Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway (original) (raw)

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Acknowledgements

We thank M. J. Hangauer, M. T. McManus, F. McCormick, K. Dutton-Regester, L. V. Kemeny, D. J. Adams and Y. Drier for valuable discussions and L. Hartman for execution of in vivo studies. This project has been supported by grants from the National Cancer Institute (Cancer Target Discovery and Development Network grant U01CA176152 to S.L.S., U01CA168397 to M.E.B., 5R01CA097061 and R01CA161061 to B.R.S., NCI-CA129933 to D.A.H., P30CA008748 to Y.C.), the National Institutes of Health (R01GM038627 to S.L.S., 5R01GM085081 to B.R.S.), the Swiss National Fund (310030_149946, to M.P.L.) and Howard Hughes Medical Institute (D.A.H., S.L.S.).

Author information

Authors and Affiliations

  1. Broad Institute, 415 Main Street, Cambridge, 02142, Massachusetts, USA
    Vasanthi S. Viswanathan, Matthew J. Ryan, Shubhroz Gill, Brinton Seashore-Ludlow, John K. Eaton, Andrew J. Aguirre, Srinivas R. Viswanathan, Shrikanta Chattopadhyay, Pablo Tamayo, Matthew G. Rees, Sixun Chen, Zarko V. Boskovic, Cherrie Huang, Xiaoyun Wu, Yuen-Yi Tseng, Jill P. Mesirov, Jesse S. Boehm, Joanne D. Kotz, Cindy S. Hon, William C. Hahn, John G. Doench, Alykhan F. Shamji, Paul A. Clemons & Stuart L. Schreiber
  2. Cancer and Cell Biology Division, The Translational Genomics Research Institute, 445 N 5th Street, Phoenix, 85004, Arizona, USA
    Harshil D. Dhruv & Michael E. Berens
  3. Department of Dermatology, University of Zurich, University Hospital of Zurich, Wagistrasse 14, Schlieren, CH-8952, Zürich, Switzerland
    Ossia M. Eichhoff, Elisabeth M. Roider & Mitchell P. Levesque
  4. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
    Samuel D. Kaffenberger, Dong Gao & Yu Chen
  5. Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Avenue, Boston, 02115, Massachusetts, USA
    Kenichi Shimada
  6. Department of Medical Oncology, Dana Farber Cancer Institute, Boston, 02115, Massachusetts, USA
    Andrew J. Aguirre, Srinivas R. Viswanathan, James M. Cleary, Brian M. Wolpin & William C. Hahn
  7. Moores Cancer Center & Department of Medicine, School of Medicine, University of California San Diego, La Jolla, 92093, California, USA
    Pablo Tamayo & Jill P. Mesirov
  8. Department of Biological Sciences, St. John’s University, 8000 Utopia Parkway, Queens, 11439, New York, USA
    Wan Seok Yang
  9. Massachusetts General Hospital Cancer Center, 149 13th Street, Charlestown, 02129, Massachusetts, USA
    Sarah Javaid & Daniel A. Haber
  10. Howard Hughes Medical Institute, Chevy Chase, 20815, Maryland, USA
    Daniel A. Haber & Stuart L. Schreiber
  11. Oncology Disease Area, Novartis Institute for Biomedical Research, Cambridge, 02139, Massachusetts, USA
    Jeffrey A. Engelman
  12. Department of Biological Sciences, Department of Chemistry, Columbia University, 550 West 120th Street, New York, 10027, New York, USA
    Brent R. Stockwell
  13. Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford St., Cambridge, Massachusetts 02138, USA
    Stuart L. Schreiber

Authors

  1. Vasanthi S. Viswanathan
  2. Matthew J. Ryan
  3. Harshil D. Dhruv
  4. Shubhroz Gill
  5. Ossia M. Eichhoff
  6. Brinton Seashore-Ludlow
  7. Samuel D. Kaffenberger
  8. John K. Eaton
  9. Kenichi Shimada
  10. Andrew J. Aguirre
  11. Srinivas R. Viswanathan
  12. Shrikanta Chattopadhyay
  13. Pablo Tamayo
  14. Wan Seok Yang
  15. Matthew G. Rees
  16. Sixun Chen
  17. Zarko V. Boskovic
  18. Sarah Javaid
  19. Cherrie Huang
  20. Xiaoyun Wu
  21. Yuen-Yi Tseng
  22. Elisabeth M. Roider
  23. Dong Gao
  24. James M. Cleary
  25. Brian M. Wolpin
  26. Jill P. Mesirov
  27. Daniel A. Haber
  28. Jeffrey A. Engelman
  29. Jesse S. Boehm
  30. Joanne D. Kotz
  31. Cindy S. Hon
  32. Yu Chen
  33. William C. Hahn
  34. Mitchell P. Levesque
  35. John G. Doench
  36. Michael E. Berens
  37. Alykhan F. Shamji
  38. Paul A. Clemons
  39. Brent R. Stockwell
  40. Stuart L. Schreiber

