(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: 9067822
• 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/9067822
• 关键词: Address; Arts; BRAF gene; Benchmarking; Biological; Biological Assay; 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; 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; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phase; Plug-in; Prediction of Response to Therapy; Process; Protein Kinase; Protein-Serine-Threonine Kinases; Recurrence; Relapse; Reproducibility; Resistance; Sampling; Signal Pathway; Signal Transduction; Speed; Staging; Suspension substance; Suspensions; Technology; Testing; 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; genomic biomarker; high throughput screening; improved; inhibitor/antagonist; innovation; melanoma; neoplastic cell; novel; novel strategies; novel therapeutics; outcome forecast; personalized medicine; preclinical study; resistance mechanism; response; screening; success; targeted treatment; therapy resistant; treatment response; tumor; tumor heterogeneity

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)：这项建议解决了D组的挑衅性问题(PQD5)：由于目前预测新药候选药物在人类身上的疗效或毒性的方法往往不准确，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？我们将解决在预测治疗反应方面的关键缺陷，以预测肿瘤复发和改善患者预后，这通常基于肿瘤的异质性。我们将通过开发和应用一种名为高通量筛选活细胞干涉仪(HTS-LCI)的新型单细胞反应测量技术来实现这一目标，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10,000个与时间相关的生物量分布，我们将快速表征肿瘤对治疗的异质性动力学反应，以便提供一个定量的统计分类器。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，但这里我们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上升的常见癌症，2)通常是迅速致命的，以及3)关于靶向治疗和耐药性的许多已知。具体地说，激活MAPK通路的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，特征良好的BRAF-抑制剂(BRAFi)敏感和耐药细胞系和新鲜的患者黑色素瘤样本随时可用于临床前原则验证研究。个性化医学中的方法依赖于静态生物标记物、基因组和表观遗传学参数来改进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图谱可能会对治疗效果、癌症快速诊断、预后和肿瘤复发预测产生巨大影响。为了实现这一目标，我们提出了一种包括三个创新组成部分的新方法，包括1)设计HTS-LCI以实时量化肿瘤细胞生物量的变化；2)使用成对的BRAFi敏感和耐药患者来源的转移性黑色素瘤细胞系，这些细胞系已由我们的合作者进行了广泛的基因组、表观基因组和表达谱分析；以及3)通过与Jonsson综合癌症中心的临床医生和他们正在进行的早期临床试验的合作，利用我们立即获得的未鉴定的患者样本。我们建议的具体目标是：目标1：生成BRAFi敏感和耐药的成对黑色素瘤细胞系统计分类器。目的2：设计HTS-LCI，用于36孔板形式的多药物生长速率分析。目的：评价HTS-LCI快速检测BRAFi敏感和抗性品系的价值。目的：应用HTS-LCI平台对新鲜黑色素瘤患者样本进行生物量分析。