Molecular Profiling to Predict Response to Chemotherapy

描述(申请人提供)：目前，对晚期上皮性卵巢癌患者的标准护理包括初次手术细胞减少术，然后是初次铂/紫杉烷化疗。虽然大多数患者最初经历了完全的临床反应，但少数患者在接受治疗后仍会出现反应迟缓或进展的疾病，而且大多数患者在接受初级治疗后会复发。这种“铂耐药”病患者接受抢救化疗，预后较差。这项建议中所描述的工作的目标是确定预测晚期卵巢癌化疗反应的基因表达模式。我们还打算进一步确定导致化疗敏感性的基因的作用。这项提案的R21回顾阶段旨在开发预测对初治和挽救化疗的反应的基因表达谱。这将通过对从H.Lee Moffitt癌症中心和杜克大学医学中心的肿瘤库中获得的卵巢癌进行回顾性微阵列分析来完成。将开发计算工具来定义预测治疗反应的基因图谱。基因表达签名将在R33赞助的前瞻性临床试验中得到验证和提炼。预期收集的卵巢样本将被排列，并观察对初级和挽救治疗的临床反应。此外，我们的目标是通过对抢救治疗开始前获得的复发性卵巢癌活检(或腹水)样本进行微阵列表达分析，探索提高我们预测抢救治疗反应的能力的机会。为了扩大我们的阵列发现，预测模型中涉及的基因和基因路径将在更多的卵巢癌中接受先进的生物信息学工具和定量聚合酶链式反应的额外分析。预测卵巢癌化疗反应的能力将使根据癌症表达谱为个体患者建立量身定制的治疗方案成为可能。因此，可以提高应答率，避免有毒物质，节省骨髓，提高生活质量。最终，明确治疗反应的生物学基础将有助于开发更活跃的药物，可能会提高卵巢癌的治愈率。

DESCRIPTION (provided by applicant): Currently, standard care for patients with advanced stage epithelial ovarian cancer includes primary surgical cytoreduction followed by primary platinum/taxane chemotherapy. Although the majority of patients initially experience a complete clinical response, a minority will have unresponsive or progressive disease despite therapy, and most will experience a recurrence following primary treatment. Patients with such "platinum resistant" disease are treated with salvage chemotherapy and have a poor prognosis. The goal of the work described in this proposal is to identify patterns of gene expression that predict response to chemotherapy for advanced stage ovarian cancers. We also aim to further characterize the role of the genes that contribute to chemosensitivity. The R21 retrospective phase of this proposal aims to develop gene expression profiles that predict response to primary and salvage chemotherapy. This will be accomplished by a retrospective microarray analysis of ovarian cancers obtained from the tumor banks of the H. Lee Moffitt Cancer Center and Duke University Medical Center. Computational tools will be developed to define gene profiles that predict response to therapy. The gene expression signatures will be validated and refined in the R33 sponsored prospective clinical trial. Prospectively collected ovarian samples will be arrayed and the clinical response to primary and salvage therapy observed. Additionally, we aim to explore opportunities to improve our ability to predict response to salvage therapy by performing microarray expression analysis of recurrent ovarian cancer biopsy (or ascites) samples obtained prior to the initiation of salvage therapies. To extend our array findings, genes and gene pathways involved in the predictive model will be subject to additional analysis by advanced bioinformatics tools and quantitative PCR in a larger number of ovarian cancers. The ability to predict response to chemotherapy for ovarian cancer will enable tailored therapeutic regimens to be established for individual patients on the basis of cancer expression profiles. As such, response rates can be improved, toxic agents avoided, bone marrow spared, and quality of life enhanced. Ultimately, defining the biologic underpinnings of response to therapy will facilitate the development of more active agents that may improve cure rates for ovarian cancer.