Supplementary Data from High-Content Phenotypic Profiling of Drug Response Signatures across Distinct Cancer Cells

Drug combination testing in the pharmaceutical industry has typically been driven by late-stage opportunistic strategies rather than by early testing to identify drug combinations for clinical investigation that may deliver improved efficacy. A rationale for combinations exists across a number of diseases in which pathway redundancy or resistance to therapeutics are evident. However, early assays are complicated by the absence of both assay formats representative of disease biology and robust infrastructure to screen drug combinations in a medium-throughput capacity. When applying drug combination testing studies, it may be difficult to translate a study design into the required well contents for assay plates because of the number of compounds and concentrations involved. Dispensing these plates increases in difficulty as the number of compounds and concentration points increase and compounds are subsequently rolled onto additional labware. We describe the development of a software tool, in conjunction with the use of acoustic droplet technology, as part of a compound management platform, which allows the design of an assay incorporating combinations of compounds. These enhancements to infrastructure facilitate the design and ordering of assay-ready compound combination plates and the processing of combinations data from high-content organotypic assays.

Phenotypic screening seeks to identify substances that modulate phenotypes in a desired manner with the aim of progressing first-in-class agents. Successful campaigns require physiological relevance, robust screening, and an ability to deconvolute perturbed pathways. High-content analysis (HCA) is increasingly used in cell biology and offers one approach to prosecution of phenotypic screens, but challenges exist in exploitation where data generated are high volume and complex. We combine development of an organotypic model with novel HCA tools to map phenotypic responses to pharmacological perturbations. We describe implementation for angiogenesis, a process that has long been a focus for therapeutic intervention but has lacked robust models that recapitulate more completely mechanisms involved. The study used human primary endothelial cells in co-culture with stromal fibroblasts to model multiple aspects of angiogenic signaling: cell interactions, proliferation, migration, and differentiation. Multiple quantitative descriptors were derived from automated microscopy using custom-designed algorithms. Data were extracted using a bespoke informatics platform that integrates processing, statistics, and feature display into a streamlined workflow for building and interrogating fingerprints. Ninety compounds were characterized, defining mode of action by phenotype. Our approach for assessing phenotypic outcomes in complex assay models is robust and capable of supporting a range of phenotypic screens at scale.

Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules' effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound's mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework.

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. Mol Cancer Ther; 9(6); 1913–26. ©2010 AACR.