High-content phenotypic profiling of drug response signatures across distinct cancer cells

Abstract

The application of high-content imaging in conjunction with multivariate clustering techniques has recently shown value in the confirmation of cellular activity and further characterization of drug mode of action following pharmacologic perturbation. However, such practical examples of phenotypic profiling of drug response published to date have largely been restricted to cell lines and phenotypic response markers that are amenable to basic cellular imaging. As such, these approaches preclude the analysis of both complex heterogeneous phenotypic responses and subtle changes in cell morphology across physiologically relevant cell panels. Here, we describe the application of a cell-based assay and custom designed image analysis algorithms designed to monitor morphologic phenotypic response in detail across distinct cancer cell types. We further describe the integration of these methods with automated data analysis workflows incorporating principal component analysis, Kohonen neural networking, and kNN classification to enable rapid and robust interrogation of such data sets. We show the utility of these approaches by providing novel insight into pharmacologic response across four cancer cell types, Ovcar3, MiaPaCa2, and MCF7 cells wild-type and mutant for p53. These methods have the potential to drive the development of a new generation of novel therapeutic classes encompassing pharmacologic compositions or polypharmacology in appropriate disease context.