Molecular Profiling to Predict Response to Chemotherapy

描述（由申请人提供）：目前，晚期上皮性卵巢癌患者的标准治疗包括原发性手术细胞减灭术，随后进行原发性铂/紫杉烷化疗。虽然大多数患者最初经历完全临床应答，但少数患者尽管接受治疗仍无应答或疾病进展，大多数患者在初次治疗后复发。患有这种“铂耐药”疾病的患者接受补救化疗治疗，预后不良。这项研究的目的是确定预测晚期卵巢癌化疗反应的基因表达模式。我们的目标也是进一步描述基因的作用，有助于化疗敏感性。该提案的R21回顾性阶段旨在开发预测对原发性和挽救性化疗反应的基因表达谱。这将通过对从H.李莫菲特癌症中心和杜克大学医学中心。将开发计算工具来确定预测治疗反应的基因谱。将在R33申办的前瞻性临床试验中验证和完善基因表达特征。将对收集的卵巢样本进行排列，并观察对初次治疗和挽救治疗的临床反应。此外，我们的目标是探索机会，以提高我们的能力，通过执行微阵列表达分析复发性卵巢癌活检（或腹水）样本开始挽救治疗前获得的挽救治疗的反应。为了扩展我们的阵列研究结果，预测模型中涉及的基因和基因通路将在大量卵巢癌中通过先进的生物信息学工具和定量PCR进行额外的分析。预测卵巢癌化疗反应的能力将使个体患者能够根据癌症表达谱建立定制的治疗方案。因此，可以提高反应率，避免有毒物质，保留骨髓，提高生活质量。最终，确定对治疗反应的生物学基础将有助于开发更有活性的药物，从而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.