Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics

Marc Hafner, Laura M. Heiser, Elizabeth H. Williams, Mario Niepel, Nicholas J. Wang, James E. Korkola, Joe W. Gray, Peter K. Sorger

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture conditions. This problem can be overcome by computing drug sensitivity on a per-division basis. The normalized growth rate inhibition (GR) approach yields per-division metrics for drug potency (GR 50) and efficacy (GR max) that are analogous to the more familiar IC 50 and E max values. In this work, we report GR-based, proliferation-corrected, drug sensitivity metrics for ∼4,700 pairs of breast cancer cell lines and perturbagens. Such data are broadly useful in understanding the molecular basis of therapeutic response and resistance. Here, we use them to investigate the relationship between different measures of drug sensitivity and conclude that drug potency and efficacy exhibit high variation that is only weakly correlated. To facilitate further use of these data, computed GR curves and metrics can be browsed interactively at http://www.GRbrowser.org/.

Original languageEnglish (US)
Article number170166
JournalScientific Data
StatePublished - Nov 7 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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