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

项目总结 细胞信号通路中的激酶功能失控与多种癌症有关。作为回应，激活剂 抑制剂(KI)已经被开发出来与这些激酶相互作用，以进行高度特异性的治疗。虽然差一点 已有50例KI获得FDA批准，KI单一疗法很少治愈，可能是由于肿瘤的异质性和 后天抵抗力。例如，肿瘤内的异质性可以导致治疗敏感细胞亚群。 同时促进耐药的“持久细胞”的生长。作为回应，有效 联合疗法必须根据已知的耐药机制进行调整，以有效地与靶点接触 并利用手机的弱点。然而，标准药物筛选工具(例如，血浆分析、蛋白质印迹 [WB])本质上是散装的，并且没有既定的技术来量化KI目标参与，伴随而来的是 局部蛋白表达，同时评估肿瘤反应的异质性。为了解决这些缺点， 我们的团队已经(1)开发了荧光标记Kis(和其他小分子疗法)的方案 模拟天然药物，(2)提出了一种新的细胞内配对药物成像(IPAI)平台来定量药物 使用这些荧光KI的目标可用性(DTA)，以及(3)建立并验证了高度多元化的IM- 利用DNA条形码抗体实现原位循环免疫荧光(CyCIF)的免疫染色策略 成像。在这个方案中，我们将把这三个互补的创新结合到一个荧光成像中 我们称之为TRIPODD(通过蛋白质组学和光学药物分布的治疗反应成像)平台 和约束性)。在这里，我们将使用TRIPODD来演示IPAI预测单音和组合音的能力。 单药耐药的肿瘤标本对KI的药物反应及耐药机制 单元格分辨率。为了实现这一目标，iPai将扩展到三色成像(即两种药物的DTA)，而cyCIF将 被应用于监测蛋白质组治疗反应，以获得临床相关的机制理解 联合治疗的结果。表皮生长因子受体突变阳性(EGFRmut+)非小细胞 目前缺乏根治性治疗的肺癌(NSCLC)将作为我们的模型系统。我们假设- 将TRIPODD定为第一项能够直接比较药物分布和结合(IPAI)技术 在细胞水平上的蛋白质组标记(CyCIF)-将是揭示治疗的显著机制的关键 非小细胞肺癌的反应和耐药性，并最终实现基于以下因素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：论证 TRIPODD可以量化KI-单药治疗的靶向反应。目标2：建立TRIPODD协议，以满足- 正确描述KI-组合治疗的结果。成功完成这项提议将产生一个机会- 微型化荧光成像工具箱(TRIPODD)将提供前所未有的空间相关性视图- 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， 实现对非小细胞肺癌治疗策略的机械性理解。

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.