Screening and Risk Biomarkers for Ovarian Cancer in EPIC Specimens

描述（由申请人提供）：卵巢癌的发病率和死亡率可以通过更好的一级和二级预防方法来提高。反过来，初级预防需要更好的风险生物标志物，特别是那些可能转化为干预策略的生物标志物；二级预防需要高度敏感和特异性的早期检测生物标志物。从卵巢癌诊断前数月或数年获得的标本中，我们积累了关于早期检测和风险生物标志物的令人兴奋的数据。我们评估了来自前列腺、肺、结直肠和卵巢（PLCO）癌症筛查试验标本中的28种生物标志物，发现在诊断后6个月内，没有比CA125更好的筛查生物标志物，强调了了解CA125“阴性”病例的必要性。然而，添加HE4、CA72.4和β -2微球蛋白（B2M）以及流行病学变量，如排卵周期、子宫内膜异位症、体重指数和乳腺癌家族史，在识别距离诊断超过一年的病例时，比单独使用CA125有改善。在对护士健康研究（NHS）中诊断后至少3年的标本的研究中，我们通过增加或减少对重要上皮细胞和癌症标志物MUC1（与CA125属于同一家族）的免疫的事件，测试了卵巢癌发病机制的新范式。在64岁以下的女性中，抗muc1抗体水平与升高或降低风险的流行病学事件有关，抗体水平较高与卵巢癌风险较低相关。在其他工作中，我们开发了一种检测抗CA125抗体的方法，在诊断时CA125正常的卵巢癌病例中发现了更高的水平，并假设免疫复合物可能屏蔽了CA125。我们现在希望在欧洲营养与癌症前瞻性调查（EPIC）的临床前样本中验证这些发现，估计有816例病例和2024例匹配对照。使用所有病例和对照，我们将首先确定关键的流行病学风险因素并开发风险预测模型。在同一组标本中，我们将测量MUC1 （CA15.3）和MUC16 （CA125）游离抗原、抗MUC1和抗MUC16抗体以及涉及粘蛋白抗原和抗体的免疫复合物，并确定它们与流行病学因素、进入年龄和卵巢癌风险的关系。在三年内抽血诊断的196例EPIC病例和784例匹配对照中，我们将测量额外的早期检测标志物HE4， CA72.4和β -2微球蛋白。我们将评估和完善一种早期检测算法，其特定目标是确定加入流行病学风险因素是否能改善早期检测模型，以及黏液相关风险生物标志物是否有助于识别CA125或CA15.3阴性病例。本研究的目的是了解黏液免疫与卵巢癌发病机制的关系，以及基于黏液免疫的标志物是否可以提高目前最好的早期检测生物标志物的性能。

DESCRIPTION (provided by applicant): Ovarian cancer morbidity and mortality could be improved by better methods of primary and secondary prevention. In turn, primary prevention requires better biomarkers of risk, especially those which might translate into strategies for intervention; and secondary prevention requires highly sensitive and specific early detection biomarkers. From specimens obtained months or years prior to ovarian cancer diagnosis, we have accumulated exciting data on early detection and risk biomarkers. We evaluated 28 biomarkers in specimens from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial and showed there is no better screening biomarker than CA125 for detecting preclinical cases within 6 months of diagnosis, underscoring the need to understand CA125 "negative" cases. However, adding HE4, CA72.4, and beta-2-microglobulin (B2M) plus epidemiologic variables like ovulatory cycles, endometriosis, body mass index, and family history of breast cancer improved upon CA125 alone in identifying cases more than a year remote from diagnosis. In work with specimens from the Nurses' Health Study (NHS) taken at least 3 years from diagnosis, we tested a new paradigm for ovarian cancer pathogenesis through events that either increase or decrease immunity to the important epithelial cell and cancer marker, MUC1 (in the same family as CA125). The level of anti-MUC1 antibodies tracked with epidemiologic events raising or lowering risk and a higher level of antibodies correlated with lower ovarian cancer risk in women less than age 64. In other work, we developed an assay to detect anti-CA125 antibodies, found higher levels in ovarian cancer cases with normal CA125 at diagnosis, and hypothesized that immune complexes may shield CA125 from its conventional assay. We now wish to validate these findings in pre-clinical specimens from the European Prospective Investigation into Nutrition and Cancer (EPIC) with an estimated 816 cases and 2024 matched controls. Using all cases and controls, we will first identify key epidemiologic risk factors and develop a risk prediction model. In the same set of specimens, we will measure MUC1 (CA15.3) and MUC16 (CA125) free antigens, anti-MUC1 and anti-MUC16 antibodies, and immune complexes involving mucin antigens and antibodies and determine how they relate to epidemiologic factors, age at entry, and risk for ovarian cancer by remoteness of the blood from diagnosis. In the 196 EPIC cases diagnosed within three years of blood draw and 784 matched controls, we will then measure the additional early detection markers HE4, CA72.4, and beta-2-microglobulin. We will evaluate and refine an early detection algorithm with the particular goal of determining whether the addition of epidemiologic risk factors improve an early detection model and whether mucin-related risk biomarkers can help identify the CA125 or CA15.3 negative case. The goal of this study is to understand how mucin-immunity relates to ovarian cancer pathogenesis and whether markers based upon it can improve performance of the current best early detection biomarkers.