Identifying Prognostic Markers and Therapeutic Targets for Serous Ovarian Cancer

描述(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，存活率非常低，因为肿瘤很快就会对当前的药物治疗产生抗药性。癌症基因组图谱(TCGA)项目已经对大约500例HGSOC患者进行了肿瘤概况分析。从表达分析中，TCGA最近确定了一组可用于预测生存的基因。尽管这组基因在其他研究中得到了验证，但许多单个基因在不同的研究中并没有显示出一致的结果。要确定适合用作药物靶点的有效预后标志物，需要在更大的一组肿瘤中发现，然后在非常大的一系列样本中进行验证。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，涉及大约8,000个卵巢肿瘤和广泛的临床数据。这些肿瘤中的大多数来自参加卵巢癌协会联合会(OCAC)的病例，因此拥有200,000个单核苷酸多态(SNPs)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中发现了与生存相关的SNPs，初步数据表明，这些胚系变化可能有助于识别新的基因，这些基因也可能是重要的躯体预后因素。我们将利用这些来自TCGA、OTA和OCAC的现有数据集来识别候选预后基因，并在OTA的样本中验证这些基因。这些生物标志物可能在临床环境中对治疗类型和复发决策有用，它们还将提供对疾病生物学的迫切需要的了解。该提案的具体目的是：目标1：从包括HGSOC亚组在内的表达数据的荟萃分析中以及从我们的生存GWA分析中识别候选预后基因。目的2：利用纳米串平台对数千例原发肿瘤进行400个基因的表达分析，以确定预后标志物并对肿瘤进行亚型划分。目的3：使用肿瘤的亚组重新分析生存Gwas数据，并识别与生存相关的新变化。目的4：检测新的卵巢癌模型中已识别的基因和通路，以确定它们是否可以作为新药治疗的靶点。

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.