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Glycoprotein biomarkers for the early detection of aggressive prostate cancer

Glycoprotein biomarkers for the early detection of aggressive prostate cancer
用于早期检测侵袭性前列腺癌的糖蛋白生物标志物
批准号:
8135439
负责人:
Hui Zhang
金额:
$43.64万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-01 至 2015-06-30

项目摘要

项目成果

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中文摘要
翻译
描述(申请人提供):前列腺癌是美国男性最常见的癌症类型(今年约有192,280例新病例),也是美国男性癌症死亡的第二大原因(今年将有27,360人死于前列腺癌)。前列腺癌检测和治疗中的一个主要问题是,我们目前还没有使用可用的生物标志物可靠地区分侵袭性前列腺癌和非侵袭性前列腺癌的方法。这导致前列腺癌患者遭受巨大的不必要的痛苦,并导致大量不必要的医疗支出。我们假设特定的糖蛋白和糖蛋白的糖链修饰可以用来区分组织和血清中的侵袭性和非侵袭性前列腺癌。我们提出了四个具体的目标,以开发新的糖蛋白生物标记物,可以检测原始组织和手术前血清中的侵袭性癌症。在目标1中,我们将分析转移性、侵袭性癌症、非侵袭性癌症和正常前列腺癌组织中的多糖和糖蛋白,以确定与侵袭性前列腺癌相关的糖蛋白。在目标2中,我们将为目标1中确定的候选糖肽开发基于MS的高灵敏度、特异性和高通量的SRM分析。我们将优化SRM分析并确定其分析性能,并构建SRM数据库并使其可用于研究社区。在AIM 3中,我们将使用AIM 2开发的SRM分析来验证其他前列腺癌组织中侵袭性前列腺癌的糖蛋白,并使用组织微阵列验证这些组织糖蛋白。在目标4中,我们将使用SRM分析来确定组织中哪些已验证的糖肽可以在患者血清中检测到,因此可以用作血清测试。然后,我们将把SRM分析应用于血清样本,并开发验证的多变量模型,利用血清检测来检测侵袭性前列腺癌。此外,我们将使用一组独立的前列腺癌血清来验证这些标记物和多变量模型。如果成功,识别和验证的生物标记物将由EDRN生物标记物参考(BRL)和临床验证(CVC)实验室以回顾和前瞻性研究进行测试。能够区分侵袭性和非侵袭性前列腺癌的生物标志物将使男性患上非侵袭性前列腺癌,而不是过度治疗,并可能使患有侵袭性前列腺癌的男性在疾病的早期接受适当的治疗。 SRM=选定的反应监控
英文摘要
DESCRIPTION (provided by applicant): Prostate cancer is the most prevalent type of cancer for U.S. men (about 192,280 new cases this year), and it is the second highest contributor to cancer death among men in the U.S. (27,360 will die of it this year). A major issue in prostate cancer detection and therapy is that we currently have no method to reliably distinguish aggressive prostate cancer from non-aggressive prostate cancer using available biomarkers. This leads to significant unnecessary suffering among prostate cancer patients and leads to massive unnecessary health care expenditures. We hypothesize that specific glycoproteins and glycan modifications of glycoproteins can be used to distinguish aggressive from non-aggressive prostate cancer in tissue and serum. We propose in four specific aims to develop novel glycoprotein biomarkers that can detect aggressive cancer in primary tissues and pre-surgical serum. In Aim 1, we will analyze glycans and glycoproteins from metastatic, aggressive cancer, non-aggressive cancer, and normal prostate tissues to identify glycoproteins associated with aggressive prostate cancer. In Aim 2, we will develop highly sensitive, specific, and high throughput MS based SRM assays for the candidate glycopeptides identified from Aim 1. We will optimize the SRM assays and determine their analytical performance, and construct an SRM database and make the assay available to the research community. In Aim 3, we will use the developed SRM assays from Aim 2 to verify the glycoproteins for aggressive prostate cancer in additional prostate cancer tissues and validate these tissue glycoproteins using tissue microarrays. In Aim 4, we will use the SRM assays to determine which of the verified glycopeptides in tissues can be detected in patient's sera, and therefore can be used as serum tests. Then, we will apply the SRM assays to the serum samples and develop validate multivariate models for detecting aggressive prostate cancer using serum tests. In addition, we will validate these markers and multivariate models using an independent testing set of prostate cancer serum. If successful, the identified and validated biomarkers will be tested by EDRN biomarker reference (BRL) and clinical validation (CVC) laboratories in retrospective and prospective studies. Biomarkers capable of distinguishing aggressive from nonaggressive prostate cancer would present men with non-aggressive prostate cancer from overtreatment, and could allow men with aggressive cancer to receive appropriate treatment earlier in the course of their diseases. SRM = selected reaction monitoring
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Mechanism of double-negative T cells in antitumor immunity to breast cancer
  • 批准号:
    10735679
  • 项目类别:
  • 资助金额:
    $36.49万
  • 财政年份:
    2023
  • 负责人:
    Hui Zhang
  • 依托单位:
Biomarker Development Laboratory
  • 批准号:
    10701247
  • 项目类别:
  • 资助金额:
    $37.87万
  • 财政年份:
    2023
  • 负责人:
    Hui Zhang
  • 依托单位:
Biostatistics and Bioinformatics Core
Biostatistics and Bioinformatics Core
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