Genetic Changes in Early Stage Ovarian Cancer

了解导致癌症形成的一连串基因变化有望通过创新和特定的方法在癌症的预防、早期发现和治疗方面带来一场革命。此外，肿瘤的分子图谱可能比传统的分期和分级具有更大的预后能力。卵巢癌早期分子遗传学改变的研究进展缓慢，可能是因为：早期癌症的研究相对较少，早期病变的新鲜组织很少，以及疾病的组织多样性可能会扰乱识别共同途径的努力。在这项研究中，我们将利用创新的高通量DNA技术 和来自两个来源的早期卵巢癌的RNA分析：272个来自妇科肿瘤组协议157的注释良好的福尔马林固定标本，以及从合作伙伴医院收集的100个样本，可获得快速冰冻组织。这些样本将使用NCI Edna阵列平台进行比较基因组杂交分析以检测DNA拷贝数异常，并在1OK SNP阵列平台上通过基于单核苷酸多态(SNP)阵列的杂合性丢失分析来检测等位基因丢失情况。候选基因将首先通过EDNA阵列平台进行表达谱筛选，然后通过荧光原位杂交(FISH)、定量PCR、免疫组织化学和酶联免疫吸附试验(ELISA)进一步验证。全 分析将是特定于组织学的，并使用适合于基于化验数据的生物信息学技术来识别与临床结果相关的基因变化。我们的假设是，识别特定于组织学的分子遗传变化将为预防、早期发现或治疗策略提供潜在的靶点，并且分子遗传图谱将提供重要的预测因子。

Understanding the cascade of genetic changes that leads to the formation of cancer promises a revolution in the prevention, early detection, and treatment of cancer by approaches that are innovative and specific. In addition, molecular profiling of tumors is likely to have far greater prognostic ability than traditional staging and grading. Advances in understanding early molecular genetic changes for ovarian cancer have been slow likely due to: a relative paucity of early-staged cancers for study of this disease, infrequent availability of fresh tissue for early-stage lesions, and histologic diversity of the disease which may confound efforts to identify common pathways. For this study, we will utilize innovative high throughput technologies for DNA and RNA analyses of early stage ovarian cancers obtained from two sources: 272 well-annotated formalin fixed specimens from the Gynecologic Oncology Group protocol 157 and 100 specimens collected from Partners Hospital with snap frozen tissue available. These specimens will be evaluated using the NCI eDNA array platform for comparative genomic hybridization analysis to detect DNA copy number abnormalities and by single nucleotide polymorphisms (SNP)-array-based loss of heterozygosity analysis on the 1OK SNP array platform to detect allelic loss profiles. Candidate genes will be first selected by performing expression profiling with the eDNA array platform and then further validated by fluorescent in situ hybridization (FISH), quantitative PCR, immunohistochemistry and enzyme linked immunosorbant assay (ELISA). All analyses will be histology-specific and use bioinformatics techniques suitable for assay based data to identify genetic changes correlated with clinical outcomes. Our hypothesis is that the identification of histologyspecific molecular genetic changes will suggest potential targets for prevention, early detection, or treatment strategies, and that the molecular genetic profiles will provide important predictors.