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Rethinking cellular drug response
Large-scale cell line profiling of drugs provides dose-response curves that contain numerous lesser-considered parameters. Understanding the reasons for systematic variation in these parameters offers new ways to compare drugs and potentially to guide improved drug profiles.
Jeremy L Jenkins Fallahi-Sichani et al.1 performed multiparametric analysis on dose-response data from 64 anticancer drugs in 53 breast cancer cell lines and on other cancer cell line data. The authors observed that dose-response curve parameters vary in a systematic way depending on properties of the components in the screening well, namely, cell proliferation rates and drug classes. Compound mechanism strongly influences potency, efficacy and steepness of the a
dose-response curve. In contrast, cell doubling time did not correlate with curve steepness; rather, doubling times correlated with potency and maximal efficacy for drugs that have cytotoxicity especially potentiated by cell cycling (for example, antimetabolites, mitotic poisons and topoisomerase inhibitors). With regard to drug class, the maximal effect was consistently high for HDAC and DNA cross-linkers, whereas MTOR inhibition yielded curves shallower than expected c
100
Cell viability
HS = 1
HS < 1
50
Benefit of increasing therapeutic dose from [1] to [10].
HS < 1 HS = 1 HS > 1
0 1
10
Drug concentration b
Signal
Target inhibition in cells
PI3K AKT HS > 1 Target inhibition– induced cooperativity • HDAC • Proteasome
HS = 1 Target fully available in cells • DNA crosslinkers
HS < 1 Fractional killing from target or pathway flux or feedback regulation • PI3K/AKT/MTOR
MTOR
Marina Corral Spence
npg
© 2013 Nature America, Inc. All rights reserved.
C
ell line profiling of compounds is an approach historically important to oncology research in government, academia and commercial drug discovery. Dose-response matrices generated across compounds and cell lines have been widely used to characterize drug potency and tumor lineage specificity. In this issue, Fallahi-Sichani et al.1 go beyond traditional drug potency measures such as halfmaximum inhibitory concentration and uncover systematic variations due to drug mechanisms, cell proliferation rates and cell-to-cell variability in target inhibition, which together help to explain why different drug classes elicit different cellular responses. In the early 1990s, high-throughput compound profiling in cultured cancer cell lines was being used to discover potential stratification of cancer drugs based on tumor lineage classifications2. In the intervening years, advances in robotics, screening and next-generation sequencing have produced higher-throughput profiling data sets with additional axes of cellular genetic features3–6. Correlation of drug potency with features from cellular sequencing data, such as the Cancer Cell Line Encyclopedia5, fostered a surge in methods to predict drug stratification on mutations, gene expression, copy number and more. Nevertheless, despite the impressive advances in screening capacity and ‘omics’ data integration, there remain gaps in how compound doseresponse curve metrics are incorporated in our large-scale analyses. Beyond halfmaximum inhibitory concentration, halfmaximum effective concentration and halfmaximum growth inhibition, more recent publications incorporated Amax and the area under the curve to characterize cancer cell sensitivity to drugs5,6; however, the Hill slope (HS)—part of the dose-response sigmoidal equation—seems to be ignored owing to limited appreciation for its relevance or interpretability in cellular screening. In purely enzymatic assays, HS values greater or less than one reflect positive or negative drug cooperativity. In a cellular context, the possibilities are more complex.
Pathway
Figure 1 | A model for how fractional cell killing may relate to dose-response curve steepness. (a) Prototypical dose responses with normal (HS = 1), steep (HS > 1) or shallow (HS < 1) dose-response curves. (b) For each curve, the cellular target ‘availability’ is shown. For HS > 1, the drug target is uniformly available, and drug-induced target inhibition creates a ‘cooperative’ toxicity. For example, HDAC or proteasome inhibition may have pleiotropic effects on the expression or stability of additional targets. Drugs with polypharmacology may also show steeper slopes. For HS = 1, the drug target is uniformly available in cells (for example, DNA crosslinking). For HS < 1 (shallow slopes), such as targets in dynamic signaling pathways, stochastic fluctuation in the available levels of protein or from feedback regulation causes a lack of drug target availability in a subset of cells. This property is stable, and cells can fluctuate between sensitive and insensitive. (c) The benefit of increasing drug therapeutic dose (for example, to achieve maximal therapeutic dose in vivo) is influenced by HS in that higher concentrations of shallowslope drugs are required to achieve the same efficacy.
