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Drug antagonism and single-agent dominance result from differences in death kinetics

Nature Chemical Biology volume 16, pages 791–800 (2020)Cite this article

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Abstract

Cancer treatment generally involves drugs used in combinations. Most previous work has focused on identifying and understanding synergistic drug–drug interactions; however, understanding antagonistic interactions remains an important and understudied issue. To enrich for antagonism and reveal common features of these combinations, we screened all pairwise combinations of drugs characterized as activators of regulated cell death. This network is strongly enriched for antagonism, particularly a form of antagonism that we call ‘single-agent dominance’. Single-agent dominance refers to antagonisms in which a two-drug combination phenocopies one of the two agents. Dominance results from differences in cell death onset time, with dominant drugs acting earlier than their suppressed counterparts. We explored mechanisms by which parthanatotic agents dominate apoptotic agents, finding that dominance in this scenario is caused by mutually exclusive and conflicting use of Poly(ADP-ribose) polymerase 1 (PARP1). Taken together, our study reveals death kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between cell death pathways.

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Fig. 1: A high-throughput assay to monitor cell death kinetics.
Fig. 2: Accurate analysis of death kinetics for apoptotic and non-apoptotic agents.
Fig. 3: Combination drug screen to evaluate co-activation of apoptotic and non-apoptotic death pathways.
Fig. 4: Combinations of apoptotic and non-apoptotic cell death drugs are enriched for antagonism and single-agent dominance.
Fig. 5: Statistical modeling reveals death onset kinetics as a key determinant of SAD combinations.
Fig. 6: PARP1-dependent interactions mediate single-agent dominance and choice between parthanatotic and apoptotic death.

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Data availability

Source data for evaluation of the mechanism by which drugs led to cell death are included in Supplementary Dataset 1. Source data for the drug combination screen in Fig. 2d are included in Supplementary Dataset th2. PCA score data related to Fig. 4a–c are included in Supplementary Dataset 3. The list of 130 SAD combinations identified in this study is included in Supplementary Dataset 4. All other data are available upon request.

Code availability

Custom analysis code for computing LF kinetics from endpoint data is included in the MATLAB script ‘backfitting and LED.m’. Other analysis code is available upon request.

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Acknowledgements

We thank current and past members of the Lee labotatory and all members of PSB for their helpful comments and critiques during the execution of this study. In addition, we thank M. Walhout, J. Dekker, A. Mitchell and J. Pritchard for their thoughtful comments during the preparation of this manuscript. The px330-puro-hSpCas9 plasmid was a kind gift from T. Fazzio (UMass Medical School). This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health (R01GM127559 to M.J.L.); the American Cancer Society (RSG-17-011-01 to M.J.L.); and an NIH/NCI training grant (Translational Cancer Biology Training Grant, T32-CA130807 to R.R., B.D.L. and P.C.G.).

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Authors and Affiliations

  1. Program in Systems Biology (PSB), University of Massachusetts Medical School, Worcester, MA, USA

    Ryan Richards, Hannah R. Schwartz, Megan E. Honeywell, Mariah S. Stewart, Peter Cruz-Gordillo, Anna J. Joyce, Benjamin D. Landry & Michael J. Lee

  2. Program in Molecular Medicine (PMM), University of Massachusetts Medical School, Worcester, MA, USA

    Michael J. Lee

  3. Department of Molecular, Cell and Cancer Biology (MCCB), University of Massachusetts Medical School, Worcester, MA, USA

    Michael J. Lee

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Contributions

This project was conceived by R.R. and M.J.L. Combinatorial drug screening was designed, executed and analyzed by R.R., B.D.L. and P.C.G., and M.J.L. helped with the execution of the combination drug screen. Imaging experiments and STACK analysis were performed by R.R. and H.R.S. Drug evaluation and annotation of the drug mechanism of action were performed by R.R., A.J.J., P.C.G. and M.S.S. Flow cytometry-based analyses were performed and analyzed by R.R. and M.E.H. All other statistical analysis and modeling were conducted by R.R., M.E.H. and M.J.L. R.R. and M.J.L. wrote and edited the manuscript.

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Correspondence to Michael J. Lee.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and Supplementary Tables 1–3.

Reporting Summary

Supplementary Dataset 1

Drug responses in U2OS WT and DKO cells.

Supplementary Dataset 2

Combination drug screen.

Supplementary Dataset 3

PCA scores for drugs and drug combinations.

Supplementary Dataset 4

SAD combinations in U2OS cells.

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Richards, R., Schwartz, H.R., Honeywell, M.E. et al. Drug antagonism and single-agent dominance result from differences in death kinetics. Nat Chem Biol 16, 791–800 (2020). https://doi.org/10.1038/s41589-020-0510-4

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