A61
Phenotypic profiling: multiparametric high-content analysis across distinct cancer types
Neil Carragher, Peter Caie, Alexandra Ingleston-Orme, Sandeep Daya, Rob Eagle, Rebecca Walls, Tom Houslay, Andy Hargreaves
AstraZeneca, Loughborough, Leicestershire, UK
Background
Traditional drug discovery strategies typically employ simplistic assays that monitor the activity of a single target or enzymatic pathway. While these approaches are amenable to high-throughput screening, they provide limited information on how therapeutics influence complex diseases such as cancer. Such limitations are a contributing factor to high attrition rates at later stages in the drug discovery process.
Method
We report the development of a suite of cell based “high-content” assays and associated image analysis algorithms designed to generate as much information as possible on how test therapeutics influence the entire cellular phenotype. The multiparametric data obtained from these assays was compiled to generate a non-biased “phenotypic fingerprint” for each tested compound. Subsequently, the fingerprint from test compounds are cross-referenced with that of a training set of compounds of known mechanism of action including standard chemotherapeutic agents.
Results
Live-cell kinetic profiling was initially applied to define appropriate time-points for high-content phenotypic analysis. Subsequent multiparametric high-content analysis identified compounds active upon cell-cycle and cell morphology with further differentiation of cell-cycle mechanism and distinct morphological effects. This approach has been applied to a panel of physiologically relevant cancer cell types to enable simultaneous assessment of; mechanism of action (including off-target activity), evaluation of cellular toxicity and identification of resistant and sensitive cancer cell types.
Conclusion
Our results demonstrate multiparametric high-content cell assays and multivariate statistical analysis can be used to further define therapeutic mechanism of action and facilitate selection of compounds or compound combinations that target multiple pathways implicated in cancer.
