Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics suffer from a fundamental flaw when applied to growing cells: they are highly sensitive to the number of divisions that take place over the course of a response assay. Division rate varies with cell line, experimental conditions, and genetic alterations. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring important biological insights and interfering with biomarker discovery. In this work, we derive alternative drug response metrics that are insensitive to number of divisions occurring during the assay. These are based on estimating growth rate inhibition (GR) in the presence of a drug using endpoint or time-course assays. The latter provides a direct measure of phenomena such as adaptive drug resistance.

Using a simple model of drug response, we first show how GR50 and GRmax are superior to IC50 and Emax for assessing the effects of drugs in dividing cells. By expressing an oncogene in a transformed cell line, we illustrate how conventional metrics can lead to artefactual connections between mutations and drug sensitivity. We further validate the superiority of GR50 over IC50 values by reanalyzing a recently published large dataset of drug sensitivity and showing cases where difference in division rates is the only reason why IC50 values correlate with tissue type or genetic alterations. Using GR50 values prevents these artificial correlations and restores known connections between drug resistance and genomic markers. Finally, we show how GRmax values, which reflect efficacy, quantify differences in the phenotypic response and thus can be used to identify new biomarkers of sensitivity.

Adopting GR metrics requires only modest changes in experimental protocols. GR values and metrics can be evaluated using scripts are available on github (www.github.com/sorgerlab/gr50_tools) or using an interactive website: www.grcalculator.org. We expect GR metrics to improve the use of drugs to identify response biomarkers, study mechanisms of cell signaling and growth, and identify drugs effective on specific patient-derived tumor cells.Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics suffer from a fundamental flaw when applied to growing cells: they are highly sensitive to the number of divisions that take place over the course of a response assay. Division rate varies with cell line, experimental conditions, and genetic alterations. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring important biological insights and interfering with biomarker discovery. In this work, we derive alternative drug response metrics that are insensitive to number of divisions occurring during the assay. These are based on estimating growth rate inhibition (GR) in the presence of a drug using endpoint or time-course assays. The latter provides a direct measure of phenomena such as adaptive drug resistance.

Using a simple model of drug response, we first show how GR50 and GRmax are superior to IC50 and Emax for assessing the effects of drugs in dividing cells. By expressing an oncogene in a transformed cell line, we illustrate how conventional metrics can lead to artefactual connections between mutations and drug sensitivity. We further validate the superiority of GR50 over IC50 values by reanalyzing a recently published large dataset of drug sensitivity and showing cases where difference in division rates is the only reason why IC50 values correlate with tissue type or genetic alterations. Using GR50 values prevents these artificial correlations and restores known connections between drug resistance and genomic markers. Finally, we show how GRmax values, which reflect efficacy, quantify differences in the phenotypic response and thus can be used to identify new biomarkers of sensitivity.

Adopting GR metrics requires only modest changes in experimental protocols. GR values and metrics can be evaluated using scripts are available on github (www.github.com/sorgerlab/gr50_tools) or using an interactive website: www.grcalculator.org. We expect GR metrics to improve the use of drugs to identify response biomarkers, study mechanisms of cell signaling and growth, and identify drugs effective on specific patient-derived tumor cells.

Citation Format: Hafner M, Niepel M, Sorger PK. Metrics of drug sensitivity based on growth rate inhibition correct for the confounding effects of variable division rates [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-07-33.