Genotype and phenotype predictors in therapy response in renal cell carcinoma

肾细胞癌治疗反应的基因型和表型预测因子

基本信息

批准号：
8473174
负责人：
KEITH T FLAHERTY
金额：
$ 42.13万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-07-01 至 2014-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8473174
关键词：
Address Algorithms American Angiogenesis Inhibitors Angiogenesis Promoter Antigens Apoptosis BAY 54-9085 Behavior Bioinformatics Biological Biological Markers Blood Vessels Cell Proliferation Characteristics Clear Cell Cleaved cell Clinical Clinical Trials Clinical Trials Cooperative Group Combination Drug Therapy Combined Modality Therapy Computer-Assisted Image Analysis Data Development Drug usage Eastern Cooperative Oncology Group Endothelial Cells Facies Foundations Future Genetic screening method Genotype Goals Human Image Analysis Incidence Individual Laboratories Link Machine Learning Malignant Neoplasms Measurement Metastatic Renal Cell Cancer Molecular Multivariate Analysis Mus Mutation Neoplasms in Vascular Tissue Newly Diagnosed Organ Outcome Pathogenesis Patients Pattern Pericytes Pharmaceutical Preparations Phase Phenotype Phosphotransferases Proto-Oncogene Proteins c-akt Quantitative Microscopy Renal Cell Carcinoma Renal carcinoma Reporting Resistance STAT3 gene Signal Pathway Signal Transduction Solid Specimen Staining method Stains Stress System Systems Analysis Testing Therapeutic Therapeutic Agents Tumor Angiogenesis VHL mutation Vascular Endothelial Cell Vascular Endothelial Growth Factor Receptor Vascular Endothelial Growth Factor Receptor-2 Vascular Endothelial Growth Factors angiogenesis bHLH-PAS factor HLF base bevacizumab c-myc Genes cancer therapy caspase-3 cell behavior human FRAP1 protein hypoxia inducible factor 1 intercellular communication mutant neoplastic cell novel novel therapeutics predictive modeling response success tool tumor

项目摘要

ABSTRACT Therapy of renal cell carcinoma (RCC) has been transformed in recent years by the efficacy of targeted agents that inhibit kinases involved in critical cellular signaling pathways and VEGF, a primary driver of tumor angiogenesis. However, patient response to these therapeutics is highly variable and currently cannot be predicted based on clinical or pathological data or available laboratory/genetic testing. The goal of this proposal is to develop predictors of therapeutic response for patients with the most common subtype of RCC, clear cell (ccRCC), based on novel tests. Our approach is based primarily on the hypothesis that the targeted agents used in ccRCC therapy - bevacizumab, sunitinib and sorafenib - inhibit tumor vessel activation and that the activation status and stability of tumor vessels are major determinants of response. We also hypothesize that the phenotype of ccRCC tumor vessels is linked to the molecular pathobiology of the tumor cells. In particular, as these drugs also can inhibit tumor cell signaling and modulate their behavior, ccRCC tumor cell signaling, HIF-1/HIF-2 (hypoxia-inducible factor) expression and VHL function are potential determinants of response. To examine parameters of vascular phenotype, tumor cell phenotype and VHL genotype as predictors of response to therapy, ccRCC specimens will undergo multiplex immunostaining for the appropriate biomarker antigens and be analyzed by a novel computer-assisted image analysis system that objectively quantifies analyte staining on a cellular (cytometric) basis as well as by traditional pixel-based analysis. These studies will be performed on tumors of ccRCC patients treated with single-agent bevacizumab, sunitinib or sorafenib in ongoing multi-institutional phase II (ECOG2804) and phase III (ECOG2805) clinical trials, using tumor blocks from a subset of 90, 170 and 170 appropriate ccRCC patients for therapy with the respective drugs. The specific aims of this proposal are (Aim 1) to analyze the vascular and endothelial cell activation phenotype and vessel pericyte coverage in ccRCC tumors; (Aim 2) to analyze tumor cell signaling, HIF phenotype and VHL genotype in ccRCC tumors; and (Aim 3) to correlate parameters quantified in the prior aims for relationships to each other, to therapeutic outcome and to develop parsimonious predictive models of therapeutic response using biostatistical and bioinformatics approaches. The results of these studies should allow identification of the most appropriate drugs for treating individual patients with ccRCC and assist in the rational development of second-line and combination drug therapies. Beyond ccRCC, the targeted agents under study are used in the therapy of an ever expanding number of cancers, and the response predictors developed for ccRCC, where these agents are best studied because they are used as single-agents, may be useful for predicting response of these other cancers to combination therapy incorporating the targeted agents.

抽象的近年来，肾细胞癌（RCC）的治疗因靶向药物的功效而发生了转变。抑制参与关键细胞信号传导途径的激酶和 VEGF（VEGF 的主要驱动因素）的药物肿瘤血管生成。然而，患者对这些疗法的反应差异很大，目前无法根据临床或病理数据或可用的实验室/基因检测来预测。这该提案的目标是为患有最常见疾病的患者开发治疗反应的预测因子基于新测试的 RCC 亚型，透明细胞 (ccRCC)。我们的方法主要基于假设 ccRCC 治疗中使用的靶向药物 - 贝伐单抗、舒尼替尼和索拉非尼 - 抑制肿瘤血管活化，肿瘤血管的活化状态和稳定性至关重要响应的决定因素。我们还假设 ccRCC 肿瘤血管的表型与肿瘤细胞的分子病理学。尤其是这些药物还可以抑制肿瘤细胞信号传导并调节其行为、ccRCC 肿瘤细胞信号传导、HIF-1/HIF-2（缺氧诱导型）因子）表达和 VHL 功能是反应的潜在决定因素。检查参数血管表型、肿瘤细胞表型和 VHL 基因型作为治疗反应的预测因子， ccRCC 标本将针对适当的生物标志物抗原进行多重免疫染色，并通过新颖的计算机辅助图像分析系统进行分析，该系统可以客观地量化分析物基于细胞（细胞计数）以及传统的基于像素的分析进行染色。这些研究将对接受贝伐单抗、舒尼替尼单药治疗的 ccRCC 患者的肿瘤进行索拉非尼正在进行多机构 II 期 (ECOG2804) 和 III 期 (ECOG2805) 临床试验，使用来自 90、170 和 170 名适当 ccRCC 患者的子集的肿瘤块进行治疗各自的药物。该提案的具体目标是（目标 1）分析血管和内皮细胞 ccRCC 肿瘤中的细胞活化表型和血管周细胞覆盖；（目标2）分析肿瘤细胞 ccRCC 肿瘤中的信号传导、HIF 表型和 VHL 基因型；和（目标 3）关联参数量化先前的目标，包括相互关系、治疗结果和发展使用生物统计学和生物信息学的治疗反应的简约预测模型接近。这些研究的结果应该能够确定最合适的药物治疗个别 ccRCC 患者并协助合理开发二线和联合药物治疗。除了 ccRCC 之外，正在研究的靶向药物也用于治疗癌症数量不断增加，以及为 ccRCC 开发的反应预测因子，其中这些代理得到了最好的研究，因为它们被用作单一代理，可能有助于预测这些其他癌症对包含靶向药物的联合疗法的反应。