Population-Based Analysis of Ovarian Cancer Biomarkers

基于人群的卵巢癌生物标志物分析

• 批准号: 7628977
• 负责人: REBECCA SUTPHEN
• 金额: $ 27.99万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2009-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7628977
• 关键词: Age; Area; Biological Markers; Blood Tests; Blood specimen; CA-125 Antigen; Cancer Patient; Case-Control Studies; Collection; County; Data; Development; Diagnosis; Diagnostic; Disease; Disease-Free Survival; Early Diagnosis; Epithelial ovarian cancer; Ethnic Origin; Florida; Investigation; Lysophosphatidylcholines; Lysophospholipids; Malignant Neoplasms; Malignant neoplasm of ovary; Mass Spectrum Analysis; Menopausal Status; Methods; Patients; Pattern; Pilot Projects; Plasma; Postoperative Period; Proteins; Proteomics; Publishing; Race; Recurrence; Reporting; Sampling; Screening for Ovarian Cancer; Screening procedure; Sensitivity and Specificity; Serum; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Sphingosine; Staging; System; Technology; Time; Woman; Work; base; case-based; improved; lysophosphatidic acid; lysophosphatidylinositol; metropolitan; mortality; population based; prognostic; residence; sphingosine phosphorylcholine; surface enhanced laser desorption ionization; tumor

DESCRIPTION (provided by applicant): The mortality rate for women with ovarian cancer is very high, mainly because there is no effective method for early detection and more than two-thirds of patients have late-stage metastatic disease at the time of diagnosis. Our preliminary data suggest that plasma levels of lysophospholipids (LPL) are elevated in ovarian cancer patients, including patients with early stage disease, and thus may be useful in ovarian cancer detection. Our preliminary LPL data also show statistically significant differences in samples obtained preoperatively versus postoperatively, suggesting that some LPL may return to baseline after successful treatment and may be useful in predicting recurrence. Serum proteomic profiles (PP) have also shown early promise for ovarian cancer detection, but further investigation regarding the identity of the discriminatory proteins is needed. Thus far, published findings regarding these promising biomarkers have been based on limited numbers of convenience samples. Prior to initiation of large-scale screening studies, it is imperative to confirm these preliminary findings in carefully controlled population-based studies. We propose a population-based case-control study of 250 women with invasive epithelial ovarian cancer and 250 healthy control women matched to cases based on age, ethnicity, county of residence and menopausal status. Our aims are to: 1) Estimate the sensitivity and specificity of LPL levels (including LPA, LPI, LPC and S1P) and MALDI-based proteomic profiles for the detection of ovarian cancer, 2) Determine which LPL levels (including LPA, LPI, LPC and S1P) or proteomic profiles, alone or in combination (or in combination with CA125) best discriminate(s) between ovarian cancer and no ovarian cancer and 3) Determine whether LPL levels and/or proteomic profiles are prognostic for disease-free survival in ovarian cancer patients. Use of a well-established population-based ultra-rapid ascertainment network will greatly facilitate the project. The proposed work is expected to contribute directly to development of a blood test for detection of ovarian cancer and management of ovarian cancer patients, and thus may result in improved survival from this deadly disease.

描述（由申请人提供）：卵巢癌女性的死亡率很高，主要是因为没有有效的早期检测方法，而在诊断时有超过三分之二的患者患有晚期转移性疾病。我们的初步数据表明，包括早期疾病的患者在内的卵巢癌患者中溶物磷脂（LPL）的血浆水平升高，因此在卵巢癌检测中可能有用。我们的初步LPL数据还表明，术前与术后获得的样品的统计学显着差异，这表明某些LPL成功治疗后可能会恢复到基线，并且可能有助于预测复发。血清蛋白质组学特征（PP）也显示出早期的卵巢癌检测前景，但是需要进一步研究有关歧视性蛋白质的身份。到目前为止，关于这些有希望的生物标志物的已发表的发现是基于有限数量的便利样本。在进行大规模筛查研究之前，必须在经过精心控制的基于人群的研究中确认这些初步发现。 我们提出了一项基于人群的病例对照研究，对250名侵入性上皮卵巢癌的妇女和250名健康对照妇女符合基于年龄，种族，居住县和绝经状况的病例。我们的目的是：1）估计LPL水平的敏感性和特异性（包括LPA，LPI，LPC和S1P）和基于MALDI的蛋白质组学特征以检测卵巢癌，2）确定LPA，LPA，LPI，LPC，LPC和S1P和蛋白质组合（或组合）（或组合）（包括组合）（或组合）组合（包括组合），或组合组合（包括）。卵巢癌和无卵巢癌和3）确定LPL水平和/或蛋白质组学特征是否对卵巢癌患者的无疾病生存预后。 使用良好的基于​​人群的超优化确定网络将极大地促进该项目。预计拟议的工作将直接为检测卵巢癌和卵巢癌患者治疗的血液测试的发展做出贡献，因此可能会改善这种致命疾病的生存率。