Maximizing the predictive power of high-throughput, microscopy-based phenotypic screens

项目总结 我们目前拥有前所未有的能力来描述癌症中发生的基因和途径水平的变化。然而，临床医生缺乏多样化的药物来更有效地治疗亚群患者，减少副作用，并在出现耐药性时提供二线治疗。目前迫切需要大幅增加抗癌药物的储备。 自动显微镜和计算机视觉的进步使表型图谱在早期药物发现中得到广泛应用。在这笔赠款中，我们解决了两个挑战。首先，表型分析的力量导致了越来越多的大型、不同的图像数据集。在目标1中，我们将开发结合不同数据集的机器学习方法，以获得对未表征的复合函数的准确预测。其次，表型筛选可以识别多个不同途径的候选化合物，但通常只使用单一的癌细胞株。在目标2中，我们将制定策略，以确定最小的细胞系集合，最大限度地检测小分子活动。 成功的执行将：通过利用现有的表型筛选数据集和提供一种合理的方法来选择最大限度地在所需途径中发现命中的机会的细胞系，来增加表型分析的能力。

PROJECT SUMMARY We currently have an unprecedented ability to profile the genetic- and pathway-level changes that occur in cancer. Yet, clinicians lack the diverse arsenal of drugs needed to treat subpopulations of patients more effectively, reduce side effects and offer second-line treatment when drug resistance emerges. There is a pressing need to dramatically increase the repertoire of drugs available to fight cancer. Advances in automated microscopy and computer vision, allow the widespread use of phenotypic profiling in early drug discovery. In this grant, we address two challenges. First, the power of phenotypic profiling has led to a growing number of large, disparate image datasets. In aim 1, we will develop machine-learning approaches that combine disparate datasets to obtain accurate predictions of uncharacterized compound function. Second, phenotypic screens can identify candidate compounds across multiple, diverse pathways, but often only use a single cancer cell line. In aim 2, we will develop strategies to identify minimal collections of cell lines that maximize detection of small molecule activities. Successful execution will: increase the power of phenotypic profiling by harnessing existing phenotypic screening datasets and providing a rational approach for selecting cell lines that maximize the chance of discovering hits in desired pathways.