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.
In this proposal, we will develop computational and experimental methods to maximize the power of high-throughput, microscopy-based phenotypic screens. Our studies will increase the power of phenotypic profiling by harnessing screens performed across the world, providing a rational approach for maximizing the chance of discovering hits for diverse pathways in future screens, and identifying novel compounds that expand our ability to perturb cancer-relevant pathways. Our proposal is designed to accelerate the pace of early drug discovery.
