk. In Aim 1 we will develop a comprehensive CRC risk-prediction model within the large existing Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), which includes over 14,000 cases and 15,300 controls with existing detailed genetic data from genome-wide genotyping arrays and whole genome sequencing, as well as harmonized clinical and epidemiologic variables (U01-CA137088, U01-CA164930). In Aim 2 we will undertake independent validation of the model in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. In PLCO, in addition to using the rich clinical and epidemiologic data, we will genotype and impute genetic variants across the genome in all 1,522 incident CRC cases, all 2,604 advanced adenoma cases with DNA, and 3,000 randomly selected controls. Including advanced adenoma will uniquely allow us to evaluate risk prediction for an important precursor lesion of CRC. Risk-prediction model for left- and right-sided CRC will be compared to account for flexible-sigmoidoscopy screening. In Aim 3 we will compare within PLCO our comprehensive model with current screening guidelines and published risk-prediction models to estimate the improvement of our model in comparison with existing guidelines and other prediction models. Our proposal leverages the extensive efforts to discover genetic and environmental risk factors for CRC, and offers the potential for pragmatic application to the population. Genetic testing is already part of routine care and it is expected that genetic data wil increasingly become part of an individual's medical record. Utilizing genetic and non-genetic risk-factor information in clinical and preventive settings is a critical step towards developing more personalized medicine based on risk stratification. Our model can be used for tailored screening and prevention strategies that are more cost-efficient and may increase adherence.