Biomarker screening algorithms for the improved early detection of hepatocellular carcinoma

项目摘要/摘要 在美国，肝细胞癌的发病率持续上升，大多数 被诊断为晚期疾病的患者，治疗选择有限，预后较差。肝细胞癌是 预计到2030年将成为癌症相关死亡的第三大原因。肝细胞癌的早期检测是 对于降低高肝癌死亡率是必要的，因为那些早期疾病的人有多个， 潜在治愈，可供选择的治疗方法。目前的指南建议肝硬变患者接受 有或无血清甲胎蛋白(AFP)的六个月肝脏超声，但超声不敏感 对于早期病变和报道的AFP表现差异很大。 我们将开发和评估两种新的生物标志物筛选算法，旨在提高早期检测 肝细胞癌的。我们先前提出了一种用于AFP的参数经验贝叶斯(PEB)筛选算法 通过纳入先前AFP结果的个性化阈值，提高了更早的肝细胞癌检测。血- 基于生物标记物是一种最有希望的、最具成本效益的广泛肝细胞癌监测工具，而且有 多个新的肝癌生物标记物正在开发中。在目标1中，我们将推广PEB算法以实现 联合筛查多种生物标志物(如AFP、DCP、AFP-L3，有希望的新型生物标志物)。我们建议 为多个肝细胞癌生物标志物制定稳健的决策规则，该规则使用先前的筛查历史更早地增加 肝细胞癌检测在丙型肝炎抗病毒长期治疗抗肝硬化试验中的应用。第二次放映 策略的基础是观察到在积极监测下的患者不断更新临床 以及收集的实验室数据，但没有系统地用于改进筛查。在目标2中，我们将开发和 评估完全贝叶斯筛查算法，将纵向AFP、其他实验室标志物和 临床协变量增加肝细胞癌早期发现的可能性。我们的目标是改进AFP筛查 通过积极开发AFP关节模型、其他实验室测试和临床数据来实现这一目标。 一旦得到验证，该算法就可以根据当前的临床实践来实现。我们将发展和 在两个大型回溯性队列中改进算法：退伍军人事务部国家肝硬变 Cohort，凯撒永久北加州肝硬变队列。在目标3中，我们将评估这两种算法 肝细胞癌早期检测策略(HEDS)研究和跨德克萨斯肝细胞癌联盟 (THCCC)；迄今为止在美国聚集的最大的潜在肝硬变队列。我们将利用 我们获得了一些最权威的肝硬变研究，以建立和评估肝细胞癌筛查算法， 目标是将肝癌筛查的敏感性提高33%，同时保持较低的假阳性率 确保肝细胞癌监测的可行性。此外，开发的统计方法将具有广泛的 应用于其他癌症筛查环境(例如肺癌、卵巢癌、前列腺癌和胰腺癌)。 1

PROJECT SUMMARY/ABSTRACT The incidence of hepatocellular carcinoma (HCC) in the United States continues to rise with majority of patients diagnosed with advanced stage disease, limited treatment options and poor prognosis. HCC is projected to become the 3rd leading cause of cancer-related deaths by 2030. The earlier detection of HCC is necessary towards reducing the high HCC mortality rates since those with early stage disease have multiple, potentially curative, treatment options available. Current guidelines recommend those with cirrhosis undergo six-monthly liver ultrasound with or without serum alpha-fetoprotein (AFP), however ultrasound is not sensitive for early lesions and the reported performance of AFP varies widely. We will develop and evaluate two novel biomarker screening algorithms that aim to improve the early detection of HCC. We have previously proposed a parametric empirical Bayes (PEB) screening algorithm for AFP that increased earlier HCC detection through personalized thresholds that incorporate prior AFP results. Blood- based biomarkers are a most promising, cost-effective tool for widespread HCC surveillance and there are multiple novel HCC biomarkers under development. In Aim 1 we will generalize the PEB algorithm to enable joint screening with multiple biomarkers (e.g. AFP, DCP, AFP-L3, promising novel biomarkers). We propose to develop a robust decision rule for multiple HCC biomarkers that uses prior screening history to increase earlier HCC detection in the Hepatitis C Antiviral Long-term Treatment against Cirrhosis Trial. A second screening strategy is based on the observation that patients under active surveillance have continuously updated clinical and laboratory data collected but not used systematically to improve screening. In Aim 2 we will develop and evaluate a fully Bayesian screening algorithm that combines longitudinal AFP, other laboratory markers and clinical covariates to increase the likelihood of earlier detection of HCC. Our goal is to improve AFP screening performance through the robust development of joint models for AFP, other laboratory tests and clinical data. Once validated, this algorithm could be implemented based on current clinical practice. We will develop and refine the algorithm in two large retrospective cohorts: a Department of Veterans Affairs national cirrhosis cohort, a Kaiser Permanente Northern California cirrhosis cohort. In Aim 3 we will evaluate both algorithms in the Hepatocellular carcinoma Early Detection Strategy (HEDS) study and the Trans Texas HCC Consortium (THCCC); the largest prospective cirrhosis cohorts assembled in the United States to date. We will leverage our access to some of the most authoritative cirrhosis studies to build and evaluate HCC screening algorithms, with a target of increasing the sensitivity of HCC screening by 33% while maintaining a low false positive rate to ensure the feasibility of HCC surveillance. Additionally, the statistical methods developed will have broad application in other cancer screening settings (e.g. lung, ovarian, prostate, and pancreatic cancer). 1