Novel Serum, Plasma, and Urine Biomarkers of Ovarian Can

卵巢癌的新型血清、血浆和尿液生物标志物

• 批准号: 6991022
• 负责人: Steven J Skates
• 金额: $ 23.61万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6991022
• 关键词: biomarker; blood chemistry; brca gene; cancer risk; diagnosis design /evaluation; diagnostic tests; early diagnosis; fallopian tube surgery; female; genetic susceptibility; high throughput technology; human tissue; liquid chromatography mass spectrometry; mass spectrometry; monoclonal antibody; neoplasm /cancer diagnosis; neoplasm /cancer genetics; ovary neoplasms; patient oriented research; proteomics; two dimensional gel electrophoresis; urinalysis

The aim of this study is to identify novel ovarian cancer biomarkers using high throughput mass spectrometry and other proteomic techniques on serum, plasma, and urine samples from high risk women undergoing risk reducing salpingo oophorectomy (RRSO). An often used approach to identifying cancer biomarkers is to obtain samples from subjects already clinically identified as having the target cancer but prior to any treatment intervention, and compare with samples from subjects without the disease. Putative markers which separate well the cases from the controls are then proposed as candidates for further testing, especially markers which separate early stage cases from controls. High throughput mass spectroscopy coupled with non-linear statistical analyses has recently demonstrated that patterns of peaks in the spectra can separate all cases from most controls. However, two issues arise with using pre-operative samples. The first is that clinically identified early stage disease is likely to be bulky, symptomatic disease, and the markers identified may be indicators only of bulky disease late in the carcinogenesis process. The second issue is that clinically identified early stage disease is not the target disease for an early detection program. In fact, an early detection program aims to identify asymptomatic subjects in early stage disease that would have been clinically identified in late stage disease. Subjects planning on RRSO form an ideal cohort for identification of biomarkers which are sensitive to low volume, asymptomatic, early stage disease. Usually individuals who undergo RRSO are at high risk of ovarian cancer due to known BRCA mutations or a strong family history of ovarian and breast cancer. Occult ovarian cancer has been identified in approximately 10% of ovaries following RRSO. Biospecimens will be obtained from a large cohort of subjects undergoing RRSO prior to and following surgery. A comprehensive pathology review will identify the subjects with occult ovarian cancer (cases) and subjects without ovarian cancer (controls). High throughput mass spectrometry followed by non-linear statistical classification methods will be utilized to identify patterns of peaks which separate cases as much as possible from non-cases. An alternative methodology, 2D DIGE (2 dimensional digital gel electrophoresis) will also be applied to identify potential serum/plasma or urine biomarkers. Following identification of the most promising peak/spot pattern, proteins and peptides corresponding to the peaks/spots will be identified through LC-MS/MS. Monoclonal antibodies will be developed for the six most important proteins/peptides in the pattern, immunoassays developed from the antibodies, and finally tested against the remaining aliquots ofbiospecimens to verify and enhance pattern identification through further application of non-linear classification methods.

本研究的目的是利用高通量质谱和其他蛋白质组学技术，对接受降低风险输卵管卵巢切除术 (RRSO) 的高危女性的血清、血浆和尿液样本进行血清、血浆和尿液样本鉴定新型卵巢癌生物标志物。鉴定癌症生物标志物的常用方法是在任何治疗干预之前从临床上已鉴定为患有目标癌症的受试者获取样本，并与未患有该疾病的受试者的样本进行比较。然后，提出将病例与对照良好区分的假定标记作为进一步测试的候选标记，特别是将早期病例与对照分开的标记。高通量质谱与非线性统计分析相结合最近证明，光谱中的峰模式可以将所有情况与大多数对照区分开来。然而，使用术前样本会出现两个问题。首先，临床上发现的早期疾病很可能是大块的、有症状的疾病，而鉴定出的标记物可能只是癌发生过程后期大块疾病的指标。第二个问题是临床发现的早期疾病并不是早期检测计划的目标疾病。事实上，早期检测计划旨在识别早期疾病中无症状的受试者，而这些受试者在临床上可在晚期疾病中识别出来。计划接受 RRSO 的受试者形成了一个理想的队列，用于识别对低容量、无症状、早期疾病敏感的生物标志物。通常，由于已知的 BRCA 突变或卵巢癌和乳腺癌的家族史，接受 RRSO 的个体患卵巢癌的风险很高。 RRSO 后约 10% 的卵巢中发现了隐匿性卵巢癌。生物样本将从手术前后接受 RRSO 的一大群受试者中获得。全面的病理学审查将确定患有隐匿性卵巢癌的受试者（病例）和没有卵巢癌的受试者（对照）。将利用高通量质谱分析和非线性统计分类方法来识别峰模式，从而尽可能地将病例与非病例分开。另一种方法，2D DIGE（二维数字凝胶电泳）也将用于识别潜在的血清/血浆或尿液生物标志物。在识别出最有希望的峰/点模式、与峰/点相对应的蛋白质和肽之后 将通过 LC-MS/MS 进行鉴定。将为模式中六种最重要的蛋白质/肽开发单克隆抗体，从抗体开发免疫测定，最后针对生物样本的剩余等分试样进行测试，以通过进一步应用非线性分类方法来验证和增强模式识别。