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 功能性精确肿瘤学是根据肿瘤细胞的生物学特性评估活检肿瘤细胞表型的实践。 扰动，例如使用候选疗法进行治疗，以足够快地产生可操作的信息， 临床决策对于功能精确方法，以提供关于肿瘤的可操作信息， 对候选疗法的反应，它必须：保持标本异质性，监测所有生物学重要的 表型，进行纵向观察，并且-由于单个细胞的克隆扩增足以驱动 肿瘤进展或耐药-具有单细胞分辨率。其中一个例子是功能性精准肿瘤学 可能是有价值的是选择治疗的约50%的黑色素瘤患者携带的黑色素瘤， BRAFV 600 E突变。这些患者有两种选择-免疫检查点抑制（ICI）或靶向治疗 （TT）。在许多情况下，这两种策略都能治愈患者，但都不适用于所有患者。贫困 对任一种的反应是由对每种治疗药物具有抗性的表型的预先存在和/或出现引起的 选项.一种临床测试，可以预测，在个性化的水平上，哪些患者可能会响应或获得 对这两种疗法的耐药性是黑色素瘤护理中最紧迫的临床需求之一。上 在人群水平上，ICI的持久应答患者多于TT，因此ICI是默认标准 护理和预测性测试必须达到高准确性，以影响临床决策。我们开创 使用定量相位成像（QPI）对黑素瘤细胞进行快速和无标记的表型评估， 包括监测治疗抗性。受试者操作特征曲线下面积（AUC） 用于预测48小时以下的抗性的预测值为0.84-0.90 -这是有希望的，但不够，需要验证。我们 建议建立一个新的技术和分析平台，有两个修改。首先，我们将增加 带有第二成像模块的QPI，通过我们开发的新方法测量光散射 暗视野显微术传统上使用流式细胞术测量光散射，并且光散射可预测相关的细胞周期。 我们的初步数据显示，在无数的癌症类型中， 黑素瘤其次，我们将建立一个分析管道，用于评估我们预期的细胞状态动态 将产生与当前方法相比跨异质活检更准确的分类器。 在本提案中，我们描述了一系列工程和分析步骤，以及技术里程碑， 以现有的方法为基准，确定量化目标，以制定我们称为 细胞状态动力学定量成像（Quick），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大潜力的原型平台，以改善黑色素瘤患者的护理 通过准确的个性化匹配最佳治疗，我们将很好地定位于前瞻性临床 审判此外，QuICK有可能为其他癌症提供功能性精准医学方法 这将受益于具有单细胞分辨率的活细胞表型的快速分类。