Screening and Risk Biomarkers for Ovarian Cancer in EPIC Specimens

EPIC 样本中卵巢癌的筛查和风险生物标志物

• 批准号: 8295543
• 负责人: DANIEL William CRAMER
• 金额: $ 56.69万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8295543
• 关键词: Age; Aging; Algorithms; Antibodies; Antigen-Antibody Complex; Antigens; Binding; Biological Assay; Biological Markers; Blood; Blood specimen; Body mass index; CA-125 Antigen; Cancer and Nutrition; Data; Data Set; Detection; Diagnosis; Disease; Early Diagnosis; Enrollment; Epidemiologic Factors; Epidemiology; Epithelial Cells; European; Evaluation; Event; Family; Family history of; General Population; Goals; Immune; Immunity; Immunoglobulin G; Individual; Intervention; Investigation; Malignant Neoplasms; Malignant neoplasm of ovary; Mammalian Oviducts; Measurement; Measures; Methods; Modeling; Morbidity - disease rate; Mucins; Nurses' Health Study; Ovarian; Pathogenesis; Performance; Primary Prevention; Prospective Studies; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; Resources; Risk; Risk Factors; Screening procedure; Secondary Prevention; Specimen; Stratification; Testing; Time; Translating; WFDC2 gene; Woman; Work; base; beta-2 Microglobulin; cancer diagnosis; cancer risk; case control; cohort; endometriosis; epidemiologic data; improved; innovation; interest; malignant breast neoplasm; mortality; novel; peritoneal cancer; pre-clinical; prospective; tumor

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.

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改善。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）癌症筛查试验的标本中的28种生物标志物，并显示在诊断后6个月内检测临床前病例时，没有比CA 125更好的筛查生物标志物，强调需要了解CA 125“阴性”病例。然而，添加HE 4、CA72.4和β-2-微球蛋白（B2 M）加上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，在识别距诊断超过一年的病例时，优于单独使用CA 125。在从护士健康研究（NHS）诊断至少3年的标本工作中，我们通过增加或减少对重要上皮细胞和癌症标志物MUC 1（与CA 125在同一家族中）的免疫力的事件来测试卵巢癌发病机制的新范式。抗MUC 1抗体水平与流行病学事件的风险升高或降低有关，在64岁以下的女性中，较高水平的抗体与较低的卵巢癌风险相关。在其他工作中，我们开发了一种检测抗CA 125抗体的方法，在诊断时CA 125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以保护CA 125免受其常规检测。我们现在希望在来自欧洲营养与癌症前瞻性调查（EPIC）的临床前标本中验证这些发现，估计有816例病例和2024例匹配对照。利用所有病例和对照，我们将首先确定关键的流行病学危险因素，并建立风险预测模型。在同一组标本中，我们将测量MUC 1（CA15.3）和MUC 16（CA 125）游离抗原，抗MUC 1和抗MUC 16抗体，以及涉及粘蛋白抗原和抗体的免疫复合物，并确定它们如何与流行病学因素，入组时年龄和诊断时血液的偏远性卵巢癌风险相关。在抽血后三年内诊断的196例EPIC病例和784例匹配的对照中，我们将测量额外的早期检测标志物HE 4、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，其特定目标是确定增加流行病学风险因素是否能改善早期检测模型，以及粘蛋白相关风险生物标志物是否有助于识别CA 125或CA15.3阴性病例。本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提高目前最好的早期检测生物标志物的性能。 公共卫生关系：该提案采用了目前关于早期检测生物标志物的最佳数据和关于卵巢癌风险生物标志物的创新想法，并在来自大型独特EPIC队列的标本中对其进行评估，目的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志物，包括结合在免疫复合物中的CA 125，并从其常规检测中屏蔽。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，并且比现有的病因模型更好地解释了风险。