(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: 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
• 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

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）：本提案解决了D组争议问题（PQD5）：由于目前预测人类新药候选药物的疗效或毒性的方法通常是不准确的，我们能否开发新的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，以预测肿瘤复发和改善患者预后，这通常是基于肿瘤的异质性。我们将通过开发和应用一种新的单细胞反应测量技术来实现这一目标，该技术被称为高通量筛选活细胞干涉仪（HTS-LCI），以量化单细胞生物量在药物暴露之前和期间的时间变化。通过10000个随时间变化的生物量分布图，我们将快速表征肿瘤对治疗的异质性动力学反应，从而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具有广泛的意义，但在这里我们关注的是转移性黑色素瘤（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)通常是快速致命的，3)关于靶向治疗和耐药性的了解很多。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%的黑色素瘤中。重要的是，表征良好的BRAFi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很容易用于原理验证的临床前研究。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来优化治疗选择和预测预后，但它们都没有纳入治疗反应，这是一个关键的遗漏。经过验证的、个性化的肿瘤细胞反应谱分析对治疗效果、快速癌症诊断、预后和肿瘤复发预测具有巨大的影响。为了实现这一目标，我们提出了一种包含三个创新组成部分的新方法，包括：1)设计HTS-LCI以实时量化反应治疗剂中肿瘤细胞生物量的变化；2)使用配对的BRAFi敏感和耐药患者来源的转移性黑色素瘤细胞系，这些细胞系已经被我们的合作者广泛地用于基因组、表观基因组和表达谱分析；3)通过与琼森综合癌症中心的临床医生及其正在进行的早期临床试验合作，利用我们对去识别患者样本的即时访问。我们提案的具体目标是：目标1：生成一个BRAFi敏感和耐药的配对黑色素瘤细胞系统计分类器。目的2：设计HTS-LCI用于36孔板格式的多药物生长速率分析。目的3：评价HTS-LCI快速反应检测BRAFi敏感和耐药品系的效果。目的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。