A Novel Fluorescence Imaging Platform to Predict Response to Combinatorial Tyrosine Kinase Inhibitors

预测组合酪氨酸激酶抑制剂反应的新型荧光成像平台

• 批准号: 10356738
• 负责人: Summer Lynne Gibbs
• 金额: $ 24.57万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10356738
• 关键词: Address; Aging; Antibodies; Bar Codes; Behavior; Binding; Biological Models; Cancer Model; Cancer Patient; Cell Line; Cell Survival; Cells; Clinical Trials; Color; Combined Modality Therapy; Complex; DNA; Development; Disease; Drug Targeting; Drug resistance; Epidermal Growth Factor Receptor; FDA approved; Flow Cytometry; Future; Genomics; Gold; Heterogeneity; Image; Immune; Immunofluorescence Immunologic; In Situ; Label; MEKs; Malignant Neoplasms; Maps; Measures; Modeling; Monitor; Morphology; Mus; Mutation; Nature; Non-Small-Cell Lung Carcinoma; Optics; Patients; Performance; Periodicity; Pharmaceutical Preparations; Phenotype; Phosphotransferases; Plasma; Proteins; Proteome; Proteomics; Protocols documentation; Reagent; Resistance; Resolution; Screening procedure; Signal Pathway; Signal Transduction; Specimen; Technology; Testing; Therapeutic; Tissues; Transgenic Organisms; Treatment Efficacy; Treatment outcome; Tyrosine Kinase Inhibitor; Validation; Western Blotting; Work; Xenograft Model; Xenograft procedure; base; cancer therapy; cancer type; clinically relevant; combinatorial; curative treatments; drug distribution; drug standard; fluorescence imaging; imaging agent; imaging platform; improved; in vivo; inhibitor therapy; innovation; kinase inhibitor; mathematical model; molecular targeted therapies; mutational status; novel; predicting response; prediction algorithm; protein expression; proteomic signature; resistance mechanism; response; small molecule therapeutics; targeted cancer therapy; therapy outcome; therapy resistant; tool; treatment response; treatment strategy; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; tumor xenograft

PROJECT SUMMARY Deregulation of kinase function in cell signaling pathways is implicated in numerous cancers. In response, kinase inhibitors (KIs) have been developed to interact with these kinases for highly specific treatment. Though nearly 50 KIs have been FDA-approved, KI monotherapy is seldom curative, likely owing to tumor heterogeneity and acquired resistance. For example, intra-tumoral heterogeneity can result in the treatment of sensitive cell sub- populations, while simultaneously promoting the outgrowth of resistant “persister cells.” In response, effective combination therapies must be tailored to known resistance mechanisms to efficiently engage with their targets and exploit cellular vulnerabilities. However, standard drug screening tools (e.g., plasma analysis, western blot [WB]) are bulk in nature, and no established technology exists to quantify KI target engagement, concomitant with local protein expression, while assessing tumor response heterogeneity. To address these shortcomings, our group has (1) developed protocols to fluorescently label KIs (and other small molecule therapeutics) that mimic the native drug, (2) advanced a novel intracellular paired agent imaging (iPAI) platform to quantify drug target availability (DTA) with these fluorescent KIs, and (3) established and validated a highly multiplexed im- munostaining strategy utilizing DNA barcoded antibodies, enabling in situ cyclic immunofluorescence (cyCIF) imaging. In this proposal, we will combine these three complementary innovations into a fluorescence imaging platform we call TRIPODD (Therapeutic Response Imaging through Proteomics and Optical Drug Distribution and binding). Herein, we will use TRIPODD to demonstrate the capability of iPAI to predict mono- and combina- torial KI drug response and uncover drug resistance mechanisms across whole tumor specimens with single- cell resolution. To achieve this, iPAI will be expanded to three-color imaging (i.e., two-drug DTA) while cyCIF will be applied to monitor proteomic therapeutic response to gain a mechanistic understanding of clinically relevant combination therapy outcomes. Epidermal growth factor receptor mutation positive (EGFRmut+) non-small cell lung carcinoma (NSCLC), which currently lacks curative treatment, will serve as our model system. We hypoth- esize that TRIPODD—as the first technology capable of comparing drug distribution and binding (iPAI) directly to proteomic markers (cyCIF) at the cellular level—will be critical to uncovering salient mechanisms of therapeutic response and resistance in NSCLC and ultimately enable tailored therapeutic strategy optimization based on predictive algorithms. This hypothesis will be tested through the following specific aims: Aim 1: Demonstrate that TRIPODD can quantify targeted KI-monotherapy response. Aim 2: Establish TRIPODD protocols to accu- rately characterize KI-combinatorial therapy outcomes. Successful completion of this proposal will yield an opti- mized fluorescence imaging toolbox (TRIPODD) that will provide an unprecedented view of the spatial correla- tions between drug distribution/binding (iPAI) and the underlying tumor/microenvironment proteome (cyCIF), enabling mechanistic understanding of NSCLC treatment strategies.

