METABOLITE BIOIMARKERS IN THE DEVELOPMENT OF ESOPHAGEAL ADENOCARCINOMA

描述(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可怕的10个月。对于EA及其前体Barrett食道(BE)，已经定义了几个主要的危险因素，包括反流、高加索种族、男性、肥胖和吸烟，但一个关键的挑战仍然是识别最高风险的个体，因为大多数有反流的人不会发展成BE，大多数BE不会进展到EA。虽然许多遗传的、表观遗传的和基于蛋白质的生物标记物被评估为它们检测或预测疾病进展的能力，但由于所需分析的技术复杂性、标记板的有限的敏感性/特异性以及缺乏验证研究，新的临床测试的发展一直受到阻碍。根据我们的初步数据，我们预计代谢组学，即对低分子代谢物的量化，为识别有效的生物标记物提供了一种有利的替代方法，以帮助那些有EA风险的人进行风险分层。Numerou的研究已经证实，在广泛的癌症中，细胞能量代谢发生了根本性的改变，越来越多的证据表明，在血液和尿液中可以检测到与癌症相关的代谢变化。在这个建议中，我们假设基于血清的代谢物可以区分疾病从胃食道反流(GERD)到EA的多个阶段的进展。使用先进的分析方法和特征良好的生物资料库，这项合作提案寻求识别和验证与这些疾病状态相关的新代谢物标记物。在目标1中，将对一组分布在不同病例类型(GERD、BE、高度异型增生/EA)的60名患者血清样本进行全球代谢概况分析，以确定在两种或所有这些情况下显著不同的候选代谢物生物标志物的扩大池。在目标2中，对这组代谢物加上先前确定的28个候选样本的有针对性的分析将在240个患者样本的更大集合上进行。基于识别的有效候选标记，将使用偏最小二乘判别分析构建样本分类(BE-GERD和HGD/EA-BE)的统计模型，并通过蒙特卡罗交叉验证来评估模型的性能。这些目标的成功完成将为开发新的非侵入性工具奠定基础，这些工具用于根据疾病进展的可能性对有EA风险的患者进行分层。这种工具有可能通过将密集的临床监测集中在癌症风险最高的人身上，来增加早期发现和减少不必要的监测。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Barrett‘s和食管腺癌(Beacon)联盟的广泛资源，在不同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正因素。

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.