Models to predict prognosis and benefit from adjuvant therapy in renal cell carci

描述（由申请人提供）：靶向抗癌药物的成功开发通常与预测性测定相结合，从而能够对更有可能从治疗中获益的患者进行选择性治疗。免疫组织化学常用于评估药物靶点的表达，但其存在主观性和缺乏定量测量。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（AQUA）方法。作为对PA-08-134的回应，我们建议扩展AQUA以同时评估肿瘤和内皮细胞，并开发模型来预测辅助索拉非尼和舒尼替尼治疗肾细胞癌（RCC）的临床获益，以及预测未经治疗患者预后的模型。 传统上，RCC是一种对全身治疗具有高度抗性的疾病。然而，多种靶向治疗，包括索拉非尼和舒尼替尼，最近彻底改变了转移性RCC的方法。这两种药物对RCC患者的亚群都有效，并且都与一些毒性有关。鉴于它们在转移性RCC中的成功，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治疗进行研究。正在收集所有患者的样本，它们提供了一个独特的机会，可以开发模型来预测这些药物的临床获益，并在一个非常大的队列中进行多中心临床试验，预测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志物。我们将研究血管生成标志物，MAPK通路的成员和其他已知的目标。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RCC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受体高表达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蔽肿瘤和血管，实现对肿瘤和血管中靶点的同时评估。我们将建立索拉非尼和舒尼替尼的所有已知靶点的染色条件，并使用未经治疗的RCC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质。然后，我们将在E2805患者的训练集（67%）中评估VHL突变和索拉非尼和舒尼替尼靶点的表达，并为每种药物生成预测模型，并在测试集中进行验证。将标准临床协变量和VHL突变状态纳入模型。我们还将使用这些分子标志物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RCC的最佳辅助治疗（索拉非尼，舒尼替尼或两者都不）的患者，并且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：在某些癌症中，靶向治疗（特异性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，并且它们的成功通常与最重要的药物靶点的鉴定和对肿瘤表达靶点的患者的选择性治疗相结合。我们建议开发索拉非尼和舒尼替尼的预测模型（正在研究的新药，以减少肾癌转移的发展），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获益的患者，并使其余患者免于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，自动分析活检标本中的目标水平，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。

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.