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: 8085276
• 负责人: Harriet M. Kluger
• 金额: $ 38.12万
• 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/8085276
• 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 Delivery Systems; 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; 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. PUBLIC HEALTH RELEVANCE: In some cancers, targeted therapies (drugs that specifically inhibit certain key proteins in the cancer cell) have dramatically impacted management of the disease, and their success has usually been coupled with identification of the most important drug target/s and selective treatment of those patients whose tumors express the target. We propose to develop predictive models for sorafenib and sunitinib (new drugs that are being studied to decrease development of metastases for kidney cancer), which will enable us to selectively treat those patients that are more likely to derive benefit from these drugs, and spare the rest of the patients the toxicity and cost associated with this therapy. We will use a newly developed method of automated analysis of target levels from biopsy specimens, and in the future we will be able to apply this technology to other diseases and other targeted therapies.

描述(由申请人提供)：靶向抗癌药物的成功开发通常伴随着预测性分析，使更有可能从治疗中受益的患者能够进行选择性治疗。免疫组织化学常用于评价药物靶点的表达，但存在主观性差、缺乏定量方法等问题。我们开发了一种用于原位评估蛋白质水平的自动定量分析方法(AQAA)。作为对PA-08-134的回应，我们建议将Aqua扩展到同时评估肿瘤和内皮细胞，并开发预测佐剂索拉非尼和舒尼替尼治疗肾癌(RCC)的临床益处的模型，以及预测未经治疗的患者预后的模型。肾癌传统上是一种对系统治疗高度抵抗的疾病。然而，包括索拉非尼和舒尼替尼在内的多种靶向治疗方法最近已经彻底改变了转移性肾癌的治疗方法。这两种药物对肾癌患者的亚群都有效，而且都有一定的毒性。鉴于它们在转移性肾癌中的成功，这些药物正在进行一项名为E2805的大型、随机、双盲、多中心试验，作为辅助治疗进行研究。目前正在收集所有患者的样本，这些样本提供了一个独特的机会来开发预测这些药物的临床益处的模型，以及在一个非常大的队列的多中心临床试验中预测未经治疗的患者的预后的模型。索拉非尼和舒尼汀有多个靶点，我们的目的是寻找最重要的预测标记物/S。我们将研究血管生成标记物、MAPK通路成员和其他已知靶点。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)受体水平高的RCC往往具有较低的微血管密度和较差的存活率。我们假设，肿瘤细胞中高表达血管内皮生长因子受体的患者比那些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蔽肿瘤和血管，使其能够同时评估肿瘤和血管中的靶点。我们将为所有已知的索拉非尼和舒尼替尼的靶标建立染色条件，并使用未经治疗的肾癌患者的历史队列来选择肿瘤、内皮和邻近正常组织中血管生成的介体。然后，我们将在E2805名患者的训练集(67%)中评估VHL突变和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，以在测试集中进行验证。标准的临床协变量和VHL突变状态将被纳入模型中。我们还将使用这些分子标志物和临床协变量来改善目前接受安慰剂治疗的患者的预后模型。这些模型可以用来为肾癌的最佳辅助治疗(索拉非尼、舒尼替尼或两者都不)选择患者，这种方法也可以在其他临床环境中进行研究。 公共卫生相关性：在一些癌症中，靶向治疗(特定抑制癌细胞中某些关键蛋白质的药物)极大地影响了疾病的管理，它们的成功通常伴随着确定最重要的药物靶点/S，以及对其肿瘤表达靶点的患者进行选择性治疗。我们建议开发索拉非尼和舒尼替尼(正在研究中的新药，以减少肾癌转移的发展)的预测模型，这将使我们能够选择性地治疗那些更有可能从这些药物中受益的患者，并使其余患者避免与这种治疗相关的毒性和成本。我们将使用一种新开发的方法，从活检样本中自动分析目标水平，在未来，我们将能够将这项技术应用于其他疾病和其他靶向治疗。