Models to Predict Prognosis and Benefit from Adjuvant Therapy in Renal Cell Carci

预测肾细胞癌预后和辅助治疗获益的模型

• 批准号: 8613470
• 负责人: Harriet M. Kluger
• 金额: $ 34.43万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-04 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8613470
• 关键词: Adjuvant; Adjuvant Therapy; Antineoplastic Agents; BAY 54-9085; Biological Assay; Biological Markers; Biology; Biopsy Specimen; Cells; Clinical; Coupled; Data; Detection; Development; Diagnosis; Disease; Disease Management; Double-Blind Method; Drug Costs; Drug Targeting; Elements; Endothelial Cells; Endothelium; Enrollment; Future; Goals; Histologic; Immunohistochemistry; In Situ; Incidence; MAP Kinase Gene; Malignant Neoplasms; Masks; Measures; Mediating; Mediator of activation protein; Metastatic Renal Cell Cancer; Methods; Modeling; Multi-Institutional Clinical Trial; Natural History; Neoplasm Metastasis; Nephrectomy; Normal tissue morphology; Output; Paraffin Embedding; Pathway interactions; Patient Selection; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phase III Clinical Trials; Placebo Control; Placebos; Platelet-Derived Growth Factor; Primary Neoplasm; Principal Investigator; Proteins; Randomized; Relapse; Renal Cell Carcinoma; Renal carcinoma; Resistance; Rest; Sampling; Signal Transduction; Specimen; Staging; Staining method; Stains; System; Systemic Therapy; Technology; Testing; Therapeutic; Tissues; Toxic effect; Training; Tyrosine Kinase Inhibitor; VHL mutation; Validation; Vascular Endothelial Growth Factor Receptor; Vascular Endothelial Growth Factor Receptor-3; Vascular Endothelial Growth Factors; Work; angiogenesis; arm; base; cancer cell; cohort; cost; density; disease natural history; fluorophore; high risk; improved; kidney cell; member; molecular marker; neoplastic cell; outcome forecast; predictive modeling; prognostic; public health relevance; receptor; receptor expression; response; success; tumor

DESCRIPTION (provided by applicant): Successful development of targeted anti-cancer drugs is often coupled with predictive assays that enable selective treatment of patients more likely to benefit from therapy. Immunohistochemistry is often used to assess expression of drug targets, but it suffers from subjectivity and lack of quantitative measures. We developed a method for automated, quantitative analysis (AQUA) for assessing protein levels in situ. In response to PA-08-134, we propose to expand AQUA to simultaneously assess tumors and endothelial cells, and develop models to predict clinical benefit from adjuvant sorafenib and sunitinib for renal cell carcinoma (RCC), as well as models to predict prognosis in untreated patients. RCC has traditionally been a disease that is highly resistant to systemic therapy. However, multiple targeted therapies, including sorafenib and sunitinib, have recently revolutionized the approach to metastatic RCC. Both drugs are effective for subsets of RCC patients, and both are associated with some toxicity. Given their success in metastatic RCC, these drugs are being studied as adjuvant therapies in a large, randomized, double blinded, multi-center trial called E2805. Specimens are being collected on all patients, and they offer a unique opportunity to develop models to predict clinical benefit from these drugs and models to predict prognosis in untreated patients in a multi-center clinical trial setting on a very large cohort. Sorafenib and sunitinb have multiple targets, and our purpose is to identify the most important predictive marker/s. We will study angiogenic markers, members of the MAPK pathway and other known targets. In preliminary studies using AQUA, we showed that RCCs with high levels of vascular endothelial growth factor (VEGF) receptors in tumor cells tend to have lower microvessel density and poor survival. We hypothesize that patients with high VEGF receptor expression in TUMOR cells are more likely to benefit from therapy than those with high microvessel density. We will expand AQUA to enable concurrent assessment of targets in tumor and vessels, by masking the tumor and vessels with different fluorophores. We will establish staining conditions for all known targets of sorafenib and sunitinib and select mediators of angiogenesis in tumor, endothelium and adjacent normal tissue using historical cohorts of untreated RCC patients. We will then assess VHL mutations and expression of sorafenib and sunitinib targets in a training set (67%) of E2805 patients and generate predictive models for each of the drugs, to be validated in a testing set. Standard clinical co-variates and VHL mutational status will be incorporated into the model. We will also use these molecular markers and clinical co-variates to improve current prognostic models in the placebo-treated patients. These models can be used to select patients for the optimal adjuvant therapy for RCC (sorafenib, sunitinib or neither), and this approach can be studied in other clinical settings as well.

描述（由申请人提供）：靶向抗癌药物的成功开发通常与预测分析相结合，从而能够对更有可能从治疗中受益的患者进行选择性治疗。免疫组织化学通常用于评估药物靶标的表达，但它存在主观性且缺乏定量测量。我们开发了一种自动定量分析 (AQUA) 方法，用于原位评估蛋白质水平。针对 PA-08-134，我们建议扩展 AQUA 以同时评估肿瘤和内皮细胞，并开​​发模型来预测索拉非尼和舒尼替尼辅助治疗肾细胞癌 (RCC) 的临床获益，以及预测肾细胞癌 (RCC) 预后的模型未经治疗的患者。 肾细胞癌传统上是一种对全身治疗具有高度抵抗力的疾病。然而，包括索拉非尼和舒尼替尼在内的多种靶向疗法最近彻底改变了转移性肾细胞癌的治疗方法。这两种药物对部分肾细胞癌患者均有效，并且均伴有一定的毒性。鉴于它们在转移性肾细胞癌方面取得的成功，这些药物正在一项名为 E2805 的大型、随机、双盲、多中心试验中作为辅助疗法进行研究。正在收集所有患者的样本，它们提供了一个独特的机会来开发模型来预测这些药物的临床益处，并建立模型来预测在一个非常大的队列的多中心临床试验环境中未经治疗的患者的预后。索拉非尼和舒尼替尼有多个靶点，我们的目的是确定最重要的预测标记物。我们将研究血管生成标记物、MAPK 通路成员和其他已知靶标。在使用 AQUA 的初步研究中，我们发现肿瘤细胞中血管内皮生长因子 (VEGF) 受体水平较高的肾细胞癌往往具有较低的微血管密度和较差的生存率。我们假设肿瘤细胞中 VEGF 受体表达高的患者比微血管密度高的患者更有可能从治疗中受益。 我们将扩展 AQUA，通过用不同的荧光团掩盖肿瘤和血管，从而能够同时评估肿瘤和血管中的目标。我们将利用未经治疗的肾细胞癌患者的历史队列，为索拉非尼和舒尼替尼的所有已知靶点建立染色条件，并选择肿瘤、内皮和邻近正常组织中血管生成的介质。然后，我们将在 E2805 患者的训练组 (67%) 中评估 VHL 突变以及索拉非尼和舒尼替尼靶标的表达，并为每种药物生成预测模型，以便在测试组中进行验证。标准临床协变量和 VHL 突变状态将纳入模型中。我们还将使用这些分子标记和临床协变量来改善安慰剂治疗患者的当前预后模型。这些模型可用于选择患者进行 RCC 的最佳辅助治疗（索拉非尼、舒尼替尼或两者都不使用），并且这种方法也可以在其他临床环境中进行研究。