Drug Discovery 2022: driving the next life science revolution

Image-based phenotypic profiling using Cell Painting in a 3D breast cancer spheroid model.


A Lim1; V Wong2; P Macha3; E Cromwell4; MD Matossian5; C Brock5; M Burow5; DE Egan2; W Omta2; O Sirenko1
1 Molecular Devices LLC, United States;  2 Core Life Analytics, Netherlands;  3 Molecular Devices (UK) Ltd, UK;  4 Protein Fluidics, United States;  5 Tulane University, United States


Most potential oncology drugs fail the drug development pipeline, despite having promising data for their efficacy in vitro. This further incentivize the need for identifying in vitro models that better recapitulate tumor biology. Two-dimensional (2D) cell culture remains the primary method of drug screening, despite being less physiologically relevant than three-dimensional (3D) culture. In addition, challenges commonly associated with 3D cell models, such as assay reproducibility, scalability, and cost have limited its widespread adoption as a primary screening method in drug discovery. Moreover, the scope of biological readouts from 3D models is usually restricted to a single or a handful of features that do not fully capture the biological complexity of these tumoroids.  Image-based phenotypic profiling, such as with the Cell Painting assay, is increasingly used in many applications to quantitatively capture a broad range of phenotypic changes in response to compound-induced or genetic perturbations. Here we performed a screen using patient-derived 3D spheroids (tumoroids) derived from a patient-derived tumor explant, TU-BcX-4IC, that represents metaplastic breast cancer with a triple-negative breast cancer subtype. Tumoroids were treated with 168 compounds from the NIH library of approved oncology drugs and Cell Painting was used to evaluate the associated phenotypic changes. We have additionally performed a single-feature readout from an image-based viability assay in parallel for comparison. Twenty-four hits were identified based on the phenotypic distance score that was calculated from the principal component analysis (PCA). Two-thirds of the hits overlapped with those from the image-based viability assay. Taken together, our results demonstrate the feasibility of using Cell Painting as an important approach for 3D cell model analysis.

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