Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity, while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative small molecule drug-response metrics that are insensitive to division number. These are based on estimation of the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of small molecule drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using small molecules and biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs effective against specific patient-derived tumor cells.
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Gene Expression Omnibus
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Garnett, M.J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).
Heiser, L.M. et al. Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc. Natl. Acad. Sci. USA 109, 2724–2729 (2012).
Schenone, M., Dancč ík, V., Wagner, B.K. & Clemons, P.A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232–240 (2013).
Cravatt, B.F. & Gottesfeld, J.M. Chemical biology meets biological chemistry minireview series. J. Biol. Chem. 285, 11031–11032 (2010).
Fallahi-Sichani, M., Honarnejad, S., Heiser, L.M., Gray, J.W. & Sorger, P.K. Metrics other than potency reveal systematic variation in responses to cancer drugs. Nat. Chem. Biol. 9, 708–714 (2013).
Sebaugh, J.L. Guidelines for accurate EC50/IC50 estimation. Pharm. Stat. 10, 128–134 (2011).
Rees, M.G. et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol. 12, 109–116 (2016).
Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 5, 1210–1223 (2015).
Errington, T.M. et al. An open investigation of the reproducibility of cancer biology research. eLife 3, e04333 (2014).
Haibe-Kains, B. et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013).
Safikhani, Z. et al. Revisiting inconsistency in large pharmacogenomic studies. Preprint at http://dx.doi.org/10.1101/026153 (2015).
The Cancer Cell Line Encyclopedia Consortium & the Genomics of Drug Sensitivity in Cancer Consortium. Pharmacogenomic agreement between two cancer cell line data sets. Nature 528, 84–87 (2015).
O'Connor, P.M. et al. Characterization of the p53 tumor suppressor pathway in cell lines of the National Cancer Institute anticancer drug screen and correlations with the growth-inhibitory potency of 123 anticancer agents. Cancer Res. 57, 4285–4300 (1997).
Serrano, M., Lin, A.W., McCurrach, M.E., Beach, D. & Lowe, S.W. Oncogenic ras provokes premature cell senescence associated with accumulation of p53 and p16INK4a. Cell 88, 593–602 (1997).
Michaloglou, C. et al. BRAFE600-associated senescence-like cell cycle arrest of human naevi. Nature 436, 720–724 (2005).
Muranen, T. et al. Inhibition of PI3K/mTOR leads to adaptive resistance in matrix-attached cancer cells. Cancer Cell 21, 227–239 (2012).
Chabner, B.A., Allegra, C.J., Curt, G.A. & Calabresi, P. in The Pharmacological Basis of Therapeutics 9th edn. (eds. Hardman, J. & Limbird, L.) Ch. 51 (McGraw-Hill, 1996).
Baguley, B.C. et al. Resistance mechanisms determining the in vitro sensitivity to paclitaxel of tumour cells cultured from patients with ovarian cancer. Eur. J. Cancer 31A, 230–237 (1995).
Caplow, M., Shanks, J. & Ruhlen, R. How taxol modulates microtubule disassembly. J. Biol. Chem. 269, 23399–23402 (1994).
Rouzier, R. et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin. Cancer Res. 11, 5678–5685 (2005).
Shi, J., Orth, J.D. & Mitchison, T. Cell type variation in responses to antimitotic drugs that target microtubules and kinesin-5. Cancer Res. 68, 3269–3276 (2008).
Gascoigne, K.E. & Taylor, S.S. Cancer cells display profound intra- and interline variation following prolonged exposure to antimitotic drugs. Cancer Cell 14, 111–122 (2008).
Konecny, G.E. et al. Activity of the dual kinase inhibitor lapatinib (GW572016) against HER-2-overexpressing and trastuzumab-treated breast cancer cells. Cancer Res. 66, 1630–1639 (2006).
Moasser, M.M., Basso, A., Averbuch, S.D. & Rosen, N. The tyrosine kinase inhibitor ZD1839 (″Iressa″) inhibits HER2-driven signaling and suppresses the growth of HER2-overexpressing tumor cells. Cancer Res. 61, 7184–7188 (2001).
Chauffert, B. et al. New insights into the kinetic resistance to anticancer agents. Cytotechnology 27, 225–235 (1998).
Dimanche-Boitrel, M.T., Garrido, C. & Chauffert, B. Kinetic resistance to anticancer agents. Cytotechnology 12, 347–356 (1993).