nature chemical biology | VOL 9 | NOVEMBER 2013 | www.nature.com/naturechemicalbiology
669
(HS < 1), and HDAC or proteasome inhibition resulted in curves steeper than expected (HS > 1). Importantly, using immunofluorescence microscopy, the authors found that cell-to-cell variability in target modulation influenced the shape of the doseresponse curve averaged for a cell population. Specifically, cell-to-cell variability in target inhibition resulted in fractional killing—that is, subpopulations of cells were simply unaffected at high drug doses, thus creating shallower curves. Wash-out experiments demonstrated that the fractional killing is ‘heritable’, meaning that single cells can flux between sensitive and insensitive states. By demonstrating cell-to-cell variability in drug targets linked to drug cellular dose-response curve shape, the authors have filled a gap in our understanding of cellular pharmacology (Fig. 1). Specifically with regards to the HS, fractional killing linked to drug target fluctuations or feedback loops could explain stable nonresponder cell populations. For example, feedback inhibition in the MTOR pathway may contribute to shallow HS curves (HS < 1; Fig. 1a,b), which might itself be targeted to counter anticooperativity and
improve therapeutic index (Fig. 1c). Thus, during drug optimization, chemists might also seek to tweak these lesser-appreciated parameters of the dose-response curve as opposed to focusing mostly on potency. In related HIV drug research, it was previously noted that protease inhibitors with HS > 1 achieved potent target inhibition, and, surprisingly, mutations in protease that confer drug resistance altered HS (from steeper to less steep). This was not true for other HIV target classes7. An alternative explanation for shallow HS curves is that target inhibition in cells may not correlate with antiproliferation. Indeed, in one study of antimitotic drugs, no correlation between levels of mitotic arrest and apoptosis were observed across cell lines, demonstrating a decoupling of target modulation and cell killing8. Large-scale cellular profiling clearly generates a number of dose-response curve parameters that yield different information from one another and vary in systematic ways on the basis of drug class (for example, Emax for HDAC inhibitors or half-maximum inhibitory concentration for EGFR inhibitors) and cell doubling times. Owing to the orthogonal information
of curve metrics, a further application is to use the curve parameters to cluster drugs by mechanism9 and potentially elucidate targets of new compounds. Overall, these lesser-appreciated parameters should immediately find their way into cell line profiling analyses. ■ Jeremy L. Jenkins is in the Department of Developmental and Molecular Pathways at the Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA. e-mail: [email protected] References
1. Fallahi-Sichani, M., Honarnejad, S., Heiser, L.M., Gray, J.W. & Sorger, P.K. Nat. Chem. Biol. 9, 708–714 (2013). 2. Monks, A. et al. J. Natl. Cancer Inst. 83, 757–766 (1991). 3. Scherf, U. et al. Nat. Genet. 24, 236–244 (2000). 4. Covell, D.G. PLoS ONE 7, e44631 (2012). 5. Barretina, J. et al. Nature 483, 603–607 (2012). 6. Basu, A. et al. Cell 154, 1151–1161 (2013). 7. Sampah, M.E.S., Shen, L., Jilek, B.L. & Siliciano, R.F. Proc. Natl. Acad. Sci. USA 108, 7613–7618 (2011). 8. Shi, J., Orth, J.D. & Mitchison, T. Cancer Res. 68, 3269–3276 (2008). 9. Rabow, A.A., Shoemaker, R.H., Sausville, E.A. & Covell, D.G. J. Med. Chem. 45, 818–840 (2002).
