NOVEL APPROACHES TO GENE PROFILING IN OVARIAN CANCER

描述（由申请人提供）：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，手术后化疗治疗后的5年生存率为10- 25%。局限于骨盆的早期疾病与大于90%的5年生存率相关，尽管存在高风险特征仍然需要术后化疗治疗。传统的临床和分子标志物，如分期，术后减积状态，p53突变和BAX表达是合理的，但不完善的措施的结果。这一观察结果表明，没有单一的标记物可以作为负责肿瘤生长和对化疗反应的复杂遗传变化的替代品。在这方面，微阵列基因分析是一种强大的技术，能够同时评估数千个基因的表达，尽管其对EOC患者的临床效用仍有待确定。使用这种技术，我们已经开发了新的生物信息学方法，以确定基因谱的训练集，在EOC的临床结果是高度预后。在这项资助中，我们将在一个由来自一个单独机构的大量肿瘤样本组成的测试集中验证这些数据，我们还将确定是否有可能使用RT-PCR和免疫组化检测来简化肿瘤分析（具体目标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。因此，在特异性目标2中，我们将根据化疗开始前以及化疗开始后几天腹水中肿瘤细胞的可及性，获得体内对化疗反应的基因表达的动态评估。最后，在特定目标3中，我们将应用微阵列技术对早期疾病患者进行研究，以确定是否有可能仅识别那些从术后化疗中获益最大的患者。我们预计，准确识别EOC的预测和预后因素的能力将允许一个更有针对性的方法来治疗这种疾病的患者的术后管理。

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.