Screening and Risk Biomarkers for Ovarian Cancer in EPIC Specimens

描述(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改善。反过来，一级预防需要更好的风险生物标记物，特别是那些可能转化为干预战略的生物标记物；二级预防需要高度敏感和具体的早期检测生物标记物。从卵巢癌诊断前几个月或几年获得的标本中，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。我们评估了前列腺癌、肺癌、结直肠癌和卵巢癌(PLCO)筛查试验样本中的28个生物标记物，发现没有比CA125更好的筛查生物标记物来检测诊断后6个月内的临床前病例，强调了了解CA125“阴性”病例的必要性。然而，加入HE4、CA72.4和β-2-微球蛋白(B2M)加上排卵周期、子宫内膜异位症、体重指数和乳腺癌家族史等流行病学变量，在识别距离诊断一年以上的病例方面，仅CA125一项指标有所改善。在护士健康研究(NHS)的样本中，从诊断开始至少3年，我们测试了一种新的卵巢癌发病机制范例，通过增加或降低对重要的上皮细胞和癌症标记物MUC1的免疫力(与CA125属于同一家族)。在年龄小于的女性中，抗MUC1抗体水平与增加或降低风险的流行病学事件以及较高水平的抗体与较低的卵巢癌风险相关。在另一项工作中，我们开发了一种检测抗CA125抗体的方法，发现在确诊时CA125正常的卵巢癌患者中，CA125的水平更高，并假设免疫复合物可能会保护CA125与其常规检测方法不同。现在，我们希望在欧洲营养与癌症前瞻性研究(EPIC)的临床前样本中验证这些发现，估计有816例病例和2024名匹配的对照。利用所有病例和对照，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。在同一组样本中，我们将检测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体，并通过远离诊断的血液来确定它们与流行病学因素、入院年龄和卵巢癌风险的关系。在采血三年内确诊的196例EPIC病例和784名匹配的对照组中，我们将检测额外的早期检测标记物HE4、CA72.4和β-2-微球蛋白。我们将评估和改进早期检测算法，以确定添加流行病学风险因素是否改善了早期检测模型，以及粘蛋白相关风险生物标记物是否可以帮助识别CA125或CA15.3阴性病例。这项研究的目的是了解粘蛋白免疫与卵巢癌发病机制的关系，以及基于粘蛋白免疫的标志物是否可以改善目前最好的早期检测生物标志物的性能。 公共卫生相关性：这项建议采用了目前关于卵巢癌早期发现生物标记物的最佳数据和关于卵巢癌风险生物标记物的创新想法，并在大型和独特的EPIC队列样本中对它们进行评估，目标是推进风险分层和一般人群筛查。我们的早期检测模型包括流行病学风险因素和粘蛋白相关标记物，其中一个标记物是CA125，与免疫复合体结合，不受常规检测的影响。我们的风险算法将基于卵巢癌的免疫模型，该模型是新颖的，比现有的病因模型更好地解释了风险。

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. PUBLIC HEALTH RELEVANCE: This proposal takes the current best data regarding early detection biomarkers and innovative ideas on risk biomarkers for ovarian cancer and evaluates them in specimens from the large and unique EPIC cohort with the goal of advancing risk stratification and general population screening. Our early detection model includes epidemiologic risk factors and mucin-related markers including one for CA125 bound in an immune complex and shielded from its conventional assay. Our risk algorithm will be based upon an immune model for ovarian cancer that is novel and explains risk better than existing etiologic models.