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

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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.