(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.

项目总结/摘要 该提案解决了D组挑衅性问题（PQD 5）：由于目前预测 候选新药在人体中的疗效或毒性往往是不准确的，我们能否开发新的方法， 测试潜在的治疗药物，以更好地预测反应？ 我们将讨论在预测治疗反应以预测肿瘤复发方面的关键缺陷 并改善患者的预后，这通常是基于肿瘤的异质性。我们将实现这一目标 通过开发和应用一种新的单细胞反应测量技术，称为高分辨率 筛选活细胞干涉仪（HTS-LCI），以量化单细胞生物量的时间变化， 和药物暴露期间。通过10，000秒的时间依赖性生物量曲线，我们将快速表征 肿瘤对治疗的异质动力学响应，以便提供定量统计分类器。 我们的建议是变革性的，对所有类型的癌症都有广泛的影响，但在这里，我们的重点是 转移性黑色素瘤（主要是III-IV期），因为1）它是一种常见的癌症，发病率不断增加，2） 通常迅速致命，以及3）对靶向治疗和耐药性了解很多。具体来说，MAPK 通路激活BRAF丝氨酸/苏氨酸激酶突变存在于约50%的黑素瘤中。 重要的是，充分表征的BRAF抑制剂（BRAFi）敏感性和抗性细胞系和新鲜患者 黑色素瘤样本可随时用于原理验证临床前研究。 个性化医疗的方法依赖于静态生物标志物、基因组和表观遗传参数， 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 关键性遗漏经验证的个体化肿瘤细胞反应谱可能对 治疗效果、癌症快速诊断、预后和肿瘤复发的预测。达到这个 我们提出了一种新的方法，包括三个创新的组成部分，1）工程HTS- LCI以真实的时间定量响应治疗剂的肿瘤细胞生物量变化; 2）使用配对的 BRAFi敏感性和耐药性患者来源的转移性黑素瘤细胞系已经被广泛研究， 由我们的合作者进行基因组、表观基因组和表达谱表征;以及3）利用我们的 通过与Jonsson Comprehensive合作，立即获取去识别化的患者样本 癌症中心的临床医生和他们正在进行的早期临床试验。我们建议的具体目标是： 目的1：产生BRAFi敏感和抗性配对黑素瘤细胞系统计分类器。 目的2：设计HTS-LCI，用于36孔板格式中的多药物生长速率分析。 目的3：评估用于BRAFi敏感和抗性品系的快速反应检测的HTS-LCI。 目的4：将HTS-LCI平台应用于新鲜黑素瘤患者样本的生物量分析。