(PQD5) Mass Profiling Melanoma Responses to Improve Therapy Choices and Prognosis

(PQD5) 大规模分析黑色素瘤反应以改善治疗选择和预后

• 批准号: 8687449
• 负责人: Jason C Reed
• 金额: $ 48.15万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8687449
• 关键词: Address; Arts; BRAF gene; Benchmarking; Biological; Biological Assay; Biological Markers; Biomass; Biopsy; Cancer Diagnostics; Cell Line; Cell Separation; Cells; Cessation of life; Clinical; Clinical Trials; Clinical assessments; Collaborations; Combined Modality Therapy; Complex; Comprehensive Cancer Center; Detection; Drug Combinations; Drug Exposure; Drug resistance; Drug-sensitive; Engineering; Epigenetic Process; Exposure to; Genomics; Goals; Growth; Heterogeneity; Hour; Human; Image; Image Analysis; In Vitro; Incidence; Incubators; Individual; Interferometry; Kinetics; Life; Link; MAP Kinase Gene; Malignant Neoplasms; Measures; Medicine; Melanoma Cell; Metastatic Melanoma; Methods; Molecular; Molecular Analysis; Molecular Profiling; Molecular Target; Mutation; NRAS gene; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phase; Plug-in; Process; Protein Kinase; Protein-Serine-Threonine Kinases; Recurrence; Relapse; Reproducibility; Resistance; Sampling; Signal Pathway; Signal Transduction; Speed; Staging; Suspension substance; Suspensions; Technology; Testing; Therapeutic; Therapeutic Agents; Time; Toxic effect; Translations; Treatment Efficacy; Tumor Subtype; Validation; base; biophysical techniques; cancer cell; cancer diagnosis; cancer therapy; cancer type; cell growth; cell type; combinatorial; computerized data processing; cost; drug candidate; drug efficacy; epigenomics; genetic analysis; high throughput screening; improved; inhibitor/antagonist; innovation; melanoma; neoplastic cell; novel; novel strategies; outcome forecast; preclinical study; public health relevance; resistance mechanism; response; screening; success; therapy resistant; tumor

PROJECT SUMMARY/ABSTRACT This proposal addresses the Group D Provocative Question (PQD5): Since current methods to predict the efficacy or toxicity of new drug candidates in humans are often inaccurate, can we develop new methods to test potential therapeutic agents that yield better predictions of response? We will address critical shortcomings in predicting therapeutic responses to anticipate tumor recurrence and improve patient outcome, which is usually based on tumor heterogeneity. We will accomplish this goal by developing and applying a novel single-cell response measuring technology, termed a High-Throughput Screening Live Cell Interferometer (HTS-LCI), to quantify single-cell biomass changes temporally, before and during drug exposure. With 10,000s of time-dependent biomass profiles, we will rapidly characterize a tumor's heterogeneous kinetic response to therapy in order to provide a quantitative statistical classifier. Our proposal is transformative with broad implications for all types of cancer, but here we focus on metastatic melanoma (mainly stage III-IV) because 1) it is a common cancer with increasing incidence, 2) is often rapidly fatal, and 3) much is known about targeted therapy and resistance. Specifically, MAPK pathway-activating BRAF serine/threonine kinase mutations are present in ~50% of melanomas. Importantly, well-characterized BRAF-inhibitor (BRAFi) sensitive and resistant cell lines and fresh patient melanoma samples are readily available for proof-of-principle preclinical studies. Approaches in personalized medicine rely on static biomarker, genomic, and epigenetic parameters to refine therapy choice and predict prognosis, but they all fail to incorporate therapeutic response, which is a critical omission. Validated, individualized tumor cell response profiling could have enormous impact on therapeutic efficacy, rapid cancer diagnosis, prognosis, and prediction of tumor recurrence. To reach this goal we propose a new approach with three innovative components that include 1) engineering the HTS- LCI to quantify tumor cell biomass changes in response therapeutic agents, in real time; 2) using paired BRAFi sensitive and resistant patient-derived metastatic melanoma cell lines that have been extensively characterized for genomic, epigenomic, and expression profiling by our collaborators; and 3) utilizing our immediate access to de-identified patient samples through collaboration with Jonsson Comprehensive Cancer Center clinicians and their ongoing early phase clinical trials. The Specific Aims of our proposal are: Aim 1: To generate a BRAFi sensitive and resistant paired melanoma cell line statistical classifier. Aim 2: To engineer the HTS-LCI for multi-drug growth rate profiling in a 36-well plate format. Aim 3: To evaluate the HTS-LCI for rapid response detection of BRAFi sensitive and resistant lines. Aim 4: To apply the HTS-LCI platform for biomass profiling of fresh melanoma patient samples.

项目摘要/摘要 该提案解决了D组挑衅性问题（PQD5）：由于当前的预测方法 人类新药的疗效或毒性通常不准确，我们可以开发新方法 测试潜在的治疗剂，可以更好地预测反应的预测？ 我们将解决预测治疗反应以预测肿瘤复发的关键缺点 并改善患者预后，通常基于肿瘤异质性。我们将实现这个目标 通过开发和应用新颖的单细胞响应测量技术，称为高通量 在 并在药物暴露期间。具有10,000次时间依赖的生物量剖面，我们将迅速表征 肿瘤对治疗的异质动力学反应，以提供定量的统计分类器。 我们的建议具有变革性，对所有类型的癌症都有广泛的影响，但是我们在这里关注 转移性黑色素瘤（主要是III-IV期），因为1）这是一种常见的癌症，发生率增加，2） 通常迅速致命，3）关于靶向治疗和抗药性知之甚少。具体来说，MAPK 途径激活BRAF丝氨酸/苏氨酸激酶突变存在于〜50％的黑色素瘤中。 重要的是，特征良好的BRAF抑制剂（BRAFI）敏感和抗性细胞系以及新鲜的患者 黑色素瘤样品很容易用于原则临床前研究。 个性化医学的方法依赖静态生物标志物，基因组和表观遗传参数 精炼疗法的选择并预测预后，但它们都无法纳入治疗反应，这是 严重的遗漏。经过验证的个性化肿瘤细胞反应分析可能会对 治疗功效，快速癌症诊断，预后和肿瘤复发的预测。达到这个 目标我们提出了一种新方法，其中包括三个创新组件，其中包括1）工程HTS- LCI实时量化肿瘤细胞生物量在反应治疗剂中的变化； 2）使用配对 Brafi对患者衍生的转移性黑色素瘤细胞系的敏感敏感和耐药性，这些细胞已广泛 我们的合作者以基因组，表观基因组和表达分析为特征； 3）利用我们的 通过与Jonsson综合的合作，立即访问取消识别的患者样品 癌症中心临床医生及其正在进行的早期临床试验。我们提案的具体目的是： 目标1：生成BRAFI敏感和抗性的配对黑色素瘤细胞系统计分类器。 AIM 2：以36孔板格式设计HTS-LCI的HTS-LCI多药增长率分析。 AIM 3：评估HTS-LCI以快速响应BRAFI敏感和抗性线。 AIM 4：应用HTS-LCI平台进行新鲜黑色素瘤患者样品的生物量分析。