Garrido, C. et al. Circumvention of confluence-dependent resistance in a human multi-drug-resistant colon-cancer cell line. Int. J. Cancer 61, 873–879 (1995).
Fang, Y., Sullivan, R. & Graham, C.H. Confluence-dependent resistance to doxorubicin in human MDA-MB-231 breast carcinoma cells requires hypoxia-inducible factor-1 activity. Exp. Cell Res. 313, 867–877 (2007).
Sorby, M. & Ostman, A. Protein-tyrosine phosphatase-mediated decrease of epidermal growth factor and platelet-derived growth factor receptor tyrosine phosphorylation in high cell density cultures. J. Biol. Chem. 271, 10963–10966 (1996).
Kim, J.H., Kushiro, K., Graham, N.A. & Asthagiri, A.R. Tunable interplay between epidermal growth factor and cell-cell contact governs the spatial dynamics of epithelial growth. Proc. Natl. Acad. Sci. USA 106, 11149–11153 (2009).
Curto, M., Cole, B.K., Lallemand, D., Liu, C.H. & McClatchey, A.I. Contact-dependent inhibition of EGFR signaling by Nf2/Merlin. J. Cell Biol. 177, 893–903 (2007).
Kaplan, P.L., Anderson, M. & Ozanne, B. Transforming growth factor(s) production enables cells to grow in the absence of serum: an autocrine system. Proc. Natl. Acad. Sci. USA 79, 485–489 (1982).
Sero, J.E. et al. Cell shape and the microenvironment regulate nuclear translocation of NF-κB in breast epithelial and tumor cells. Mol. Syst. Biol. 11, 790 (2015).
Orth, J.D. et al. Analysis of mitosis and antimitotic drug responses in tumors by in vivo microscopy and single-cell pharmacodynamics. Cancer Res. 71, 4608–4616 (2011).
Yuan, H. et al. Use of reprogrammed cells to identify therapy for respiratory papillomatosis. N. Engl. J. Med. 367, 1220–1227 (2012).
Crystal, A.S. et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 346, 1480–1486 (2014).
Nam, H.S. & Benezra, R. High levels of Id1 expression define B1 type adult neural stem cells. Cell Stem Cell 5, 515–526 (2009).
Hockemeyer, D. et al. Efficient targeting of expressed and silent genes in human ESCs and iPSCs using zinc-finger nucleases. Nat. Biotechnol. 27, 851–857 (2009).
Boehm, J.S. et al. Integrative genomic approaches identify IKBKE as a breast cancer oncogene. Cell 129, 1065–1079 (2007).
Soumillon, M., Cacchiarelli, D., Semrau, S., van Oudenaarden, A. & Mikkelsen, T.S. Characterization of directed differentiation by high-throughput single-cell RNA-Seq. Preprint at http://biorxiv.org/content/early/2014/03/05/003236 (2014).
Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
This work was funded by grants U54-HL127365 and P50-GM107618 to P.K.S. and by a fellowship from the Swiss National Science Foundation (P300P3_147876) to M.H. We thank M. Soumillon for expression profiling, J. Chen (Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA) for the modified RPE-1 cells and A. Palmer, M. Eisenstein, and G. Berriz for help with the manuscript.
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Modeling drug response and dependence of drug response metrics on division time.
(a-b) Simulated data showing relative cell count (green lines) and GR value (purple lines) for a partial cytostatic (a) or cytotoxic (b); see Online Methods for parameter values. The darker the line the longer the division time (Td, given in days, see legend); note that all GR curves overlap. IC50 and GR50 are projected onto the x-axis, Emax and GRmax onto the y-axis. The subsequent three columns show values of IC50 and GR50 (left), Emax and GRmax (middle), and AUC and GRAOC (right) computed from a theoretical three-day assay with cells dividing at different rates under the same drug response models. (c) Values of AUC and GRAOC for a 3 day assay over a range of division times, with drug response model corresponding to Fig. 1g.
Supplementary Figure 2 Modeling toxic drug response and dependence of drug response metrics on division time.
