This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 16 October 2023
Nature Communications Open Access 14 November 2022
Optimization of cell viability assays to improve replicability and reproducibility of cancer drug sensitivity screens
Scientific Reports Open Access 02 April 2020
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Hafner, M., Niepel, M., Chung, M. & Sorger, P.K. Nat. Methods 13, 521–527 (2016).
Haibe-Kains, B. et al. Nature 504, 389–393 (2013).
CCLE Consortium & GDSC Consortium. Nature 528, 84–87 (2015).
Harris, L.A. et al. Nat. Methods 13, 497–500 (2016).
Niepel, M., Hafner, M., Chung, M. & Sorger, P.K. Curr. Protoc. Chem. Biol. 9, 55–74 (2017).
Hafner, M., Niepel, M., Subramanian, K. & Sorger, P.K. Curr. Protoc. Chem. Biol. 9, 96–116 (2017).
Haverty, P.M. et al. Nature 533, 333–337 (2016).
Im, H.K. et al. PLoS Genet. 8, e1002525 (2012).
Choy, E. et al. PLoS Genet. 4, e1000287 (2008).
Eichhorn, P.J. et al. Cancer Res. 68, 9221–9230 (2008).
Rozenblatt-Rosen, O. et al. Mol. Cell. Biol. 25, 612–620 (2005).
Fallahi-Sichani, M., Honarnejad, S., Heiser, L.M., Gray, J.W. & Sorger, P.K. Nat. Chem. Biol. 9, 708–714 (2013).
Letai, A. Clin. Cancer Res. 21, 5015–5020 (2015).
Fisher, R.I. et al. N. Engl. J. Med. 328, 1002–1006 (1993).
Oakes, S.R. et al. Proc. Natl. Acad. Sci. USA 109, 2766–2771 (2012).
Seashore-Ludlow, B. et al. Cancer Discov. 5, 1210–1223 (2015).
Johnson, J.I. et al. Br. J. Cancer 84, 1424–1431 (2001).
The authors declare no competing financial interests.
Integrated supplementary information
(a) Simulation of responses to two drugs inducing either partial growth inhibition (blue) or cell death (green) in slow- and fast-growing cell lines for a 72 h assay. Colored lines correspond to the cell count over time for the maximal drug effect; black lines correspond to untreated control samples; x0 is the cell count at the time of treatment; xctrl and x(c) are the cell counts in the untreated and treated samples at the end of treatment. End-point ratios (x(c)/xctrl) are reported. (b) Simulation of the effects of a hypothetical mutation on cell division time (by row) and drug sensitivity (by column) in comparison to a reference response (center plot in gray). The untreated population is shown in black and the treated population in color based on the type of response (partial growth inhibition in blue, cytostasis in red, or cell death in green). End-point relative cell counts are reported. (c) Conventional (top) and GR-based (bottom) drug dose-response curves corresponding to the conditions in (b). Equations for each method is based on the values defined in (a). Note that the GR curves for each type of response overlap. (d) Conventional and GR-based sensitivity metrics corresponding to the dose-response curves in (c).
Each panel shows GRmax plotted against GR50 values across all cell lines for a given drug. For illustration purposes, GR50 values are capped at 31 μM.
(a) Entropies for GR50 and GR2μM for each drug in the CTRP dataset. Size of the circle reflects the mutual information between GR50 and GR2μM. (b) Joint entropy of GR50 and GR2μM (H50+2μM) metrics plotted against the entropy of the GRAOC value (HAOC). (c) Distribution of entropies for GR50 (H50), GR2μM (H2μM), GRAOC (HAOC), and joint entropy of GR50 and GR2μM (H50+2μM). Red line marks the median value. (d) Each panel shows GR2μM plotted against GR50 values across all cell lines for drugs or drug combinations with the highest joint entropy of GR50 and GR2μM (H50+2μM). For illustration purposes GR50 values are capped at 31 μM.
(a) Spearman's correlation values between the two datasets based on conventional AUC (x-axis) or GRAOC (y-axis) for the nine drugs shared between the datasets. Mean increase in correlation and p-value of a t-test are reported. Note that gemcitabine (blue cross) is not included in the statistics because it was used for calibration. (b) Each pair of panels shows the AUC (left) or GRAOC (right) values calculated on the CTRP dataset versus the values on the gCSI dataset for all cell lines for each drug shared between the datasets. Each pair of plots is a different drug; Spearman's correlation is displayed at the top of each plot.
About this article
Cite this article
Hafner, M., Niepel, M. & Sorger, P. Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics. Nat Biotechnol 35, 500–502 (2017). https://doi.org/10.1038/nbt.3882
This article is cited by
Nature Communications (2023)
Nature Communications (2022)
Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms
Nature Methods (2021)
Nature Reviews Drug Discovery (2021)
Scientific Reports (2020)