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

Deep learning takes on tumours

“…Building on that effort, Carragher and his colleagues screened 14,000 compounds across 8 forms of breast cancer 5 . "We did identify some interesting hits," he says -including a compound that was already known to modulate receptors for serotonin, which is important in mammary-gland development, as they reported earlier this year 6 .…”

Phenotypic Profiling of Reference Chemicals across Biologically Diverse Cell Types Using the Cell Painting Assay

“…Well-level data corresponding to DMSO controls and the cell viability NOEC for each chemical by chemical by cell type combination were analyzed using a variation of the Theta Comparative Cell Scoring (TCCS) method described by Warchal et al 11,13 In instances when a cell viability NOEC could not be determined, the highest tested concentration was retained in the analysis. A total of n = 9 treatment wells per chemical by cell type combination and n = 72 DMSO control wells per cell line were included in the TCCS analysis.…”

“…More recently, Warchal et al have used the Cell Painting assay to screen libraries of pharmacological compounds in a set of biologically and morphologically distinct breast cancer cell lines, and have developed computational approaches to quantify and visualize the degree of dissimilarity in phenotypic response profiles throughout this cell line panel. [11][12][13] These studies were conducted in the context of personalized medicine: that is, the identification of drugs that produce distinct phenotypes in cell lines with a particular clinical subtype. Each cell line in the breast cancer cell line panel responded similarly to a majority of active chemicals tested, whereas a smaller subset of chemicals produced a dissimilar phenotype in at least one of the cell lines within the panel.…”

Image-based profiling for drug discovery: due for a machine-learning upgrade?

“…As the choice of cell line informs MOA prediction accuracy 21 , effort has been made towards engineering reporter cell lines 43 and identifying cell line-invariant features 115 . Others have interrogated genetic heterogeneity, finding distinct morphological responses to serotonin modulators across breast cancer cell lines 116 . The integration of image and transcriptome data into ensemble approaches has shown promise to improve MOA determination for synthetic small molecules, natural products and identified bioactive metabolites 21 , 117 .…”