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于减轻这种疾病的负担至关重要。虽然只有乳腺癌的八分之一，但卵巢癌的死亡人数不成比例地多，因为它的典型表现是晚期，几乎没有治愈的机会。上皮性卵巢癌现在被认为不是一种单一的疾病，而是一组不同的肿瘤，其亚型最好根据分子遗传特征进行分类。我们将应用该模型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局的相关性。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%。这为采用新方法来识别和评估HGSC的生物相关亚组提供了强烈的激励。识别具有真正病因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，这些研究基于mRNA表达模式确定了四种HGSC的强大亚型。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（详细信息包括生殖、生活方式和病史，以及生殖系遗传变异），我们提出：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）、类胶质瘤（EC）、透明细胞（CCC）或粘液性（MC）; B.根据mRNA表达模式将HGSC分为四个稳定且可重复的亚组，并描述每个亚型在我们基于人群的样本中的患病率; 2.检查是否与已知或推定的流行病学和遗传风险和保护因素的关联因蛋白质和mRNA表达亚型而异; 3.检查蛋白质和mRNA表达亚型的生存率是否不同我们方法的一个组成部分是检查上皮性卵巢癌的新的、分子定义的和生物学意义的亚型。我们将使用NanoString nCounter平台，一种高度灵敏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，我们将检查HGSC的四个亚组之间的流行病学差异，这是以前没有做过的。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。