High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways

Abstract

Heterogeneity in disease mechanisms between genetically distinct patients contributes to high attrition rates in late stage clinical drug development. New personalized medicine strategies aim to identify predictive biomarkers which stratify patients most likely to respond to a particular therapy. However, for complex multifactorial diseases not characterized by a single genetic driver, empirical approaches to identifying predictive biomarkers and the most promising therapies for personalized medicine are required. In vitro pharmacogenomics seeks to correlate in vitro drug sensitivity testing across panels of genetically distinct cell models with genomic, gene expression or proteomic data to identify predictive biomarkers of drug response. However, the vast majority of in vitro pharmacogenomic studies performed to date are limited to dose-response screening upon a single viability assay endpoint. In this article we describe the application of multiparametric high content phenotypic screening and the theta comparative cell scoring method to quantify and rank compound hits, screened at a single concentration, which induce a broad variety of divergent phenotypic responses between distinct breast cancer cell lines. High content screening followed by transcriptomic pathway analysis identified serotonin receptor modulators which display selective activity upon breast cancer cell cycle and cytokine signaling pathways correlating with inhibition of cell growth and survival. These methods describe a new evidence-led approach to rapidly identify compounds which display distinct response between different cell types. The results presented also warrant further investigation of the selective activity of serotonin receptor modulators upon breast cancer cell growth and survival as a potential drug repurposing opportunity.

Keywords: Breast cancer; Cell Painting; High content imaging; Pathway analysis; Pharmacogenomics; Phenotypic screening; Serotonin; Triflupromazine.

Graphical abstract

Fig. 1

Summary of phenotypic screen. (A) Schematic of screening strategy. (B) Principal component analysis (PCA) of phenotypic screen. Data points color coded: drug treatment, positive control (300 nM staurosporine) or negative control (0.1% DMSO). (C) PCA analysis of phenotypic screen color coded by cell line. Multivariate Z’-factor analysis between negative (0.1% DMSO) and positive (0.3 μM staurosporine) controls by cell line is indicated in brackets.

Fig. 2

Chemical structures of hit compounds that are phenotypically different between cell line pairs.

Fig. 3

Concentration-response curves for 12 hits from the Prestwick Chemical Library. Compounds were used in a 2D cell proliferation assay measuring cell count expressed as the percentage of the DMSO control after 72 h. Note: compounds were originally screened at a concentration of 1 μM using the Cell Painting assay.

Fig. 4

Effects of protriptyline and triflupromazine using nuclei/cell counting and viability assays in T47D and HCC1954 cells. (A) Cell lines treated with protriptyline or triflupromazine for 48 h. Scale bar is 100 μm. (B) Quantification of nuclear counts (top) after 48 h and cell viability (bottom) after 72 h of compound treatment. n = 3, mean ± SEM is shown. STS, staurosporine (300 nM). (C) Table of EC50 values for growth inhibition.

Fig. 5

Gene expression analysis of compound treatments in T47D and HCC1954 cell lines. (A) Heatmap of gene expression following 24 h treatment with compound. (B) Differential gene expression analysis of gene changes following triflupromazine treatment for 24 h in the T47D cell line. (C) Differential gene expression analysis of gene changes following protriptyline treatment for 24 h in the T47D cell line. For (B) and (C), genes significantly altered (p < 0.05, Benjamini–Yekutieli-corrected test) are highlighted; up-regulated (red circles) or down-regulated (blue circles). Gene names are displayed for genes with log2(fold change) greater than 3 or less than -3.

Fig. 6

Interaction network analysis of differentially expressed genes in triflupromazine treated T47D cells. (A) Network of cell cycle related genes. (B) Network of TNFR1 signalling genes.