Glycoprotein Signatures as Biomarkers for Breast Cancer

描述(申请人提供)：乳腺癌是一种异质性疾病。为了提高我们对这种疾病的了解，更好地治疗它的各种形式，需要更多关于其亚型之间发生的分子变异的信息。需要更好的生物标志物来检测它并监测对治疗的反应。此外，还需要改进方法来确定无病生存与疾病复发的几率。虽然在确定乳腺癌的基因表达方面已经取得了重大进展，但我们对这种疾病各种形式的蛋白质表达谱的了解是有限的。这项研究的长期目标是(1)确定将在临床上用于更有效地检测和治疗乳腺癌的糖蛋白生物标记物特征；(2)开发可用于识别新的治疗靶点的糖蛋白乳腺癌数据库，并提高我们对导致乳腺癌的肿瘤启动细胞和机制的了解。目前应用的目标是识别能够区分腔内和基底乳腺癌细胞系的糖蛋白特征，以及能够区分正常乳腺上皮细胞和恶性细胞系的糖蛋白特征。中心假设是，来自恶性乳腺肿瘤和来自正常乳腺上皮细胞的细胞系具有不同的糖蛋白图谱，可分别用于将它们划分为不同的乳腺癌亚型和正常乳腺上皮细胞。我们还假设，这些糖蛋白的一个子集将作为区分三种恶性乳腺癌亚型和正常乳腺上皮细胞的标志。在强大的初步数据的指导下，这些假说将通过以下具体目标进行检验：1a)确定正常乳腺上皮细胞以及腔和基底乳腺癌细胞系的糖蛋白特征；1b)定义claudin-low类型的乳腺癌细胞糖蛋白质组；2)使用免疫荧光来验证将乳腺癌细胞系分为基底和腔内亚型的糖蛋白特征。目标1将使用基于质谱学的方案，结合分级聚类分析来完成，该方案先前在申请人手中演示，以产生区分基础乳腺癌细胞系和管腔癌细胞系的糖蛋白签名。目的2使用流式细胞术和抗糖蛋白抗体来验证质谱学衍生的特征。识别正常乳腺上皮和不同亚型乳腺癌的细胞表面和分泌糖蛋白的差异，将促进这种异质性疾病的诊断和治疗。拟议研究的成功完成将导致一组抗体的鉴定，这些抗体可用于将乳腺癌细胞分为基底或管腔亚型，并区分恶性和非恶性细胞。通过这项工作产生的糖蛋白数据库将提供关于正常乳腺上皮和乳腺癌亚型之间潜在重要生物学差异的信息，以及帮助确定潜在的治疗靶点。 公共卫生相关性：拟议的研究通过提供可转化为潜在的新生物标记物和治疗靶点的新知识，解决了改进乳腺癌诊断工具和治疗方法的需要。因此，拟议的研究与NIH任务的一部分有关，该部分涉及发展有助于减轻人类疾病负担和延长寿命的基础知识。

DESCRIPTION (provided by applicant): Breast cancer is a heterogeneous disease. To improve our understanding of this disease and better treat its various forms, more information about the molecular variations that occur among its subtypes is needed. Better biomarkers are needed for its detection and for monitoring response to treatment. Additionally, improved methods for determining the chances of disease-free survival vs. disease recurrence are also needed. Although significant progress has been made in defining gene expression in breast cancer, our knowledge of protein expression profiles of various forms of this disease is limited. The long-term goals of the research are (1) to identify glycoprotein biomarker signatures that will be clinically useful for more effective detection and treatment of breast cancer; and (2) to develop a glycoproteomic breast cancer database that can be used to identify new therapeutic targets and improve our understanding of tumor-initiating cells and mechanisms that give rise to breast tumors. The objective of the current application is to identify glycoprotein signatures that can distinguish between luminal and basal breast cancer cell lines, and glycoprotein signatures that can distinguish between normal breast epithelial cells and malignant cell lines. The central hypothesis is that cell lines derived from malignant breast tumors and from normal breast epithelial cells have distinct glycoprotein profiles that can be used to classify them into various breast cancer subtypes and normal breast epithelial cells, respectively. We also hypothesize that a subset of these glycoproteins will serve as signatures that will distinguish among the three malignant breast cancer subtypes, and normal breast epithelial cells. Guided by strong preliminary data, these hypotheses will be tested through the following Specific Aims: 1a) Identify glycoprotein signatures for normal breast epithelial cells, and for luminal and basal breast cancer cell lines; 1b) Define the claudin-low type breast cancer cell glycoproteome; 2) Use immunofluorescence to validate glycoprotein signatures that classify breast cancer cell lines into basal and luminal subtypes. Aim 1 will be accomplished using a mass spectrometry-based protocol, coupled with hierarchical cluster analysis, previously demonstrated in the applicant's hands to result in a glycoprotein signature that distinguishes basal from luminal breast cancer cell lines. Aim 2 uses flow cytometry and antibodies to glycoprotein to validate the mass spectrometry-derived signature. Identifying differences in cell surface and secreted glycoprotein profiles from normal breast epithelia and those from various subtypes of breast cancer will advance diagnosis and treatment of this heterogeneous disease. Successful completion of the proposed studies will result in the identification of a set of antibodies that can be used to classify breast cancer cells into basal or luminal subtypes, and to discriminate malignant from non-malignant cells. The glycoprotein database produced through this work will provide information on potentially important biological differences between normal breast epithelia and breast cancer subtypes, as well as help identify potential therapeutic targets. PUBLIC HEALTH RELEVANCE: The proposed research addresses the need for improved diagnostic tools and treatments for breast cancer by providing new knowledge that can be translated into potentially new biomarkers and therapeutic targets. Thus, the proposed research is relevant to the part of NIH's mission that pertains to developing fundamental knowledge that will help to reduce the burdens of human illness and lengthen life.