Risk Stratification for and Early Detection of Liver Cancer

翻译研究中心(TRC)包括一个由临床和翻译组成的多学科团队 具有良好的协作工作记录的研究人员，其集体使命是减少 肝癌的负担。在拟议的TRC的中心是两个独特的积极的前瞻性(肝细胞癌内监测) 一群患有肝硬变的患者。其中一组来自一家癌症预防研究所 德克萨斯大学(CPRIT)资助了正在进行的前瞻性多中心研究(德克萨斯州肝癌联合会)，该研究的目标是 招募3000名肝硬变患者(12,000个监测病例和200个预期的肝细胞癌病例) 多种病因(包括治愈的丙型肝炎和非酒精性脂肪性肝病)。这些病人都处于常规状态。 在德克萨斯州的5个医疗中心进行一年两次的监测。第二个是由700名患者组成的队列(1,300次就诊 截至2017年9月的监测事件和33例肝细胞癌事件)从和预期招募 在休斯顿退伍军人管理局紧随其后，大多数人感染了治愈的丙型肝炎病毒。真相与和解委员会将发挥杠杆作用，扩大后续行动，并协调 来自两个队列的数据和样本，共同导致了23,000次肝细胞癌监测(和&gt； 300个预期的肝细胞癌)，以及生物库标本，每个病例的临床和放射数据，使其成为 为拟议的研究和其他跨联盟项目提供宝贵的资源。使用来自这些网站的数据 我们将开发和测试新的个性化风险分层指数，以预测未来- 各种病因的肝硬变患者发展为肝细胞癌(目标1)。我们还将开发和 评估结合现有的基于肝癌血液的生物标记物(例如，AFP，AFP L3， DCP)，它们随时间的纵向变化，并在第三阶段研究中选择宿主特征(年龄、病因)。我们会 还检查了该算法在不同肝癌风险级别的患者中的性能(目标2)。我们的工作将设定 将其他患者和肝病相关因素纳入(新的)生物标志物配置文件的框架，以及 无论生物标志物的类型如何，都可能保持高度相关的区域。我们将给予高度评价 甲基化DNA标志物(MDM、液体活检)有望成为预测中国人肝癌风险的独立检测方法 目的3.这些标记物已经在组织病例对照阶段1研究中被确定，可靠的检测方法已经被证实 它们已经开发出来，在2期研究中具有出色的肝细胞癌检测性能。我们将验证 研究中的个体标记分组并训练一种算法，该算法结合MDM以实现最大 性能。在第三阶段的生物标志物研究中，我们将在测试样本中全面和关键地验证算法 以肝细胞癌危险层为基础的分组。我们的方法(同时优化可用标记 保持强劲的前瞻性展望)将对与肝细胞癌相关的 发病率和死亡率。真相与和解委员会将建立在已建立和强大的基础设施和关系的基础上，以 完成建议的研究。它还代表了一种新的、令人兴奋的与其他 U01财团内的调查人员。

The Translational Research Center (TRC) includes a multidisciplinary team of clinical and translational researchers that has a strong track record of collaborative work, with the collective mission of reducing the burden of HCC. At the center of the proposed TRC lie two unique active prospective (in-HCC surveillance) cohorts of patients with cirrhosis. One of the cohorts comes from a Cancer Prevention and Research Institute of Texas (CPRIT) funded ongoing prospective multicenter study (Texas HCC Consortium) that is on target to recruit > 3000 patients with cirrhosis (>12,000 surveillance episodes and 200 expected HCC cases) from diverse etiologies (including cured HCV and non-alcoholic fatty liver disease). These patients are under routine bi-annual surveillance at 5 medical centers in Texas. The second is a cohort of >700 patients (>1,300 visits surveillance episodes and 33 incident HCC cases as of September 2017) recruited from and prospectively followed at the Houston VA, most with cured HCV. The TRC will leverage, extend follow up, and harmonize data and samples from both cohorts, collectively resulting in a > 23,000 episodes of HCC surveillance (and > 300 expected HCC) with bio-banked specimens, clinical and radiological data for each episode, rendering it an invaluable resource for the proposed research and other trans-consortium projects. Using data from these cohorts, we will develop and test novel personalized risk stratification indices for predicting the future ‐ development to HCC in patients with cirrhosis across diverse etiologies (Aim 1). We will also develop and evaluate an early detection algorithm that combines existing HCC blood based biomarkers (e.g., AFP, AFP L3, DCP), their longitudinal changes over time and select host features (age, etiology) in a phase 3 study. We will also examine the performance of this algorithm in patients at different HCC risk strata (Aim 2). Our work will set the framework for incorporating other patient and liver disease related factors into (new) biomarker profiles, an area that is likely to remain highly relevant irrespective of the type of biomarker. We will evaluate highly promising methylated DNA markers (MDMs, liquid biopsy) as an independent test for HCC risk prediction in Aim 3. These markers have been identified in tissue case control phase 1 studies, reliable assays have been developed and they have excellent performance for HCC detection in phase 2 studies. We will validate individual markers in the study cohort and train an algorithm that combines the MDM to achieve maximum performance. In a phase 3 biomarker study, we will validate the algorithm in the test sample overall and in key subgroups based on HCC risk strata. Our approach (optimizing available markers while simultaneously maintaining a strong forward outlook) will have both an immediate and long-lasting impact on HCC related morbidity and mortality. The TRC will build on established and strong infra-structure and relationships to complete the proposed research. It also represents a new and exciting avenue for collaboration with other investigators within the U01 consortium.