Competing financial interests The author declares no competing financial interests.
npg
© 2013 Nature America, Inc. All rights reserved.
news & views
670
nature chemical biology | VOL 9 | NOVEMBER 2013 | www.nature.com/naturechemicalbiology
Rethinking cellular drug response
Large-scale cell line profiling of drugs provides dose-response curves that contain numerous lesser-considered parameters. Understanding the reasons for systematic variation in these parameters offers new ways to compare drugs and potentially to guide improved drug profiles.
Jeremy L Jenkins Fallahi-Sichani et al.1 performed multiparametric analysis on dose-response data from 64 anticancer drugs in 53 breast cancer cell lines and on other cancer cell line data. The authors observed that dose-response curve parameters vary in a systematic way depending on properties of the components in the screening well, namely, cell proliferation rates and drug classes. Compound mechanism strongly influences potency, efficacy and steepness of the a
dose-response curve. In contrast, cell doubling time did not correlate with curve steepness; rather, doubling times correlated with potency and maximal efficacy for drugs that have cytotoxicity especially potentiated by cell cycling (for example, antimetabolites, mitotic poisons and topoisomerase inhibitors). With regard to drug class, the maximal effect was consistently high for HDAC and DNA cross-linkers, whereas MTOR inhibition yielded curves shallower than expected c
100
Cell viability
HS = 1
HS < 1
50
Benefit of increasing therapeutic dose from [1] to [10].
HS < 1 HS = 1 HS > 1
0 1
10
Drug concentration b
Signal
Target inhibition in cells
PI3K AKT HS > 1 Target inhibition– induced cooperativity • HDAC • Proteasome
HS = 1 Target fully available in cells • DNA crosslinkers
HS < 1 Fractional killing from target or pathway flux or feedback regulation • PI3K/AKT/MTOR
MTOR
Marina Corral Spence
npg
© 2013 Nature America, Inc. All rights reserved.
C
ell line profiling of compounds is an approach historically important to oncology research in government, academia and commercial drug discovery. Dose-response matrices generated across compounds and cell lines have been widely used to characterize drug potency and tumor lineage specificity. In this issue, Fallahi-Sichani et al.1 go beyond traditional drug potency measures such as halfmaximum inhibitory concentration and uncover systematic variations due to drug mechanisms, cell proliferation rates and cell-to-cell variability in target inhibition, which together help to explain why different drug classes elicit different cellular responses. In the early 1990s, high-throughput compound profiling in cultured cancer cell lines was being used to discover potential stratification of cancer drugs based on tumor lineage classifications2. In the intervening years, advances in robotics, screening and next-generation sequencing have produced higher-throughput profiling data sets with additional axes of cellular genetic features3–6. Correlation of drug potency with features from cellular sequencing data, such as the Cancer Cell Line Encyclopedia5, fostered a surge in methods to predict drug stratification on mutations, gene expression, copy number and more. Nevertheless, despite the impressive advances in screening capacity and ‘omics’ data integration, there remain gaps in how compound doseresponse curve metrics are incorporated in our large-scale analyses. Beyond halfmaximum inhibitory concentration, halfmaximum effective concentration and halfmaximum growth inhibition, more recent publications incorporated Amax and the area under the curve to characterize cancer cell sensitivity to drugs5,6; however, the Hill slope (HS)—part of the dose-response sigmoidal equation—seems to be ignored owing to limited appreciation for its relevance or interpretability in cellular screening. In purely enzymatic assays, HS values greater or less than one reflect positive or negative drug cooperativity. In a cellular context, the possibilities are more complex.
Pathway
Figure 1 | A model for how fractional cell killing may relate to dose-response curve steepness. (a) Prototypical dose responses with normal (HS = 1), steep (HS > 1) or shallow (HS < 1) dose-response curves. (b) For each curve, the cellular target ‘availability’ is shown. For HS > 1, the drug target is uniformly available, and drug-induced target inhibition creates a ‘cooperative’ toxicity. For example, HDAC or proteasome inhibition may have pleiotropic effects on the expression or stability of additional targets. Drugs with polypharmacology may also show steeper slopes. For HS = 1, the drug target is uniformly available in cells (for example, DNA crosslinking). For HS < 1 (shallow slopes), such as targets in dynamic signaling pathways, stochastic fluctuation in the available levels of protein or from feedback regulation causes a lack of drug target availability in a subset of cells. This property is stable, and cells can fluctuate between sensitive and insensitive. (c) The benefit of increasing drug therapeutic dose (for example, to achieve maximal therapeutic dose in vivo) is influenced by HS in that higher concentrations of shallowslope drugs are required to achieve the same efficacy.
