Novel Serum, Plasma, and Urine Biomarkers of Ovarian Can

本研究的目的是利用高通量质谱学和其他蛋白质组学技术对接受风险降低输卵管卵巢切除术(RRSO)的高危女性的血清、血浆和尿液样本进行鉴定，以确定新的卵巢癌生物标志物。识别癌症生物标记物的一种常用方法是在进行任何治疗干预之前，从已经被临床确认为患有目标癌症的受试者中获取样本，并将其与未患该病的受试者的样本进行比较。然后，提出了将病例与对照很好地分开的假定标记，作为进一步测试的候选，特别是将早期病例与对照分开的标记。高通量质谱学结合非线性统计分析最近证明，谱峰模式可以将所有病例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，临床发现的早期疾病很可能是大块的症状性疾病，所确定的标记物可能只是大块疾病在致癌过程的晚期的指示物。第二个问题是，临床确定的早期疾病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的无症状受试者，这些受试者本应在晚期疾病中被临床识别。RRSO计划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记物。通常，由于已知的BRCA突变或有卵巢癌和乳腺癌家族史，接受RRSO的人患卵巢癌的风险很高。RRSO后，在大约10%的卵巢中发现了隐匿性卵巢癌。生物标记物将从手术前和手术后接受RRSO的大量受试者中获得。一项全面的病理学回顾将确定患有隐匿性卵巢癌的受试者(病例)和未患卵巢癌的受试者(对照组)。将利用高通量质谱学和非线性统计分类方法来识别峰的模式，从而尽可能地将病例与非病例区分开来。另一种方法，2D DGE(二维数字凝胶电泳法)也将被应用于识别潜在的血清/血浆或尿液生物标志物。在确定最有希望的峰/点模式之后，与峰/点相对应的蛋白质和肽 将通过LC-MS/MS进行鉴定，将针对模式中最重要的六种蛋白质/多肽开发出单抗，由抗体发展而来的免疫分析，最后针对剩余的等量生物旋毛虫进行测试，以通过进一步应用非线性分类方法来验证和加强模式识别。

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.