项目摘要 细胞信号传导途径中激酶功能的失调与许多癌症有关。作为响应，激酶 已经开发了与这些激酶相互作用的抑制剂（KI），用于高度特异性的治疗。尽管近 50种KI已获FDA批准，KI单药治疗很少治愈，可能是由于肿瘤异质性， 获得性抵抗例如，肿瘤内异质性可导致敏感细胞亚群的治疗。 种群，同时促进抗性“持久细胞”的生长。作为回应，有效 联合治疗必须针对已知的耐药机制进行调整，以有效地与其靶点结合 利用手机的弱点然而，标准药物筛选工具（例如，血浆分析，蛋白质印迹 [WB])在性质上是散装的，没有既定的技术存在量化KI目标的参与，伴随 局部蛋白表达，同时评估肿瘤反应异质性。为了解决这些缺点， 我们的小组已经（1）开发了荧光标记KI（和其他小分子治疗剂）的方案， 模拟天然药物，（2）提出了一种新的细胞内配对试剂成像（iPAI）平台，以定量药物 目标可用性（DTA）与这些荧光KI，和（3）建立和验证了一个高度多路复用的IM- 利用DNA条形码抗体的免疫染色策略，实现原位循环免疫荧光（cyCIF） 显像在本提案中，我们将联合收割机这三个互补的创新组合成一个荧光成像 我们称之为TRIPODD（通过蛋白质组学和光学药物分布进行治疗反应成像）的平台 约束力）。在此，我们将使用TRIPODD来证明iPAI预测单药和联合用药的能力。 在整个肿瘤标本中，使用单克隆抗体， 细胞分辨率为了实现这一目标，iPAI将扩展到三色成像（即，双药DTA），而cyCIF将 应用于监测蛋白质组学治疗反应，以获得临床相关机制的理解。 联合治疗结果。表皮生长因子受体突变阳性（EGFRmut+）非小细胞 肺癌（NSCLC），目前缺乏治愈性治疗，将作为我们的模型系统。我们假设- 评价TRIPODD-作为第一个能够直接比较药物分布和结合（iPAI）技术 蛋白质组标记物（cyCIF）在细胞水平上的研究，将是揭示治疗的显着机制的关键 并最终实现基于以下方面的定制治疗策略优化： 预测算法这一假设将通过以下具体目标进行检验：目标1：证明 TRIPODD可以量化靶向KI单药治疗反应。目标2：建立TRIPODD协议， 比率表征KI组合治疗结果。成功完成这一提案将产生一个最佳选择- mized荧光成像工具箱（TRIPODD），将提供一个前所未有的空间视野， 药物分布/结合（iPAI）和潜在的肿瘤/微环境蛋白质组（cyCIF）之间的关系， 使人们能够对NSCLC治疗策略有更深入的了解。