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

 描述(申请人提供)：糖基转移酶是以非模板驱动的方式构建多糖的糖聚合物构建酶。异常的糖转移酶活性会产生异常的糖聚合物(多糖)，这几乎是所有已知癌症的标志。异常的葡聚糖似乎有助于癌细胞逃避天然免疫检测，脱离天然部位，穿过非天然组织，允许癌细胞转移。糖基转移酶构建于特定的聚糖链支点(链连接部位)，通常表现出严格的供体、受体和连接专一性。考虑到这些想法，我们提出了这样的想法，即特定的单糖和连接特定的糖链聚合物链(我们称之为糖链“节点”)，如果从生物样本中所有的糖链结构中被分解、浓缩和量化，可能会成为糖转酶活性异常的直接分子替代品--这通常与传统的糖组学方法着眼于完整的糖链形成鲜明对比。为了实现这一新颖的“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微升的血清或血浆就能同时定量N-、O-和脂键连接的糖链中的二十多种类型的糖链节点。现已研究了3个独立肺癌试点研究队列的样本，包括1)31名IA期肺腺癌患者和31名年龄/性别/吸烟配对的对照组，这些样本来自纽约大学肺癌生物标记物中心(NCI EDRN的一个网站)；2)30名新诊断的肺癌患者和29名年龄/性别/吸烟状况匹配的对照组，来自中东欧哥白尼肺癌研究所；3)24名肺癌患者和25名年龄/性别匹配的对照组来自商业生物库。每个被分析的队列在区分病例和对照方面都产生了令人振奋的结果；所有人都大体上一致认为肺癌患者中哪些糖结节是异常产生的。该项目的目标是验证糖结节分析作为肺癌早期检测的工具和作为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)程序已经确定，这将通过仔细选择和分析大量肺癌患者样本来完成，随后是用于检测的明确的生物统计分析和用于组织学分类的多变量模型建立。样本组将包括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名年龄/性别/吸烟匹配的对照的扩展集合，2)来自国际癌症研究所研究的500例新诊断病例和500名对照，以及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名对照。这项研究将明确定义基于血液的糖结节作为肺癌生物标记物的作用。

 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.