Drug GRADE: An Integrated Analysis of Population Growth and Cell Death Reveals Drug-Specific and Cancer Subtype-Specific Response Profiles

Abstract

SUMMARY When evaluating anti-cancer drugs, two different measurements are used: relative viability, which scores an amalgam of proliferative arrest and cell death, and fractional viability, which specifically scores the degree of cell killing. We quantify relationships between drug-induced growth inhibition and cell death by counting live and dead cells using quantitative microscopy. We find that most drugs affect both proliferation and death, but in different proportions and with different relative timing. This causes a non-uniform relationship between relative and fractional response measurements. To unify these measurements, we created a data visualization and analysis platform called drug GRADE, which characterizes the degree to which death contributes to an observed drug response. GRADE captures drug- and genotype-specific responses, which are not captured using traditional pharmacometrics. This study highlights the idiosyncratic nature of drug-induced proliferative arrest and cell death. Furthermore, we provide a metric for quantitatively evaluating the relationship between these behaviors.

Human ALS/FTD brain organoid slice cultures display distinct early astrocyte and targetable neuronal pathology

“…Images were acquired every 6 h over a duration of 5 days using a ×10 magnification objective of the Incucyte S3 Live-Cell Analysis system (Sartorius, 4647). For assessing cell viability, cell confluency was determined as a cell body cluster area by phase microscopy using the IncuCyte NeuroTrack software 59 .…”

Bacterial diet modulates tamoxifen-induced death via host fatty acid metabolism

“…The GR index is calculated by the relative number of live cells over time in presence or absence of drug and integrates both cytostatic and lethal effects of a drug 21 . In contrast, FV only considers the lethal effect of a drug 22 . We first calculated the GR index and, as expected, observed a decrease in the growth rate of T-47D cells incubated with tamoxifen in a dose-dependent manner ( Fig.…”

FLICK: An optimized plate reader-based assay to infer cell death kinetics

“…For vehicle-treated wells, fluorescence measurements after endpoint permeabilization should be substantially higher than measurements taken at the end point of the assay prior to permeabilization, reflecting an expected low percentage of dead cells in the population ( Figures 4 B and 4C). Outcomes for drug-treated wells will vary depending on the concentration and mechanism of action (death, growth arrest, or a combination of death and arrest) ( Schwartz et al., 2020 ). Death-inducing drugs should cause an increase in the fluorescence signal over time and may plateau at later time points.…”

“…Importantly, because this protocol facilitates quantification of both live and dead cells, these methods can be used to quantify any commonly used drug response metric, including relative viability or the normalized growth rate inhibition value (GR) ( Figures 5 A and 5D). Drugs vary in the degree to which they activate cell death versus inhibit growth ( Schwartz et al., 2020 ). For drugs like SGI-1027, that primarily activate death without strongly inhibiting cell proliferation, FV, RV, and GR metrics will produce very similar insights ( Figures 4 B–4D).…”

“…Each of these metrics can be appropriate, depending on the focus of a study, but each metric produces unique and incomplete insights. Additionally, using data generated from this protocol, the complementary insights derived from cell death-focused or population size-focused pharmacometrics can be integrated by calculating drug GRADE ( Schwartz et al., 2020 ). GRADE analysis reveals the degree of drug-induced cell death versus drug-induced proliferative arrest induced by a given drug.…”