(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

基本信息

批准号：
8851546
负责人：
Jason C Reed
金额：
$ 48.37万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
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/8851546
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 Health Heterogeneity Hour Human Image Image Analysis In Vitro Incidence Incubators Individual Interferometry Kinetics Life Link MAP Kinase Gene Malignant Neoplasms Measures 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 personalized medicine preclinical study resistance mechanism response screening success targeted treatment therapy resistant tumor

项目摘要

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：该提案解决了 D 组挑衅性问题（PQD5）：由于当前预测新候选药物在人类中的功效或毒性的方法通常不准确，我们是否可以开发新方法来测试潜在的治疗药物，从而产生更好的反应预测？我们将解决预测治疗反应的关键缺陷，以预测肿瘤复发并改善患者的治疗结果，这通常是基于肿瘤异质性。我们将通过开发和应用一种新型单细胞反应测量技术（称为高通量筛选活细胞干涉仪（HTS-LCI））来实现这一目标，以量化药物暴露之前和期间单细胞生物量的暂时变化。通过数万个时间依赖性生物量分布，我们将快速表征肿瘤对治疗的异质动力学反应，以提供定量统计分类器。我们的提议具有变革性，对所有类型的癌症都有广泛的影响，但在这里我们重点关注转移性黑色素瘤（主要是 III-IV 期），因为 1) 它是一种发病率不断增加的常见癌症，2) 通常会迅速致命，3)众所周知，靶向治疗和耐药性。具体而言，约 50% 的黑色素瘤中存在 MAPK 通路激活 BRAF 丝氨酸/苏氨酸激酶突变。重要的是，已充分表征的 BRAF 抑制剂 (BRAFi) 敏感和耐药细胞系以及新鲜的黑色素瘤患者样本可随时用于原理验证临床前研究。个性化医疗方法依靠静态生物标志物、基因组和表观遗传参数来完善治疗选择和预测预后，但它们都未能纳入治疗反应，这是一个关键的遗漏。经过验证的个体化肿瘤细胞反应分析可能对治疗效果、快速癌症诊断、预后和肿瘤复发预测产生巨大影响。为了实现这一目标，我们提出了一种包含三个创新组件的新方法，其中包括 1) 设计 HTS-LCI 以实时量化响应治疗剂中肿瘤细胞生物量的变化； 2) 使用配对的 BRAFi 敏感和耐药的患者来源的转移性黑色素瘤细胞系，我们的合作者已对这些细胞系进行了广泛的基因组、表观基因组和表达谱分析； 3) 通过与 Jonsson 综合癌症中心临床医生合作以及他们正在进行的早期临床试验，利用我们立即获取去识别化的患者样本。我们提案的具体目标是：目标 1：生成 BRAFi 敏感且耐药的配对黑色素瘤细胞系统计分类器。目标 2：设计 HTS-LCI，以在 36 孔板中进行多药物生长速率分析。目标 3：评估 HTS-LCI 对 BRAFi 敏感和抗性品系的快速响应检测。目标 4：应用 HTS-LCI 平台对新鲜黑色素瘤患者样本进行生物量分析。