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: 8504516
• 负责人: Jennifer A. Doherty
• 金额: $ 68.27万
• 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/8504516
• 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减轻这种疾病的负担至关重要。虽然卵巢癌的发病率仅为乳腺癌的八分之一，但由于卵巢癌通常在晚期出现，几乎没有治愈的机会，因此造成的死亡人数多得不成比例。上皮性卵巢癌现在被认为不是一种单一的疾病，而是一种具有不同亚型的肿瘤，可以根据分子遗传特征进行最好的分类。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与疾病结局的关联。高达75%的上皮性卵巢癌现在被认为是高级别浆液性（HGSC），占疾病死亡率的90%。这为采用新方法识别和评估HGSC的生物学相关亚群提供了强大的动力。鉴别具有真正病因差异的亚型对预防和改进靶向治疗具有重要意义。在这项拟议的研究中，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，这些研究已经确定了基于mRNA表达模式的四种强大的HGSC亚型。在两项基于人群的研究中，2240例浸润性卵巢癌病例和2900例对照（包括生殖、生活方式和病史以及种系遗传变异的详细信息），我们提出：1。a.使用基于蛋白（IHC）和mRNA （NanoString）的分类方案，将肿瘤分为HGSC、低级别浆液性（LGSC）、子宫内膜样（EC）、透明细胞（CCC）或黏液性（MC）；b.根据mRNA的表达模式，将HGSC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患病率；2. 检查与已知或假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. 研究存活是否因蛋白质和mRNA表达亚型而不同。我们的方法的一个整体优势是研究新的、分子定义的和生物学上有意义的上皮性卵巢癌亚型。我们将使用NanoString nCounter平台，这是一种高度敏感和准确的多重检测方法，直接测量福尔马林固定石蜡包埋肿瘤标本代表性切片的mRNA表达水平。值得注意的是，我们将研究HGSC四个亚组的流行病学差异，这是以前没有做过的。我们的发现可以对疾病预防和治疗更有效策略的发展产生重要影响。