(a-c) Simulated data showing relative cell count (green lines) and GR value (purple lines) for partial toxic effect (a), mixed toxic and cytostatic effects (b), or a strong toxic effect (c); see Online Methods for parameter values. The darker the line the longer the division time (Td,, given in days, see legend); note that all GR curves overlap. IC50 and GR50 are projected onto the x-axis, Emax and GRmax onto the y-axis. The subsequent three columns show values of IC50 and GR50 (left), Emax and GRmax (middle), and AUC and GRAOC (right) computed from a theoretical three-day assay with cells dividing at different rates under the same drug response models..
(a) Relative cell count (left) and GR values (right) were evaluated for hTERT RPE-1 cells expressing BRAFV600E under the control of a DOX-regulated promoter 48 h after treatment with etoposide. Color intensity reflects the concentration of DOX used to stimulate BRAFV600E expression. (b) Relative cell count (left) and GR values (right) were evaluated for MCF 10A cells grown in serum-free media with different levels of EGF 59 h after treatment with etoposide. Color intensity reflects treatments with different concentrations of EGF. (c) Evaluation of relative cell count (top) and GR values (bottom) for a drug concentration close to the GR50 value (left) and computed response metrics at different time points (middle and right) as estimated from live-cell imaging of BT-20 cells exposed to one of five drugs with different mechanisms of action. Data shown are from one of two biological replicates.
(a) Values of IC50 and GR50 (left) and Emax and GRmax (right) for cell cycle drugs evaluated by Heiser et al. Black lines show the median for a given number of divisions, color intensity reflects density of the data points. Undefined and large IC50 and GR50 values are capped at 100 μM for illustration purposes. (b) Values of IC50 and GR50 (left) and Emax and GRmax (right) for 20,000 models of drug response with randomized parameters and numbers of divisions. Black lines show the median for a given number of divisions, color intensity reflects density. Undefined and large IC50 and GR50 values are capped at 30 μM for illustration purposes. (c) Distribution of IC50 and GR50 (left) as well as Emax and GRmax (right) for paclitaxel response in cells grouped by subtype: HER2-amplified (HER2amp), triple-negative breast cancer (TNBC), hormone receptor-positive (HR+), and non-malignant (NM).
Spearman’s correlation between IC50 (top left), Emax (top right), GR50 (bottom left), or GRmax (bottom right) and number of divisions for six breast cancer cell lines. Data derive from an experiment in which cells were plated at a range of six densities and treated with eleven drugs having diverse mechanisms of action (see Online Methods and Supplementary Data 2). Significance: * for p<0.05, ** for p<0.01, and *** for p<0.001.
(a) Enriched gene sets for genes on the first principal component of transcriptional data from MCF 10A cells collected at different densities, 2 and 3 days after plating. Only genes in the leading edge of sets enriched at high density and late time points with FDR<0.1 are shown. Yellow represents up-regulation and blue down-regulation. Conditions are ordered by interpolated cell number (left column). (b) PCA projection of transcriptional data from MCF 10A cells collected at different densities, 2 and 3 days after plating. Shapes, day of collection; size, seeding density; color, interpolated cell number at time of collection. Axis labels include the Spearman’s correlation value between the cell number and the PCA coordinate.
GR value dose-response curves for MCF 10A cells seeded at different densities (indicated by color intensity) and treated with a dilution series of methotrexate for 3 days (left). Error bars are the SEM from three biological replicates. GR dose-response curves at 3 days for MCF 10A treated with oligomycin with a constant number of seeded cells in different volumes of media in each well (right). Data shown are from one of two biological replicates.
(a) Curve with negative GRmax value corresponds to a cytotoxic response (cell death). (b) Curve with GRmax value converging to 0 corresponds to a cytostatic response (no growth). (c) Curve with positive GRmax value corresponds to a partial growth inhibition; GR50 is not defined as GRinf is above 0.5. (d) Noisy, weak response for which the sigmoidal fit is not significantly better than a flat fit, and thus GEC50 is set to 0 and GR50 is not defined. Hill slope, hGR, is defined in all but the last case, whereas the area over the curve (GRAOC) can always be calculated.
Supplementary Figures 1–8 and Supplementary Note (PDF 1505 kb)
GR values and metrics computed from the drug-response data publishedin Heiser et al. (2012) (ZIP 2201 kb)
GR values and metrics for the drug-response data collected across different densities (ZIP 175 kb)
Source code for computing GR metrics. (ZIP 5 kb)
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Hafner, M., Niepel, M., Chung, M. et al. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods 13, 521–527 (2016). https://doi.org/10.1038/nmeth.3853
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