Biosensor Assay to Screen for Signaling Pathway Inhibition in Cancer

描述（由申请人提供）：激酶抑制剂创造了化疗的新范例，是新肿瘤药物开发的主要焦点，但开发成功率很低（5-10%）。对激酶抑制剂的耐药性通过靶标依赖性机制（例如，取消药物结合的点突变）和靶标非依赖性机制（例如，被称为“激酶组重编程”的替代信号通路的上调）发生。因此，一次针对几种激酶的抑制剂组合是可取的，以有更好的机会避免耐药/复发周期。检测活细胞内的蛋白激酶活性（而不是在裂解物或重组系统中）对于了解激酶抑制剂的药物敏感性和耐药机制非常重要，并且可以更好地筛选可能通过开发过程的抑制剂。我们将开发对抑制剂反应和激酶组重编程很重要的特定激酶活性的多重细胞检测，并使用它们来检测患者细胞中的激酶激活谱和抑制剂筛选。使用时间分辨镧系发光测量来检测生物传感器的磷酸化，其中通过小分子荧光团直接测量Tb3+发射能量，根据荧光团给出不同的发射颜色。在Aim 1中，我们将建立药物敏感和耐药CML细胞中关键激酶活性（以及抑制）的定量分析，分析治疗相关细胞状态下通路激活表型。我们将使用我们在前期工作中已经建立的一套生物传感器，并在开发其他激酶时增加生物传感器。将使用来自CML患者的细胞系和样本（与健康对照进行比较）建立检测，并使用RT-qPCR和SWATH“蛋白质组学”进行验证。在Aim 2中，我们将筛选现有激酶抑制剂药物和新化合物之间的协同作用（通过文库）。我们还将利用从镧系元素到有机染料的能量转移，开发生物传感器磷酸化的均匀复用分析。在目标3中，我们扩展了生物传感器设计管道，以开发新的非天然肽底物，用作其他激酶的生物传感器。在这个目标中描述的工作将增加我们已经可用的生物传感器集。完成本提案中描述的工作将为我们提供一种新的多种激酶检测方法，一套新的生物传感器以及用于筛选的新的和精细的检测策略。药物发现将受益于这项技术的能力，即通过一次靶向几种激酶来解决激酶组重编程机制。药物开发将受益于针对多种靶点的伴随试验，这些试验可以跟踪一种药物或药物组合，通过先导过渡、靶点验证、临床前研究、临床试验，甚至进入治疗管理。通过在药物筛选和开发过程中对疾病环境进行建模，该检测及其相关工具将为下一代癌症靶向治疗的发展做出贡献。

DESCRIPTION (provided by applicant): Kinase inhibitors created a new paradigm in chemotherapy and are a major focus of new oncology drug development, but developmental success rates are low (5-10%). Resistance to kinase inhibitors occurs through target-dependent mechanisms (e.g. point mutations that abrogate drug binding) and target-independent mechanisms (e.g. upregulation of alternative signaling pathways, termed "kinome reprogramming"). Therefore, combinations of inhibitors that target several kinases at once are desirable to have a better chance of avoiding the resistance/relapse cycle. Detecting protein kinase activity inside living cells (rather than in lysates or reconstituted systems) is important or understanding kinase inhibitor drug sensitivity and resistance mechanisms, and would lead to better screening for inhibitors likely to make it through the development process. We will develop multiplexed, cell-based assays for specific kinase activities that are important to inhibitor response and kinome reprogramming, and use them to detect kinase activation profiles in patient cells and for inhibitor screens. Phosphorylation of the biosensors is detected using time-resolved lanthanide luminescence measurements, in which Tb3+ emission energy is measured directly via small molecule fluorophores to give different emission colors depending on the fluorophore. In Aim 1, we will establish quantitative assays for activity (and therefore inhibition) of key kinases in drug sensitive and drug resistant CML cells, profiling the pathway activation phenotypes in therapeutically relevant cellular states. We will use the set of biosensors we have already established in preliminary work, and add biosensors for other kinases as they are developed. The assays will be established with cell lines and samples from CML patients (comparing to healthy controls), and validated with RT-qPCR and SWATH" proteomics. In Aim 2, we will screen for synergies between existing kinase inhibitor drugs and new compounds (via libraries). We will also develop homogenous multiplexed analysis of biosensor phosphorylation using energy transfer from lanthanides to organic dyes. In Aim 3, we expand the biosensor design pipeline to develop new, non-natural peptide substrates to use as biosensors for other kinases. The work described in this aim will add to the set of biosensors we already have available. Completion of the work described in this proposal will give us a novel assay for multiple kinases, a suite of new biosensors as well as new and refined detection strategies to use in screening. Drug discovery will benefit from this technology's ability to address kinome reprogramming mechanisms by targeting several kinases at a time. Drug development will benefit from companion assays for multiple targets that could follow a drug or drug combination through the hit to lead transition, target validation, pre-clinical studies, clinial trials, and beyond into treatment management. This assay and its associated tools will contribute to the next generation of targeted therapy development in cancer by breaking new ground in our ability to model the disease environment during drug screening and development.