Identifying Prognostic Markers and Therapeutic Targets for Serous Ovarian Cancer

描述（由申请人提供）：高级别浆液性卵巢癌（HGSOC）是最常见的卵巢癌类型，由于肿瘤很快对目前的药物治疗产生耐药性，因此生存率非常低。癌症基因组图谱（TCGA）项目已经对约500例HGSOC病例进行了肿瘤分析。从表达分析中，TCGA最近确定了一组可用于预测生存的基因。尽管基因组在其他研究中得到验证，但许多单个基因在研究中并未显示出一致的结果。为了鉴定适合用作药物靶标的经验证的预后标志物，需要在更大的肿瘤组中发现，然后在非常大的一系列样品中验证。我们已经建立了卵巢肿瘤组织分析（OTTA）联盟，这是一个由30项研究组成的国际合作组织，涉及约8，000例卵巢肿瘤和广泛的临床数据。这些肿瘤中的大多数来自参与卵巢癌协会联盟（OCAC）的病例，因此具有200，000个单核苷酸多态性（SNP）的基因分型数据和流行病学数据。我们已经从我们的全基因组关联研究（GWAS）中鉴定出与生存相关的SNP，初步数据表明这些种系变化可能有助于鉴定新的基因，这些基因也可能是重要的体细胞预后因素。我们将利用这些现有的数据集从TCGA，OTTA和OCAC，以确定候选的预后基因，并验证这些基因的样本从OTTA。这些生物标志物可能在临床环境中用于治疗类型和复发决策，它们还将提供对疾病生物学的迫切需要的理解。该提案的具体目标是：目标1：从包括HGSOC亚组的表达数据的荟萃分析和我们的生存GWAS的分析中识别候选预后基因。目标二：使用Nanostring平台对数千种原发性肿瘤进行400种基因的表达分析，以识别预后标志物并对肿瘤进行亚型分析。目的3：使用肿瘤亚组重新分析生存GWAS数据，并确定与生存相关的新变化。目的4：在新的卵巢癌模型中检测已鉴定的基因和通路，以确定它们是否可用作新药治疗的靶点。

DESCRIPTION (provided by applicant): High-grade serous ovarian cancer (HGSOC) is the most common type of ovarian cancer and has very poor survival, as the tumors quickly become resistant to the current drug treatments. The Cancer Genome Atlas (TCGA) project has performed tumor profiling of ~500 HGSOC cases. From the expression analysis TCGA has recently identified a panel of genes that can be used to predict survival. Although the panel of genes validate in other studies many of the individual genes do not show consistent results across studies. To identify validated prognostic markers suitable to use as drug targets will require discovery in a larger set of tumors followed by validation in a very large series of samples. We have established the Ovarian Tumor Tissue Analysis (OTTA) consortium, an international collaboration of 30 studies with approximately 8,000 ovarian tumors and extensive clinical data. The majority of these tumors is from cases participating in the Ovarian Cancer Association Consortium (OCAC) and therefore have genotyping data on 200,000 single nucleotide polymorphisms (SNPs) and epidemiological data. We have identified SNPs that are associated with survival from our genome wide association study (GWAS) and preliminary data suggests that these germline changes may help to identify novel genes that may also be important somatic prognostic factors. We will utilize these existing datasets from TCGA, OTTA and OCAC to identify candidate prognostic genes and validate these genes in samples from OTTA. These biomarkers could be useful in the clinical setting for type of treatment and decision-making in recurrence and they will also provide a much-needed understanding of the biology of the disease. The specific aims of the proposal are: Aim 1: To identify candidate prognostic genes from a meta-analysis of expression data including subgroups of HGSOC and from an analysis of our survival GWAS. Aim 2: To perform expression analysis of 400 genes using the Nanostring platform on thousands of primary tumors to identify prognostic markers and to subtype the tumors. Aim 3: To reanalyze the survival GWAS data using the subgroups of the tumors and identify novel changes associated with survival. Aim 4: To test the identified genes and pathways in novel ovarian cancer models to determine if they could be used as targets for new drug treatments.