Validation and Advanced Development of Glycan Node Analysis in Lung Cancer Research

 描述（由申请人提供）：糖基转移酶是以非模板驱动方式构建聚糖的糖聚合物构建酶。异常的糖基转移酶活性产生异常的糖聚合物（聚糖），这是基本上所有已知癌症的标志。异常聚糖似乎促进癌细胞避免先天免疫检测、从天然位点脱离并穿过非天然组织的能力，从而允许癌细胞转移。糖基转移酶在特定的聚糖聚合物分支点（链连接位点）处构建，并且通常表现出严格的供体、受体和连接特异性。考虑到这些想法，我们开发了这样的想法，即特定的单糖和连接特异性聚糖聚合物链连接（聚糖“节点”，我们称之为），如果从生物样品中所有聚糖结构的池中分解，浓缩和定量，可能会成为异常糖基转移酶活性的直接分子替代物-与传统的糖组学方法相比，这些方法着眼于完整的完整聚糖。为了实现这种新的“自下而上”的糖组学概念，设计了一种分析方法，并于最近发表，该方法允许仅使用10微升血清或血浆同时定量来自N-、O-和脂质连接聚糖的二十多种类型的聚糖节点。现已研究了来自3个独立肺癌初步研究队列的样本，包括1）来自纽约大学肺癌生物标志物中心的31名IA期肺腺癌患者和31名单独配对的年龄/性别/吸烟匹配对照（NCI EDRN网站），2）30名新诊断的肺癌患者和29名年龄/性别/吸烟状态匹配的对照，来自INCO-COPERNICUS中欧和东欧肺癌研究，以及3）来自商业生物库的24名肺癌患者和25名年龄/性别匹配的对照。分析的每个队列在区分病例和对照方面都产生了有希望的结果;所有人都对肺癌患者中异常产生的聚糖结达成了普遍一致。该项目的目标是验证聚糖结分析作为肺癌早期检测工具和肺肿瘤组织学分类器的有效性。既然检测的分析和计算（数据分析）程序已经锁定，这将通过仔细选择和分析大量肺癌患者样本来完成，然后进行定义明确的检测生物统计分析和组织学分类的多变量模型构建。样本集将包括1）来自纽约大学肺癌生物标志物中心的170例IA期肺腺癌病例和170例年龄/性别/吸烟匹配对照的扩展集，2）来自INCO-COPERNICUS研究的500例新诊断病例和500例对照，和3）来自休斯顿德克萨斯大学MD安德森癌症中心正在进行的研究的类似的100例病例和100例对照组。这项研究将明确定义血液中聚糖结作为肺癌生物标志物的作用。

 DESCRIPTION (provided by applicant): Glycotransferases are sugar polymer-building enzymes that build glycans in a non-template driven fashion. Aberrant glycotransferase activity creates abnormal sugar polymers (glycans) which are a hallmark of essentially every known cancer. Abnormal glycans appear to facilitate the ability of cancer cells to avoid innate immune detection, detach from native sites, and traverse non-native tissues, allowing the cancer cells to metastasize. Glycotransferase enzymes build at specific glycan polymer branch-points (chain link sites) and, as a general rule, exhibit strict donor, acceptor, and linkage specificity. With these ideas in mind we developed the idea that specific monosaccharide-and-linkage-specific glycan polymer chain links (glycan "nodes", as we call them), if broken down, condensed and quantified from the pool of all glycan structures in a biological sample, could potentially serve a direct molecular surrogates of aberrant glycotransferase activity-in general contrast to traditiona glycomics approaches that look at whole, intact glycans. To enable this novel 'bottom-up' glycomics concept, an analytical methodology was devised and recently published that allows the simultaneous quantification of more than two dozen types of glycan nodes from N-, O-, and lipid-linked glycans relative to one another using only 10 microliters of blood serum or plasma. Samples from 3 independent lung cancer pilot study cohorts have now been studied, including 1) 31 stage IA lung adenocarcinoma patients and 31 individually paired age/gender/smoking matched controls from the NYU Lung Cancer Biomarker Center (an NCI EDRN site), 2) 30 newly diagnosed lung cancer patients and 29 age/gender/smoking status-matched controls from the INCO-COPERNICUS lung cancer in central and eastern Europe study, and 3) 24 lung cancer patients and 25 age/gender matched controls from a commercial biobank. Each cohort analyzed produced promising results with regard to distinguishing cases from controls; all were in general agreement about which glycan nodes are aberrantly produced in lung cancer patients. The goal of this project is to validate glycan node analysis as a tool for the early detection of lung cancer and as a classifier of lung tumor histology. Now that analytical and computational (data analysis) procedures for detection are locked down, this will be accomplished by carefully selecting and analyzing large sets of lung cancer patient samples, followed by well-defined biostatistical analysis for detection and multivariate model building for histology classification. Sample sets will include 1) an expanded set of 170 stage IA lung adenocarcinoma cases and 170 age/gender/smoking matched controls from the NYU Lung Cancer Biomarker Center, 2) 500 newly diagnosed cases and 500 controls from the INCO-COPERNICUS study, and 3) a similar set of 100 cases and 100 controls from ongoing studies at The University of Texas MD Anderson Cancer Center in Houston. This study will unambiguously define the role of blood-based glycan nodes as lung cancer biomarkers.