Genomic Stratification of Ovarian Cancer Patients

描述（由申请人提供）：应用基因组学和其他高通量技术是本申请涉及的主题领域。卵巢癌是最致命的妇科恶性肿瘤。它有一个高的反应率，以初步联合铂紫杉烷化疗减瘤手术后。然而，这些女性中的绝大多数将在诊断后12至24个月内复发，并将死于渐进性化疗耐药肿瘤。在过去的十年中，没有改善总体生存率。在过去的几年中，多种预后和预测标志物被确定，但没有一个具有令人满意的预测价值。利用基因组技术，我们和其他人，包括癌症基因组图谱项目，最近产生了转录组签名，旨在根据生存率对患者进行分层或预测对化疗的反应。然而，没有一个签名在转录水平上得到验证，并且每项研究仅使用来自单个机构的样本。这些特征的临床意义尚不清楚。在本申请中，我们建议对已发表的卵巢癌特征进行全面调查，包括使用现有公开数据进行交叉研究验证、调整可用特征以及使用DASL技术对多中心临床试验GOG 218标本进行特征验证。这种方法将为我们提供强大的预后和预测特征，这将使医生能够识别对标准治疗无反应的患者，从而避免不必要的毒性，并有机会接受实验性治疗。此外，抗性的特征肯定会包含对抗性表型具有重要功能的基因。这些基因将成为逆转耐药表型的重要治疗靶点。我们希望这一建议能从根本上改变卵巢癌患者的临床管理，他们应该从这个项目的结果中立即受益。 公共卫生相关性：这项拟议的研究旨在对已发表的卵巢癌特征进行全面调查，包括使用现有公开数据进行交叉研究验证，调整可用特征，并使用多中心临床试验GOG 218的标本进行特征验证。这种方法将为我们提供强大的预后和预测特征，这将彻底改变卵巢癌的治疗策略。

DESCRIPTION (provided by applicant): Applying Genomics and Other High Throughput Technologies is the thematic area that this application addresses. Ovarian cancer is the most lethal gynecologic malignancy. It has a high response rate to initial combined platinum taxane chemotherapy following debulking surgery. However, the vast majority of these women will have their cancer recur within 12 to 24 months after diagnosis and will die of progressively chemotherapy-resistant tumor. No improvement has been made to improve overall survival over the past decade. Multiple prognostic and predictive markers were identified in the past few years but none of them have satisfactory predictive values. Using genomic technologies, we and others including the Cancer Genome Atlas Project have recently generated transcriptome signatures which purport to stratify patients according to survival or predict response to chemotherapy. However, none of signatures have been validated on the transcript levels and only samples from a single institution were used for each study. The clinical significance of these profiles remains unknown. In this application, we propose to perform a comprehensive survey of published ovarian cancer signatures, including a cross-study validation using existing publicly available data, tuning of available signatures and validation of signatures using DASL technology on multi-center clinical trial GOG218 specimens. This approach will provide us with robust prognostic and predictive signatures, which will enable physicians to identify patients who will not respond to standard therapy, allow for the avoidance of unnecessary toxicity and the opportunity to be treated with experimental therapy. In addition, the signatures of resistance will certainly contain genes, which are functionally important to the resistant phenotype. These genes will be important therapeutic targets for the reversal of the resistant phenotype. We expect this proposal to radically change the clinical management of ovarian cancer patients, who should benefit greatly and immediately from the results of this project. PUBLIC HEALTH RELEVANCE: The proposed study seek to perform a comprehensive survey of published ovarian cancer signatures, including a cross-study validation using existing publicly available data, tuning of available signatures and validation of signatures using specimens from a multi-center clinical trial GOG218. This approach will provide us with robust prognostic and predictive signatures, which will revolutionize ovarian cancer treatment strategies.