A New Tool for Discovery, Assay and Validation of Biomarkers in Prostate Cancer

描述(由申请人提供)：在美国，前列腺癌的发病率每年约导致30,000名男性死亡(1)。前列腺特异性抗原筛查可以发现早期前列腺癌，但它是非特异性的，不被疾病控制和预防中心推荐(2)。因此，前列腺癌早期诊断和预后的新生物标志物的发现对于改善疾病的管理和开发新的治疗方法以提高存活率将是重要的。该项目第一阶段的目标是优化一种新的蛋白质组学技术，即人血清中的差异捕获蛋白质组学(DCP)。提交这项建议的研究团队在差异蛋白质组学公司，该公司已经获得了这项技术的专利。DCP识别两个液体或组织样本之间的蛋白质组成的差异，并同时为每个已识别的差异蛋白质创建亲和试剂。DCP方法由三个主要部分组成：DCP部分1：使用创新的方法选择源自展示文库的亲和结合剂。选择的产品是一组亲和试剂(来自当前工作计划中噬菌体展示文库的多肽)，它们只识别两个样本之间在数量或类型上不同的蛋白质(图2，DCP步骤1-4)。DCP组分2：噬菌体展示的多肽被整体重新格式化成单个亲和矩阵，适合同时纯化所有检测到的差异蛋白(图2，DCP步骤5)。DCP组分3：使用特定的亲和矩阵从两个样品中捕获差异蛋白。然后使用质谱仪对这些蛋白质进行鉴定和定量，从而可以直接比较两个样品中它们的水平和类型(图2，DCP步骤6-7)。到目前为止，优化DCP技术的研究已经成功地集中在模型系统上。然而，DCP在患者血清样本中发现癌症生物标记物的应用尚未得到优化研究的证实。这里提出了这样一项研究，其中掺杂的前列腺癌相关生物标记物(代表模型前列腺癌患者样本)将在临床样本水平上进行可检测性测试。这项拟议的研究将使用五种特定的前列腺癌相关蛋白，对于这些蛋白，有商业上可用的免疫分析。它们是前列腺特异性抗原、激肽释放酶6、前列腺酸性磷酸酶、胰岛素样生长因子结合蛋白3和转化生长因子β1。具体目标1：确保汇集的随机肽噬菌体展示文库实际上包含针对所选五种癌症蛋白的结合噬菌体物种。具体目标2：确定差异捕获蛋白质组学(Tm)检测两个人类血清样本(其中一个样本掺杂了五种癌症相关蛋白质的混合物)之间的差异的程度，以及作为这一过程的一部分产生的亲和捕获试剂的特异性。这项第一阶段研究的成功将导致第二阶段的项目，即测试多个前列腺癌患者血清样本，以发现、分析和验证新的生物标记物。公共卫生相关性：差异捕获蛋白质组学将比目前发现生物标记物的方法更具成本和时间效益。这项技术是一项突破性的系统，用于集成发现、验证和验证生物标记物，从而导致临床重要的分析，包括前列腺癌生物标记物的分析。

DESCRIPTION (provided by applicant): In the United States, the incidence of prostate cancer accounts for approximately 30,000 male deaths annually (1). Prostate-specific antigen screening can detect early-stage prostate cancer, but it is nonspecific and is not recommended by the Centers for Disease Control and Prevention (2). The discovery of new biomarkers for early diagnosis and prognosis of prostate cancer will therefore be important for improvements in the management of the disease and in developing new treatments to increase survival rates. The goal for Phase I of this Project is to optimize a new proteomics technology, Differential Capture Proteomics(tm) (DCP) in human serum. The team of investigators submitting this proposal is at Differential Proteomics Inc., which has received a patent for the technology. DCP identifies the differences in protein composition between two fluid or tissue samples and also simultaneously creates affinity reagents for each of the identified difference proteins. The DCP process consists of three major components: DCP Component 1: Affinity binders originating from a display library are selected using an innovative process. The product of the selection is a set of affinity reagents (peptides from phage display libraries in the current work plan) that recognize just the proteins that are different in amount or type between the two samples (Figure 2, DCP Steps 1-4). DCP Component 2: The phage-displayed peptides are reformatted en masse into a single affinity matrix suitable for purifying all of the detected difference proteins simultaneously (Figure 2, DCP Step 5). DCP Component 3: The specific affinity matrix is used to capture the difference proteins from each of the two samples. These proteins are then identified and quantified using mass spectrometry, allowing a direct comparison of their level and type in the two samples (Figure 2, DCP Steps 6-7). Studies to date to optimize the DCP technology have successfully focused on a model system. However, the application of DCP for cancer biomarker discovery in patient serum samples has yet to be demonstrated with an optimization study. Such a study is proposed here, in which doped prostate cancer-associated biomarkers (representing a model prostate cancer patient sample) will be tested for detectability at clinical sample level concentrations. The proposed study will use five specific prostate cancer-associated protein proteins, for which there are commercially available immunoassays. They are Prostate Specific Antigen, Kallikrein 6, Prostate Acid Phosphatase, IGF Binding Protein 3, and TGF Beta 1. The study proposed herein has two specific aims. Specific Aim 1: To ensure that the pooled Random Peptide Phage Display Libraries actually contain binding phage species against each of the five cancer proteins chosen. Specific Aim 2: To determine how well Differential Capture Proteomics(tm) detects the differences between two human serum samples, one of which is doped with the mixture of five cancer-associated proteins, and how specific the affinity capture reagents are that are generated as part of the process. The success of this Phase I study will lead to a Phase II project for testing multiple prostate cancer patient serum samples for novel biomarker discovery, assay generation, and validation. PUBLIC HEALTH RELEVANCE:Differential Capture Proteomics will be significantly more cost- and time-effective than current approaches to biomarker discovery. This technology is a breakthrough system for the integrated discovery, verification, and validation of biomarkers leading to clinically important assays, including those for biomarkers of prostate cancer.