NOVEL APPROACHES TO GENE PROFILING IN OVARIAN CANCER

描述(申请人提供)：上皮性卵巢癌(EOC)是最致命的妇科恶性肿瘤。晚期疾病通常累及上腹部，影响70%的患者，在手术和化疗后的5年存活率在10%-25%之间。早期局限于骨盆的疾病与5年存活率超过90%有关，尽管高危特征的存在仍然需要术后化疗。传统的临床和分子标志物如分期、术后恢复状态、P53突变和Bax表达是合理但不完美的预后指标。这一观察表明，没有一个单一的标记可以替代复杂的基因变化，这些变化导致肿瘤生长和对化疗的反应。在这方面，微阵列基因图谱是一项强大的技术，能够同时评估数千个基因的表达，尽管其对卵巢癌患者的临床应用仍有待确定。利用这项技术，我们开发了新的生物信息学方法来识别训练集中的基因图谱，这些基因图谱对EOC的临床结果具有很高的预测性。在这笔赠款中，我们将在由来自独立机构的大量肿瘤样本组成的测试集中验证这些数据，并将确定是否有可能使用RT-PCR和免疫组织化学分析来简化肿瘤分析(特定目标1)。此外，我们挑战了普遍接受的概念，即准确的预后信息总是可以从静态的、治疗前的肿瘤样本的分析中获得。因此，在特定目标2中，我们将根据化疗开始前以及化疗后几天腹水中肿瘤细胞的可获得性，对体内化疗反应的基因表达进行动态评估。最后，在具体目标3中，我们将把微阵列技术应用于对早期疾病患者的研究，试图确定是否有可能只确定那些将从术后化疗中获得最大好处的患者。我们预计，能够准确识别EOC中的预测和预后因素将允许对这种疾病患者的术后处理采取更有针对性的方法。

DESCRIPTION (provided by applicant): Epithelial ovarian cancer (EOC) is the most lethal of gynecologic malignancies. Advanced disease typically involves the upper abdomen and affects 70% of patients, associated with 5 year survival in the range of 10- 25% after treatment with surgery followed by chemotherapy. Early stage disease confined to the pelvis is associated with 5 year survival of greater than 90%, although the presence of high risk features still requires treatment with post-operative chemotherapy. Traditional clinical and molecular markers as stage, postoperative debulking status, p53 mutation, and BAX expression are reasonable but imperfect measures of outcome. This observation suggests that no single marker can serve as a surrogate for the complex genetic changes that are responsible for tumor growth and response to chemotherapy. In this regard, microarray gene profiling is a powerful technique that is capable of simultaneously assessing the expression of thousands of genes, although its clinical utility for patients with EOC remains to be determined. Using this technique, we have developed novel bioinformatics approaches to identify gene profiles in a training set that are highly prognostic of clinical outcome in EOC. In this grant, we will validate these data in a test set comprised of a large number of tumor samples from a separate institution, and we will also determine whether it is possible to streamline tumor profiling using RT-PCR and immunohistochemistry assays (Specific Aim 1). Furthermore, we challenge the generally accepted concept that accurate prognostic information can always be obtained from analysis of a static, pre-treatment tumor sample. Thus, in Specific Aim 2 we will obtain a dynamic assessment of gene expression in response to chemotherapy in vivo, based upon the accessibility of tumor cells from ascites immediately before as well as for several days after chemotherapy has begun. Finally, in Specific Aim 3 we will apply the micro-array technique to a study of patients with early stage disease, in an attempt to determine whether it is possible to identify only those patients who will derive the greatest benefit from post-operative chemotherapy. We anticipate that the ability to accurately identify predictive and prognostic factors in EOC will permit a more tailored approach to post-operative management for patients with this disease.