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）：成功开发有针对性的抗癌药物，通常与预测性测定相结合，使患者的选择性治疗更有可能受益于治疗。免疫组织化学通常用于评估药物靶标的表达，但它具有主观性和缺乏定量措施。我们开发了一种自动定量分析（Aqua）的方法，用于评估原位蛋白质水平。为了响应PA-08-134，我们建议扩大Aqua，以同时评估肿瘤和内皮细胞，并开​​发模型，以预测辅助索拉非尼和Sunitinib辅助肾细胞癌（RCC）的临床益处，以及模型，以预测未经培训的患者的预后。 RCC传统上是一种对全身治疗高度抗药性的疾病。但是，包括索拉非尼和苏尼替尼在内的多种有针对性疗法最近彻底改变了转移性RCC的方法。两种药物对RCC患者的亚群都有效，并且都与某些毒性有关。鉴于它们在转移性RCC方面的成功，这些药物在大型，随机，双眼的多中心试验中被研究为辅助疗法，称为E2805。正在对所有患者收集标本，他们提供了一个独特的机会来开发模型，以预测这些药物和模型的临床益处，以预测在非常大的队列上的多中心临床试验环境中未经治疗的患者的预后。 Sorafenib和Sunitinb具有多个目标，我们的目的是确定最重要的预测标记。我们将研究血管生成标记，MAPK途径的成员和其他已知靶标。在使用Aqua的初步研究中，我们表明肿瘤细胞中具有高水平血管内皮生长因子（VEGF）受体的RCC往往具有较低的微血管密度和较差的存活率。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。 我们将通过掩盖不同荧光团的肿瘤和血管来扩展Aqua，以同时评估肿瘤和血管中的靶标。我们将使用未经治疗的RCC患者的历史人群来为索拉非尼和舒尼尼的所有已知靶标建立染色条件，并选择肿瘤，内皮和邻近正常组织的血管生成介质。然后，我们将在E2805患者的训练集（67％）中评估Sorafenib和Sunitinib靶标的VHL突变和表达，并为每种药物产生预测模型，并在测试集中得到验证。标准的临床共同变化和VHL突变状态将纳入模型。我们还将使用这些分子标记物和临床共同变化来改善安慰剂治疗的患者中当前的预后模型。这些模型可用于选择患者的RCC最佳辅助治疗（Sorafenib，Sunitinib或两者），并且在其他临床环境中也可以研究这种方法。