Epidemiologic factors and survival by molecular subtypes of ovarian cancer

卵巢癌分子亚型的流行病学因素和生存率

• 批准号: 8631078
• 负责人: Jennifer A. Doherty
• 金额: $ 60.24万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8631078
• 关键词: Accounting; Address; Appearance; Biological; Biological Assay; Biology; Cells; Cessation of life; Classification; Classification Scheme; Clear Cell; Clinical; Cloning; Data; Development; Disease; Disease Outcome; Endometrioid Tumor; Epidemiologic Factors; Epidemiologic Studies; Epidemiology; Epithelial ovarian cancer; Etiology; Formalin; Gene Expression; Genes; Genetic; Genetic Risk; Genetic Variation; Grouping; Histologic; Histology; Immunophenotyping; Incentives; Knowledge; Lead; Life Style; Malignant Neoplasms; Malignant neoplasm of ovary; Measures; Medical History; Mesenchymal; Messenger RNA; Methods; Mind; Modeling; Molecular; Molecular Genetics; Mucinous; Nature; Ovarian Surface Epithelial-Stromal Tumor; Paraffin Embedding; Pathway interactions; Pattern; Prevalence; Prevention; Preventive; Proteins; Publishing; RNA; RNA amplification; Relative (related person); Reporting; Research; Risk; Risk Factors; Sampling; Scheme; Serous; Site; Specimen; Staging; Subgroup; System; The Cancer Genome Atlas; Tissues; Transcript; base; burden of illness; cancer diagnosis; cancer risk; cancer type; design; disorder prevention; disorder subtype; fimbria; follow-up; improved; innovation; mRNA Expression; malignant breast neoplasm; mortality; nano-string; novel; outcome forecast; population based; prevent; protein expression; public health relevance; reproductive; tumor

DESCRIPTION (provided by applicant): New research that improves prospects for prevention or treatment of ovarian cancer is essential to reduce the burden of this disease. Although only one-eighth as common as breast cancer, ovarian cancer accounts for a disproportionately large number of deaths, due to its typical presentation in an advanced stage with little chance for cure. Epithelial ovarian cancer is now considered not as a single disease, but rather as a diverse group of tumors with subtypes that can best be classified based on molecular genetic features. We will apply this model to assess the association of tumor subgroups with known or suspected ovarian cancer risk and preventive factors and with disease outcome. As much as 75% of epithelial ovarian cancer is now regarded as high-grade serous (HGSC), and accounts for 90% of disease mortality. This provides strong incentive to employ novel methods to identify and assess biologically relevant subgroups of HGSC. Identifying subtypes with true etiologic differences has important implications for prevention and for improved, targeted therapy. In the proposed study, we will follow up on intriguing findings of The Cancer Genome Atlas Research Network and related research that has identified four robust subtypes of HGSC based on patterns of mRNA expression. In two population-based studies of 2240 invasive ovarian cancer cases and 2900 controls (with detailed information on reproductive, lifestyle and medical histories, and on germline genetic variation), we propose to: 1. a. Classify tumors as HGSC, low-grade serous (LGSC), endometrioid (EC), clear cell (CCC) or mucinous (MC), using protein (IHC) and mRNA (NanoString) based classification schemes; b. Sub-classify HGSC into four robust and reproducible subgroups according to mRNA expression patterns, and describe the prevalence of each subtype in our population-based samples; 2. Examine whether associations with known or putative epidemiologic and genetic risk and protective factors differ by protein and mRNA expression subtype; 3. Examine whether survival differs by protein and mRNA expression subtype An integral strength of our approach is the examination of novel, molecularly-defined and biologically meaningful subtypes of epithelial ovarian cancer. We will use the NanoString nCounter platform, a highly sensitive and accurate multiplex assay, to directly measure mRNA expression levels from representative sections of formalin-fixed paraffin-embedded tumor specimens. Notably, we will examine epidemiologic differences across four subgroups of HGSC, which has not previously been done. Our findings can importantly influence the development of more effective strategies for disease prevention and treatment.

描述（由申请人提供）：改善卵巢癌预防或治疗前景的新研究对于减轻这种疾病的负担至关重要。尽管卵巢癌的发病率只有乳腺癌的八分之一，但由于其典型的晚期症状且几乎没有治愈的机会，因此导致的死亡人数不成比例。现在，上皮性卵巢癌不再被认为是一种单一疾病，而是一组不同的肿瘤，其亚型可以根据分子遗传特征进行最佳分类。我们将应用该模型来评估肿瘤亚组与已知或疑似卵巢癌风险和预防因素以及疾病结果的关联。目前，多达 75% 的上皮性卵巢癌被认为是高级别浆液性癌 (HGSC)，占疾病死亡率的 90%。这为采用新方法来识别和评估 HGSC 的生物学相关亚组提供了强有力的动力。识别具有真正病因学差异的亚型对于预防和改进靶向治疗具有重要意义。在拟议的研究中，我们将跟进癌症基因组图谱研究网络和相关研究的有趣发现，这些研究已根据 mRNA 表达模式确定了 HGSC 的四种强大亚型。在对 2240 例浸润性卵巢癌病例和 2900 例对照进行的两项基于人群的研究中（包含有关生殖、生活方式和病史以及种系遗传变异的详细信息），我们建议： 1. a.使用基于蛋白质 (IHC) 和 mRNA (NanoString) 的分类方案将肿瘤分类为 HGSC、低度浆液性 (LGSC)、子宫内膜样 (EC)、透明细胞 (CCC) 或粘液性 (MC)； b.根据 mRNA 表达模式将 HGSC 细分为四个稳健且可重复的亚组，并描述每个亚型在我们基于人群的样本中的流行率； 2. 检查与已知或推定的流行病学和遗传风险及保护因素的关联是否因蛋白质和 mRNA 表达亚型而异； 3. 检查生存率是否因蛋白质和 mRNA 表达亚型而异 我们方法的一个整体优势是检查上皮性卵巢癌的新型、分子定义和生物学意义的亚型。我们将使用 NanoString nCounter 平台（一种高度灵敏且准确的多重检测）直接测量福尔马林固定石蜡包埋肿瘤标本的代表性切片的 mRNA 表达水平。值得注意的是，我们将检查 HGSC 四个亚组之间的流行病学差异，这是以前从未做过的。我们的研究结果可以对制定更有效的疾病预防和治疗策略产生重要影响。