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

Marc HafnerLaura M HeiserElizabeth H WilliamsMario NiepelNicholas J WangJames E KorkolaJoe W GrayPeter K Sorger
Published in: Scientific data (2017)
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 (GR50) and efficacy (GRmax) that are analogous to the more familiar IC50 and Emax 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/.
Keyphrases
  • drug induced
  • adverse drug
  • stem cells
  • electronic health record
  • magnetic resonance imaging
  • emergency department
  • signaling pathway
  • big data
  • cell therapy
  • deep learning
  • magnetic resonance
  • diffusion weighted imaging