nature chemical biology | VOL 9 | NOVEMBER 2013 | www.nature.com/naturechemicalbiology
669
(HS < 1), and HDAC or proteasome inhibition resulted in curves steeper than expected (HS > 1). Importantly, using immunofluorescence microscopy, the authors found that cell-to-cell variability in target modulation influenced the shape of the doseresponse curve averaged for a cell population. Specifically, cell-to-cell variability in target inhibition resulted in fractional killing—that is, subpopulations of cells were simply unaffected at high drug doses, thus creating shallower curves. Wash-out experiments demonstrated that the fractional killing is ‘heritable’, meaning that single cells can flux between sensitive and insensitive states. By demonstrating cell-to-cell variability in drug targets linked to drug cellular dose-response curve shape, the authors have filled a gap in our understanding of cellular pharmacology (Fig. 1). Specifically with regards to the HS, fractional killing linked to drug target fluctuations or feedback loops could explain stable nonresponder cell populations. For example, feedback inhibition in the MTOR pathway may contribute to shallow HS curves (HS < 1; Fig. 1a,b), which might itself be targeted to counter anticooperativity and
improve therapeutic index (Fig. 1c). Thus, during drug optimization, chemists might also seek to tweak these lesser-appreciated parameters of the dose-response curve as opposed to focusing mostly on potency. In related HIV drug research, it was previously noted that protease inhibitors with HS > 1 achieved potent target inhibition, and, surprisingly, mutations in protease that confer drug resistance altered HS (from steeper to less steep). This was not true for other HIV target classes7. An alternative explanation for shallow HS curves is that target inhibition in cells may not correlate with antiproliferation. Indeed, in one study of antimitotic drugs, no correlation between levels of mitotic arrest and apoptosis were observed across cell lines, demonstrating a decoupling of target modulation and cell killing8. Large-scale cellular profiling clearly generates a number of dose-response curve parameters that yield different information from one another and vary in systematic ways on the basis of drug class (for example, Emax for HDAC inhibitors or half-maximum inhibitory concentration for EGFR inhibitors) and cell doubling times. Owing to the orthogonal information
of curve metrics, a further application is to use the curve parameters to cluster drugs by mechanism9 and potentially elucidate targets of new compounds. Overall, these lesser-appreciated parameters should immediately find their way into cell line profiling analyses. ■ Jeremy L. Jenkins is in the Department of Developmental and Molecular Pathways at the Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA. e-mail: [email protected] References
1. Fallahi-Sichani, M., Honarnejad, S., Heiser, L.M., Gray, J.W. & Sorger, P.K. Nat. Chem. Biol. 9, 708–714 (2013). 2. Monks, A. et al. J. Natl. Cancer Inst. 83, 757–766 (1991). 3. Scherf, U. et al. Nat. Genet. 24, 236–244 (2000). 4. Covell, D.G. PLoS ONE 7, e44631 (2012). 5. Barretina, J. et al. Nature 483, 603–607 (2012). 6. Basu, A. et al. Cell 154, 1151–1161 (2013). 7. Sampah, M.E.S., Shen, L., Jilek, B.L. & Siliciano, R.F. Proc. Natl. Acad. Sci. USA 108, 7613–7618 (2011). 8. Shi, J., Orth, J.D. & Mitchison, T. Cancer Res. 68, 3269–3276 (2008). 9. Rabow, A.A., Shoemaker, R.H., Sausville, E.A. & Covell, D.G. J. Med. Chem. 45, 818–840 (2002).
Competing financial interests The author declares no competing financial interests.
npg
© 2013 Nature America, Inc. All rights reserved.
news & views
670
nature chemical biology | VOL 9 | NOVEMBER 2013 | www.nature.com/naturechemicalbiology
