Development and Pre-Clinical Validation of Quantitative Imaging of Cell State Kinetics (QuICK) for Functional Precision Oncology

用于功能性精准肿瘤学的细胞状态动力学定量成像 (QuICK) 的开发和临床前验证

• 批准号: 10737379
• 负责人: Robert Laird Judson-Torres
• 金额: $ 39.05万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-12 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10737379
• 关键词: Address; Adjuvant; Adjuvant Therapy; Adoption; Animal Model; Animals; Area; Behavior; Benchmarking; Biocompatible Materials; Biological Models; Biopsy; Cancer Intervention; Cancer Patient; Caring; Cell Line; Cells; Cessation of life; Characteristics; Classification; Clinical; Clinical Trials; Clonal Expansion; Coupled; Darkness; Data; Data Analyses; Decision Making; Development; Disease Resistance; Distal; Drug resistance; Engineering; Epigenetic Process; Exposure to; Flow Cytometry; Fluorescence; Fluorescent Antibody Technique; Future; Goals; Heterogeneity; Human; Image; Immunotherapy; Individual; Intervention; Kinetics; Label; Longterm Follow-up; Malignant Neoplasms; Measurement; Measures; Melanoma Cell; Melanosomes; Methods; Microscopy; Modification; Molecular Profiling; Monitor; Morphology; Multimodal Imaging; Mutation; Nature; Neoplasm Metastasis; Organelles; Patient-Focused Outcomes; Patients; Performance; Phase; Phenotype; Population; Population Heterogeneity; Pre-Clinical Model; Primary Neoplasm; Punch Biopsy; Receiver Operating Characteristics; Reporter; Resistance; Resolution; Sampling; Selection for Treatments; Series; Skin Cancer; Specimen; Speed; Surface; System; Technology; Testing; Therapeutic; Translating; Translations; Validation; Visualization; Work; analytical method; cancer risk; cancer type; cellular imaging; checkpoint inhibition; checkpoint therapy; clinical care; clinical decision-making; clinical translation; clinically actionable; density; genetic signature; improved; in vivo; individual patient; light scattering; machine learning classifier; melanoma; multimodality; neoplastic cell; new technology; novel; novel strategies; patient derived xenograft model; personalized approach; pre-clinical; precision medicine; precision oncology; predictive test; prevent; programs; prospective; prototype; quantitative imaging; research clinical testing; response; standard of care; stem; stem-like cell; success; survival prediction; targeted treatment; therapeutic candidate; therapy resistant; transcriptomics; tumor; tumor progression

PROJECT ABSTRACT Functional precision oncology is the practice of assessing the phenotype of biopsied tumor cells upon perturbation, e.g. treatment with candidate therapies, to yield actionable information fast enough to influence clinical decision making. For a functional precision approach to provide actionable information on tumors’ response to candidate therapeutics, it must: retain specimen heterogeneity, monitor all biologically important phenotypes, make longitudinal observations, and - since clonal expansion of individual cells is sufficient to drive tumor progression or resistance – have single cell resolution. One example where functional precision oncology could be valuable is the choice of therapy for the approximately 50% of melanoma patients harboring the BRAFV600E mutation. Such patients have two options – immune checkpoint inhibition (ICI) or targeted therapy (TT). Either strategy is capable of curing patients in many cases but neither option works for all patients. Poor response to either is caused by the pre-existence and/or emergence of phenotypes resistant to each therapeutic option. A clinical test that could predict, on a personalized level, which patients are likely to respond or acquire resistance to either of these therapies is among the most pressing clinical needs in melanoma care. On a population level, more patients present with durable response to ICI than to TT, and so ICI is the default standard of care and a predictive test must achieve high accuracy to influence clinical decision making. We have pioneered the use of quantitative phase imaging (QPI) for rapid and label-free phenotype assessment of melanoma cells, including monitoring for therapeutic resistance. Our area under the receiver operator characteristic curve (AUC) for predicting resistance under 48 h is 0.84-0.90 – which is promising, but insufficient and needs validation. We propose to construct a new technological and analytical platform with two modifications. First, we will augment QPI with a second imaging module to measure light scatter via a new method we have developed based on darkfield microscopy. Light scatter is traditionally measured using flow cytometry and is predictive of relevant cell phenotypes in a myriad of cancer types, including, our preliminary data show, therapeutic resistance in melanoma. Second, we will establish an analytical pipeline for assessing cell state dynamics which we anticipate will yield a classifier that is more accurate across heterogeneous biopsies as compared to current approaches. In this proposal, we describe a series of engineering and analytical steps, coupled with technical milestones and target quantitative goals benchmarked against existing approaches for developing an approach we call Quantitative Imaging of Cell state Kinetics (QuICK), as well as a proof of principle study using clinical biopsies. If successful, we will have built a prototype platform with high potential to improve the care of melanoma patients through accurate personalized matching to the best therapy and we will be well situated for prospective clinical trials. In addition, QuICK has the potential to inform functional precision medicine approaches for other cancers that would benefit from rapid classification of live cell phenotypes with single cell resolution.

项目摘要 功能精度肿瘤学是评估活检肿瘤细胞表型的实践 扰动，例如使用候选疗法治疗，以产生足够快的可行信息以影响 临床决策。对于功能上的精确方法，可以提供有关肿瘤的可行信息 对候选疗法的反应，必须：保留标本异质性，监测所有对生物学重要的重要性 表型，进行纵向观察，并且由于单个细胞的克隆扩张足以驱动 肿瘤进展或抗性 - 具有单细胞分辨率。功能精度肿瘤学的一个示例 可能值得重视的是大约50％携带的黑色素瘤患者的治疗选择 BRAFV600E突变。这样的患者有两种选择 - 免疫抑制（ICI）或有针对性的治疗 （TT）。在许多情况下，任何一种策略都能治愈患者，但两种选择都适用于所有患者。贫穷的 对两种疗法的抗性和/或出现的反应是对每种疗法的抗性作用 选项。可以在个性化水平上预测患者可能会做出反应或获取的临床测试 对这两种疗法的抗性是黑色素瘤护理中最紧迫的临床需求之一。在 人口水平，对ICI的持久反应的患者多于对TT的持久反应，因此ICI是默认标准 护理和预测性测试必须达到高精度，以影响临床决策。我们已经开创了开创性 使用定量相成像（QPI）来快速且无标记的表型评估黑色素瘤细胞， 包括监视热电阻。我们的接收器操作员特征曲线（AUC）下的区域 在48小时以下预测电阻为0.84-0.90 - 这是有希望的，但不足，需要验证。我们 提案，构建一个具有两个修改的新技术和分析平台。首先，我们将增加 具有第二个成像模块的QPI通过我们基于的新方法测量光散射 黑场显微镜。传统上使用流式细胞仪测量光散射，并可以预测相关的 无数癌症类型的细胞表型，包括我们的初步数据显示，治疗性抗性 黑色素瘤。其次，我们将建立一个分析管道来评估我们预期的细胞状态动力学 与当前方法相比，将产生一个在异质活检中更准确的分类器。 在此建议中，我们描述了一系列的工程和分析步骤，再加上技术里程碑和 目标定量目标基准针对现有方法开发一种我们称为方法的方法 细胞态动力学（快速）的定量成像以及使用临床活检的原理研究证明。 如果成功，我们将建立一个具有很高潜力来改善黑色素瘤患者护理的原型平台 通过准确的个性化匹配到最佳疗法，我们将适合前瞻性临床 试验。此外，Quick有可能为其他癌症提供功能精确医学方法 这将受益于用单细胞分辨率快速分类的活细胞表型。