An unbiased metric of antiproliferative drug effect in vitro

Harris, Leonard A.; Frick, Peter L.; Garbett, Shawn; Hardeman, Keisha N.; Paudel, B. Bishal; López, Carlos F.; Quaranta, Vito; Tyson, Darren R.

doi:10.1038/nmeth.3852

Cited by 96 publications

(147 citation statements)

References 31 publications

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“…The signs of GR values and GR max relate directly to response phenotype: positive for partial growth inhibition, zero for complete cytostasis, and negative for cell death. By contrast, E max values do not have this property as they are strongly confounded by proliferation rates 1,4 . Collecting GR values requires only modest changes in experimental approach 5,6 and calculations can be performed online (GRcalculator.org).…”

mentioning

confidence: 95%

Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics

2017

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mentioning

confidence: 95%

Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics

2017

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“…Cell death kinetics vary between lethal perturbations (Grootjans et al, 2016; Hafner et al, 2016; Harris et al, 2016; Tyson et al, 2012). However, the extent of this variability, whether different classes of lethal perturbations are associated with unique cell death kinetics, and the effect of genetic background on these properties, is not well understood.…”

Section: Resultsmentioning

confidence: 99%

“…This method therefore appears to be highly generalizable and, when combined with high-throughput microscopy, can be used to examine cell death kinetics for over a thousand different conditions in a single experiment. We envision that STACK will be highly complementary to other recently-developed kinetic approaches to measuring cell viability (Hafner et al, 2016; Harris et al, 2016; Tyson et al, 2012). …”

Section: Discussionmentioning

confidence: 99%

“…For reasons that remain poorly understood, cell death kinetics vary considerably between lethal stimuli, cell lines and cell death pathways, as well as between individual cells within the same population (Bernheim et al, 1977; Biton and Ashkenazi, 2011; Dixon et al, 2012; Lu et al, 2014; Shimizu et al, 2004; Spencer et al, 2009; Vanden Berghe et al, 2010). A major goal of the present work was to develop means to quantify this variability in cell death kinetics at the population level, as this knowledge may enhance our understanding of different lethal pathways, improve the classification of lethal perturbations and help identify new drugs that act through unique mechanisms (Grootjans et al, 2016; Hafner et al, 2016; Harris et al, 2016; Palchaudhuri et al, 2015; Tyson et al, 2012). …”

Section: Introductionmentioning

confidence: 99%

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Systematic Quantification of Population Cell Death Kinetics in Mammalian Cells

et al. 2017

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Summary Cytotoxic compounds are important drugs and research tools. Here, we introduce a method, Scalable Time-lapse Analysis of Cell death Kinetics (STACK), to quantify the kinetics of compound-induced cell death in mammalian cells at the population level. STACK uses live and dead cell markers, high-throughput time-lapse imaging, and mathematical modeling to determine the kinetics of population cell death over time. We used STACK to profile the effects of 1,819 bioactive compounds on cell death in two human cancer cell lines, resulting in a large and freely dataset [doi:10.17632/3pnv5wh5jm.2]. 79 potent lethal compounds common to both cell lines caused cell death with widely divergent kinetics. Thirteen compounds triggered cell death within hours, including the metallophore zinc pyrithione (ZP). Mechanistic studies demonstrated that this rapid onset lethal phenotype was caused in human cancer cells by metabolic disruption and ATP depletion. These results provide the first comprehensive survey of cell death kinetics and analysis of rapid onset lethal compounds.

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“…There are other ways to analyze drug response assays using time-lapse microscopy (Harris et al, 2016; Tyson et al, 2012), however, we have not tested these methods.…”

Section: Basic Protocol 2 Time-dependent Measurement Of Drug Sensitimentioning

confidence: 99%

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells

Niepel

Hafner

Chung

et al. 2017

CP Chemical Biology

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Measuring the potencies of small molecule drugs in cell lines is a critical aspect of preclinical pharmacology. Such experiments are also prototypical of high-throughput experimentation in multi-well plates. The procedure is simple in principle, but many unrecognized factors can affect the results, potentially making data unreliable. The procedures for measuring drug response described here were developed by the NIH LINCS program (http://www.lincsproject.org/) and improve reproducibility. Key features include maximizing uniform cell growth during the assay period, accounting for the effects of cell density on response, and correcting sensitivity measures for differences in proliferation rates. Two related protocols are described: one involves an endpoint measure well suited to large-scale studies and the second a time-dependent measurement that reveals changes in response over time. The methods can be adapted to other types of plate-based experiments. This protocol has a companion in this issue, focused on experimental design and data analysis.

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An unbiased metric of antiproliferative drug effect in vitro

Cited by 96 publications

References 31 publications

Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics

Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics

Systematic Quantification of Population Cell Death Kinetics in Mammalian Cells

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells

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