Bowhead: Bayesian modelling of cell velocity during concerted cell migration.

Cell migration is a central biological process that requires fine coordination of molecular events in time and space. A deregulation of the migratory phenotype is also associated with pathological conditions including cancer where cell motility has a causal role in tumor spreading and metastasis formation. Thus cell migration is of critical and strategic importance across the complex disease spectrum as well as for the basic understanding of cell phenotype. Experimental studies of the migration of cells in monolayers are often conducted with 'wound healing' assays. Analysis of these assays has traditionally relied on how the wound area changes over time. However this method does not take into account the shape of the wound. Given the many options for creating a wound healing assay and the fact that wound shape invariably changes as cells migrate this is a significant flaw. Here we present a novel software package for analyzing concerted cell velocity in wound healing assays. Our method encompasses a wound detection algorithm based on cell confluency thresholding and employs a Bayesian approach in order to estimate concerted cell velocity with an associated likelihood. We have applied this method to study the effect of siRNA knockdown on the migration of a breast cancer cell line and demonstrate that cell velocity can track wound healing independently of wound shape and provides a more robust quantification with significantly higher signal to noise ratios than conventional analyses of wound area. The software presented here will enable other researchers in any field of cell biology to quantitatively analyze and track live cell migratory processes and is therefore expected to have a significant impact on the study of cell migration, including cancer relevant processes. Installation instructions, documentation and source code can be found at http://bowhead.lindinglab.science licensed under GPLv3.

Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode states of signaling networks and unravel cellular mechanisms hidden to conventional approaches. We perform high-content screening of 17 cancer cell lines, generating more than 500 billion data points from ∼850 million cells. We analyze these data using a deep learning model, resulting in the identification of a continuous 27-dimension space describing all of the observed cell morphologies. From its morphology alone, we could thus predict whether a cell was resistant to ErbB-family drugs, with an accuracy of 74%, and predict the potential mechanism of resistance, subsequently validating the role of MET and insulin-like growth factor 1 receptor (IGF1R) as drivers of cetuximab resistance in in vitro models of lung and head/neck cancer.

Design and synthesis of novel flexible ester-containing analogs of tamoxifen and their evaluation as anticancer agents.

BACKGROUND: Tamoxifen (TAM) is metabolized to the more active 4-hydroxytamoxifen by CYP2D6 enzyme. Due to the genetic polymorphisms in CYP2D6, clinical outcomes of TAM treatment vary. Novel flexible TAM analogs with altered activation pathway were synthesized and were tested for their antiproliferative action on MCF-7 cell lines and their binding affinity for ERα and ERß. RESULTS: All compounds showed better antiproliferative activity than TAM. Compound 3 showed 80-times more ERα binding than TAM, 900-times more selectivity toward ERα. Compound 3 was tested on the entire National Cancer Institute cancerous cell lines; results indicated a broad spectrum anticancer activity. CONCLUSION: The novel analogs were more potent than TAM with higher selectivity toward ERα and with potential metabolic stability toward CYP2D6.

A new pregnenolone analogues as privileged scaffolds in inhibition of CYP17 hydroxylase enzyme. Synthesis and in silico molecular docking study.

A new series of 17-(N-(arylimino)-5-pregnen-3ß-ol derivatives 19-32 as well as carboxylate and acrylate analogues of pregnenolone 37-40 were synthesized and evaluated for their inhibitory activity against human CYP17 hydroxylase expressed in Escherichia coli. Compounds 32 and 37 were the most potent analogues in this series, showing inhibition activity with IC50 = 2.11 and 1.29 µM, respectively. However, the analogue 37 revealed a better selectivity profile (83.21% inhibition of hydroxylase), which is a leading candidate for further development. Molecular docking study of 37 showed binding with the amino acid residues of CYP17 through hydrogen bonds and hydrophobic interaction.