A scalable image-based approach for profiling and annotating very large compound

描述(申请人提供)：迫切需要大幅增加可用于抗癌的药物：许多药物具有严重的副作用，抗药性可能会迅速出现。为了使我们目前选择的化疗药物多样化，一个有效的方法是识别那些在细胞中显示出与已证实的药物相似的效果，但针对不同的途径成分或作用机制的化合物。大型复合库正变得越来越容易获得，并成为此类搜索的起点。然而，这些图书馆中的绝大多数化学物质的生物活性是完全未知的。目前，整个化合物文库需要重新筛选，以完成看似简单的任务，即查询更多具有类似生物功能的药物--这一过程昂贵、耗时且效率低下。高维表型筛选非常适合于表征跨越多个途径和遗传背景的化合物的系统级反应。然而，转录组学或蛋白质组学等方法过于昂贵和耗时，无法常规地扩展到包含数十万种化合物的库中。用预测的生物学功能注释化合物文库的一种有效方法是使用基于显微镜的细胞学图谱，这是我们实验室在过去十年中开创的一种量化细胞对扰动的反应的方法。通常，只需要一个屏幕就可以获得可用于预测跨多个功能类别的作用机制的配置文件。尽管这种方法显示出了希望，但由于试剂的高昂成本以及不确定哪些细胞读数将最好地区分广泛的生物功能类别，它的使用一直局限于小型化合物文库。在目标1中，我们克服了目前的限制，开发了一种可扩展的、具有成本效益的方法来识别对多种已证实的抗癌药物具有类似反应的化合物。在目标2中，我们在一个中型化合物库上校准和测试了我们的方法。在目标3中，我们注释了包含数十万种化学物质的大型化合物库，在多种已证明的药物类别中识别高价值的治疗前线索，并寻找具有全新作用机制的化合物。我们的目标将为加快癌症药物发现的步伐提供一个新的范例。

DESCRIPTION (provided by applicant): There is a pressing need to dramatically increase the repertoire of drugs available to fight cancer: many drugs have severe side effects and drug resistance can rapidly emerge. An effective approach for diversifying our current selection of chemotherapeutic agents is to identify compounds that show similar effects to proven drugs in cells, but target different pathway components or mechanisms of action. Large compound libraries are becoming increasingly available and serve as starting points for such searches. However, biological activities are completely unknown for the vast majority of chemicals in these libraries. Currently, entire compound libraries need to be re-screened for the seemingly simple task of querying for more drugs with similar biological function-a process that is costly, time consuming and inefficient. High-dimensional phenotypic screens are well suited to characterize systems-level responses to compounds across multiple pathways and genetic backgrounds. However, approaches such as transcriptomics or proteomics are far too expensive and time consuming to be scaled routinely to libraries with hundreds of thousands of compounds. A powerful method for annotating compound libraries with predicted biological function is the use of microscopy-based cytological profiles, an approach for quantifying cellular responses to perturbations that our lab has pioneered over the past decade. Often, only a single screen is necessary to obtain profiles that can be used to predict mechanisms of action across multiple functional categories. Although this approach shows promise, its use has been limited to small compound libraries due to the high cost of reagents and uncertainty about which cellular readouts would best allow broad classes of biological function to be distinguished. In Aim 1, we overcome current limitations and develop a scalable and cost-effective approach for identifying compounds that give similar responses to multiple classes of proven cancer drugs. In Aim 2, we calibrate and test our approach on a medium-sized compound library. In Aim 3, we annotate large compound libraries containing hundreds of thousands of chemicals, identify high-value pre-therapeutic leads in multiple proven drug categories, and search for compounds with completely novel mechanisms of action. Our Aims will provide a new paradigm for accelerating the pace of cancer drug discovery.