METABOLITE BIOIMARKERS IN THE DEVELOPMENT OF ESOPHAGEAL ADENOCARCINOMA

描述（由申请人提供）：近几十年来，美国食管腺癌（EA）的发病率增加了300%，而中位生存期仍然是糟糕的10个月。已经确定了EA及其前驱物巴雷特食管（BE）的几个主要危险因素，包括反流、高加索人种、男性、肥胖和吸烟，但一个关键的挑战仍然是确定最高风险个体，因为大多数反流患者不会发展为BE，大多数BE患者不会发展为EA。尽管许多遗传、表观遗传和基于蛋白质的生物标志物已经被评估为检测或预测疾病进展的能力。由于所需检测的技术复杂性、标记面板的有限敏感性/特异性以及缺乏验证研究，新型临床试验的发展受到了阻碍。根据我们的初步数据，我们预计代谢组学，即低分子量代谢物的量化，为识别有效的生物标志物提供了一种有利的替代方法，以帮助对EA风险人群进行风险分层。许多研究已经确定，细胞能量代谢在广泛的癌症中发生了根本性的改变，越来越多的证据表明，癌症相关的代谢变化可以在血液和尿液中检测到。在这项提议中，我们假设基于血清的代谢物可以区分从胃食管反流（GERD）到BE再到EA的疾病进展的多个阶段。使用先进的分析方法和一个特征良好的生物库，这项合作提议旨在识别和验证与这些疾病状态相关的新型代谢物标记。在Aim 1中，将对分布在不同病例类型（GERD、be、高度不典型增生/EA）的60例患者血清样本进行全球代谢分析，以确定在两种或所有这些疾病中存在显著差异的候选代谢物生物标志物的扩展池。在Aim 2中，这组代谢物的靶向分析加上先前确定的28个候选小组将在240个患者样本中进行。利用偏最小二乘判别分析构建样本分类统计模型（be - gerd和HGD/EA-BE），并通过蒙特卡罗交叉验证对模型性能进行评估。这些目标的成功完成将为产生新的无创工具奠定基础，根据疾病进展的可能性对EA风险患者进行分层。这些工具有可能增加早期发现和减少不必要的监测，通过集中对癌症风险最高的人进行密集的临床监测。这些研究将为未来更大规模的研究奠定基础，这些研究将利用国际巴雷特和食管腺癌（BEACON）联盟的广泛资源，在人群中验证我们的发现，并确定可能提高我们预测模型准确性和适用性的关键修改因素。

DESCRIPTION (provided by applicant): The incidence of esophageal adenocarcinoma (EA) in the US has increased 300% over recent decades while median survival remains an abysmal 10 months. Several major risk factors have been defined for EA and its precursor, Barrett's esophagus (BE), including reflux, Caucasian race, male gender, obesity, and smoking, but a key challenge remains the identification of individuals at highest risk, since most with reflux do not develop BE, and most with BE do not progress to EA. While a number of genetic, epigenetic, and protein-based biomarkers have been evaluated for their ability to detect or predict disease progression, the development of novel clinical tests has been hampered by technical complexities of the required assays, limited sensitivity/specificity of the marker panels and lack of validation studies. Based on our preliminary data, we anticipate that metabolomics, the quantification of low molecular weight metabolites, offers a favorable alternative approach for identifying effective biomarkers to aid in risk stratification for those at risk of EA. Numerou studies have established that cellular energy metabolism is fundamentally altered in a broad spectrum of cancers, and increasing evidence indicates that cancer-associated metabolic changes are detectable in blood and urine. In this proposal, we hypothesize that serum-based metabolites can distinguish multiple stages of disease progression along the pathway from gastroesophageal reflux (GERD) to BE to EA. Using advanced profiling methods and a well-characterized biorepository, this collaborative proposal seeks to identify and validate novel metabolite markers linked to these disease states. In Aim 1, global metabolic profiling will be conducted on a set of 60 patient serum samples distributed across case type (GERD, BE, high- grade dysplasia/EA) to identify an expanded pool of candidate metabolite biomarkers that differ significantly across two or all of these conditions. In Aim 2, this targeted profiling of this set f metabolites plus a previously-identified panel of 28 candidates will be conducted on a larger set of 240 patient samples. Based on validated candidate markers identified, statistical models for sample classification (BE-GERD and HGD/EA-BE) will be constructed using partial least-squares discriminant analysis, and model performance estimated by Monte Carlo cross-validation. Successful completion of these aims will lay the groundwork for generating novel non-invasive tools for stratifying patients at risk of EA according to their likelihood of disease progression. Such tools have the potential to increase early detection and reduce unnecessary surveillance, by focusing intensive clinical monitoring on those at highest risk of cancer. These investigations will set the stage for larger-scale future studies, which will leverage the extensiv resources of the international Barrett's and Esophageal Adenocarcinoma (BEACON) consortium to validate our findings across population groups and identify key modifying factors that may enhance the accuracy and applicability of our predictive models.