Identification of ovarian cancer plasma biomarkers

卵巢癌血浆生物标志物的鉴定

• 批准号: 7881494
• 负责人: DAVID W. SPEICHER
• 金额: $ 42.62万
• 依托单位: WISTAR INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7881494
• 关键词: Affect; Biological Assay; Biological Markers; Biological Models; Biological Process; Blood; Blood Volume; Cancer Model; Cancer Patient; Cancer cell line; Cell Line; Cells; Clinical Management; Collection; Conditioned Culture Media; Coupled; Culture Media; Cultured Cells; Data Analyses; Development; Diagnosis; Diagnostic; Disease; Disease Management; Early Diagnosis; Epithelial; Gel; Goals; Health Care Costs; Human; Human Cell Line; Hypoxia; Immune; Immunocompromised Host; Immunodeficient Mouse; In Vitro; Individual; Inflammatory Response; Lead; Malignant Neoplasms; Malignant neoplasm of ovary; Mass Spectrum Analysis; Methods; Modeling; Molecular; Morbidity - disease rate; Mouse Protein; Mus; Operative Surgical Procedures; Organ Culture Techniques; Ovarian; Ovarian Carcinoma; Ovary; Patients; Pattern; Peptides; Phase; Physiological; Plasma; Plasma Proteins; Primary Neoplasm; Protein Fragment; Proteins; Proteome; Public Health; Sampling; Serous; Specificity; Specimen; Staging; Survival Rate; Testing; Tumor Tissue; Tumor-Derived; Validation; Xenograft Model; Xenograft procedure; base; biosignature; candidate validation; cohort; improved; minimally invasive; mortality; mouse model; multiple reaction monitoring; neoplastic cell; novel; ovarian neoplasm; protein profiling; public health relevance; tumor; tumor specificity; tumor xenograft; validation studies; Affect; Biological Assay; Biological Markers; Biological Models; Biological Process; Blood; Blood Volume; Cancer Model; Cancer Patient; Cancer cell line; Cell Line; Cells; Clinical Management; Collection; Conditioned Culture Media; Coupled; Culture Media; Cultured Cells; Data Analyses; Development; Diagnosis; Diagnostic; Disease; Disease Management; Early Diagnosis; Epithelial; Gel; Goals; Health Care Costs; Human; Human Cell Line; Hypoxia; Immune; Immunocompromised Host; Immunodeficient Mouse; In Vitro; Individual; Inflammatory Response; Lead; Malignant Neoplasms; Malignant neoplasm of ovary; Mass Spectrum Analysis; Methods; Modeling; Molecular; Morbidity - disease rate; Mouse Protein; Mus; Operative Surgical Procedures; Organ Culture Techniques; Ovarian; Ovarian Carcinoma; Ovary; Patients; Pattern; Peptides; Phase; Physiological; Plasma; Plasma Proteins; Primary Neoplasm; Protein Fragment; Proteins; Proteome; Public Health; Sampling; Serous; Specificity; Specimen; Staging; Survival Rate; Testing; Tumor Tissue; Tumor-Derived; Validation; Xenograft Model; Xenograft procedure; base; biosignature; candidate validation; cohort; improved; minimally invasive; mortality; mouse model; multiple reaction monitoring; neoplastic cell; novel; ovarian neoplasm; protein profiling; public health relevance; tumor; tumor specificity; tumor xenograft; validation studies

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.

描述（由申请人提供）：该应用的总体目标是发现和验证人类卵巢癌的多种新型血浆蛋白生物标志物。我们最初将通过对两个卵巢癌互补模型的平行分析发现大量候选生物标志物，并随后验证人类患者同类中最有希望的候选生物标志物。在AIM 1中，我们将使用一种异种移植模型，其中将人类卵巢癌细胞系注射到点头/SCID/3C - / - （NOG）的两个卵巢中，允许小鼠和人类原发性肿瘤在收集血浆之前生长。将使用几种现有的早期通过浆液性癌细胞系，并将额外的早期通过细胞系从人类卵巢肿瘤中得出。低丰度等离子体蛋白将使用4-D蛋白分析方法鉴定，该方法能够检测到血浆中低Ng/mL至PG/ML范围的许多蛋白质。通过高质量精度质谱法与严格的数据分析结合，将明确鉴定由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类和小鼠蛋白质。低丰度的人类蛋白质可以以> 99％的置信度鉴定，并且在重复靶向LC-MS/MS分析的情况下，可以证实蛋白质鉴定将被视为候选生物标志物。在AIM 2中，我们将使用新鲜的卵巢肿瘤分析短期器官培养物的分泌组（棚和分泌蛋白）。将比较常氧和低氧条件对蛋白质脱落的影响。此外，将比较来自相同肿瘤和异种移植肿瘤的短期器官培养物的秘密组，从同一细胞系进行比较，以确定不同的环境和生理环境如何影响卵巢肿瘤细胞蛋白脱落。在AIM 3中，我们将从AIM 1和2中选择最有希望的候选生物标志物进行验证分析。 AIM 1和2中发现研究中的候选生物标志物将根据：正常人血浆中的丰度；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。最高优先级候选生物标志物将随后使用多重多重反应监测（MRM）质谱分析来验证，以量化卵巢患者和对照组的血浆中的生物标志物水平。单个生物标志物以及生物标志物的群体将用于预测早期卵巢癌公共卫生相关性的能力：如果该疾病仅限于诊断时卵巢，则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。最近开发了强大的基于质谱的血浆蛋白质组分析方法，再加上小鼠卵巢癌模型，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，这些候选者将改善对这种疾病的早期诊断和临床管理。