Identification of ovarian cancer plasma biomarkers

描述（由申请人提供）：本申请的总体目标是发现和验证人卵巢癌的多种新型血浆蛋白生物标志物。我们最初将通过对卵巢癌的两个互补模型的平行分析发现大量的候选生物标志物，随后将在人类患者队列中验证最有希望的候选生物标志物。在目标1中，我们将使用异种移植模型，其中将人卵巢癌细胞系注射到NOD/SCID/3c-/-（NOG）小鼠的两个卵巢中，并在收集血浆之前允许人原发性肿瘤生长。本研究将使用几种现有的早期传代浆液性癌细胞系，并将使用来自人卵巢肿瘤的其他早期传代细胞系。将使用4-D蛋白质谱分析方法鉴定低丰度血浆蛋白，该方法能够检测血浆中低ng/ml至pg/ml范围内的许多蛋白质。将使用高质量准确度质谱法结合严格的数据分析明确鉴定人肿瘤分泌或脱落的蛋白质，以基于物种特异性序列差异区分人和小鼠蛋白质。可以以>99%置信度鉴定的低丰度人蛋白质，并且其中馏分的重复靶向LC-MS/MS分析可以确认蛋白质鉴定，将被视为候选生物标志物。在目标2中，我们将使用新鲜卵巢肿瘤分析来自短期器官培养物的分泌组（脱落和分泌蛋白）。将比较常氧和低氧条件对蛋白质脱落的影响。此外，将比较短期器官培养物、来自相同肿瘤的早期传代建立的培养物和来自相同细胞系的异种移植肿瘤的分泌物组，以确定不同的环境和生理背景如何影响卵巢肿瘤细胞蛋白质脱落。在目标3中，我们将从目标1和2中选择最有希望的候选生物标志物进行验证分析。目标1和2中发现研究的候选生物标志物将根据以下因素进行优先排序：正常人血浆中的丰度;对卵巢肿瘤的特异性;不同卵巢肿瘤脱落的一致性和生物学功能。随后将使用多重多反应监测（MRM）质谱测定法对最高优先级的候选生物标志物进行验证，以定量卵巢患者和对照血浆中的生物标志物水平。将评估单个生物标志物以及生物标志物组预测早期卵巢癌的能力。公共卫生相关性：如果疾病在诊断时仅限于卵巢，卵巢癌患者的生存率约为90%。不幸的是，早期卵巢肿瘤通常是无症状的，大约75%的病例在癌症扩散后被诊断出来。最近开发的强大的基于质谱的血浆蛋白质组分析方法与小鼠卵巢癌模型相结合，为系统地发现许多新的卵巢癌生物标志物候选者提供了独特的机会，这将导致改善这种疾病的早期诊断和临床管理。

DESCRIPTION (provided by applicant): The overall goal of this application is to discover and validate multiple novel plasma protein biomarkers of human ovarian cancer. We will initially discover a substantial number of candidate biomarkers through parallel analysis of two complementary models of ovarian cancers and will subsequently validate the most promising candidate biomarkers in a human patient cohort. In Aim 1, we will use a xenograft model where human ovarian cancer cell lines are injected into both ovaries of NOD/SCID/3c-/- (NOG) mice and human primary tumors are allowed to grow prior to collection of the plasma. Several existing early passage serous cancer cell lines will be used and additional early passage cell lines will be derived from human ovarian tumors for this study. Low abundance plasma proteins will be identified using a 4-D protein profiling method that is capable of detecting many proteins in the low ng/ml to pg/ml range in plasma. Proteins secreted or shed by the human tumors will be unambiguously identified using high mass accuracy mass spectrometry coupled with rigorous data analysis to distinguish human and mouse proteins based on species-specific sequence differences. Low abundance human proteins that can be identified with >99% confidence and where repeat targeted LC-MS/MS analysis of the fraction can confirm the protein identification will be considered as candidate biomarkers. In Aim 2, we will analyze the secretome (shed and secreted proteins) from short term organ cultures using fresh ovarian tumors. The effects of normoxic and hypoxic conditions on protein shedding will be compared. In addition, the secretomes of short term organ culture, early passage established cultures from the same tumor and xenograft tumors from the same cell line will be compared to determine how different environmental and physiological context affects ovarian tumor cell protein shedding. In Aim 3, we will select the most promising candidate biomarkers from Aims 1 and 2 for validation analyses. Candidate biomarkers from the discovery studies in Aims 1 and 2 will be prioritized based upon: abundance in normal human plasma; specificity for ovarian tumors: consistency of shedding by different ovarian tumors, and biological function. The highest priority candidate biomarkers will be subsequently validated using multiplexed multiple reaction monitoring (MRM) mass spectrometry assays to quantify biomarker levels in plasma of ovarian patients and controls. Individual biomarkers as well as groups of biomarkers will be evaluated for their capacity to predict early stage ovarian cancers PUBLIC HEALTH RELEVANCE: Survival rates of ovarian cancer patients are about 90% if the disease is confined to the ovaries at diagnosis. Unfortunately, early stage ovarian tumors are usually asymptomatic and about 75% of cases are diagnosed after the cancer has spread. Recently developed powerful mass spectrometry-based plasma proteome analysis methods coupled with mouse ovarian cancer models provide unique opportunities to systematically discover many new ovarian cancer biomarker candidates that will lead to improved early diagnosis and clinical management of this disease.