Contributions

S.L.S. directed the project; S.L.S. and V.S.V. wrote the manuscript; V.S.V. and M.J.R. performed research; H.D.D. performed in vivo experiments; S.G. performed CRISPR experiments; O.M.E. performed TGFβ treatment of melanoma cell lines; S.R.V. and S.Che. generated CRISPR reagents; S.D.K. performed organoid experiments; B.S.-L., A.J.A., M.G.R. and P.T. generated mesenchymal scores; K.S. performed the lipid peroxidation assay; W.S.Y. performed the GPX4 activity assay; Z.V.B. and J.K.E synthesized compounds; S.Cha. and C.H. contributed to profiling of non-transformed cell lines; J.M.C. and B.M.W. collected patient samples; A.J.A., X.W. and Y.-Y.T. generated patient-derived pancreatic cancer cell lines; J.P.M., D.A.H., J.A.E., J.S.B., D.G., S.J., Y.C., W.C.H., M.P.L., J.G.D., E.M.R and M.E.B. contributed reagents; J.D.K., C.S.H., B.R.S. and A.F.S. provided project support; P.A.C. oversaw data analysis; V.S.V. and P.A.C. performed large-scale data analysis.

Corresponding author

Correspondence toStuart L. Schreiber.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks N. Chandel, T. Sato and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Correlation of E-cadherin and vimentin protein levels of cell lines with sensitivity to mesenchymal state-targeting compounds.

a, b, Pancreatic and gastric cancer cell lines with low E-cadherin protein levels have high levels of vimentin (a) and are preferentially sensitive to ML210, a mesenchymal state-targeting compound (b). Concentration–response curves are from CTRP. c, d, Two patient-derived pancreatic cancer cell lines with differing sensitivity to erlotinib (d), show GPX4-inhibitor sensitivity and levels of epithelial and mesenchymal protein markers correlating in the predicted direction with erlotinib sensitivity. Data plotted in c and d are mean ± s.d. of four technical replicates and are representative of two biological replicates.

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Extended Data Figure 2 Lineage-specific AUC–mesenchymal score correlations.

Scatter plots with linear regression line (red) show the relationship between cancer cell-line mesenchymal score and cell-line sensitivity to ML210 (a ferroptosis-inducing, mesenchymal state-targeting compound) within different epithelium-derived cancer lineages. Gastrointestinal cancer lineages showing stronger correlations are demarcated with dashed boxes.

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Extended Data Figure 3 Correlation of individual cell-line features with mesenchymal state-targeting compounds.

a, Box-and-whisker plot shows the coefficient of correlation between the cytotoxic effects of the GPX4 inhibitor RSL3 and cytotoxic effects of 481 other compounds (black dots; inducers of electrophilic stress in shades of orange) across 656 cancer cell lines (excludes suspension cell lines). Plotted values are absolute correlation coefficients z scored using Fisher’s z transformation to account for individual compounds having been exposed to different numbers of cell lines; line, median; box, 25th–75th percentile; whiskers, expansion of the 2.5th and 97.5th percentile outlier compounds (black and coloured dots); dotted line marks 0.0. Data for compounds indicated to the right of the plot are significant with a P value of less than 0.005. b, c, Box-and-whisker plots show the extent of correlation between baseline expression of gene-expression transcripts (b) or sensitivity to gene knockdown (c) and cytotoxic effects of compounds with strong (RSL3, ML210), intermediate (erastin) and weak (piperlongumine) mesenchymal state-targeting properties despite otherwise sharing similar cell death-inducing profiles (shown in a). Plotted values in b and c are z scored Pearson’s correlation coefficients (see Methods); line, median; box, 25th–75th percentile; whiskers, expansion of 1st and 99th percentile outlier correlates; dotted line marks 0.0. Data highlighted with coloured dots are significant with a P value of less than 0.0002. Plots in b are derived from 610–631 cancer cell lines (excludes non-adherent cell lines). SLC7A11 (red dots) and SLC3A2 (orange dots) are shared gene-expression outlier correlates among the shown electrophilic stress-inducing compounds. Plots in c are derived from 132–136 cancer cell lines (excluding haematopoietic cell lines). Sensitivity to GPX4 knockdown (red dots) is uniquely correlated with sensitivity to mesenchymal state-targeting electrophilic compounds.

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Extended Data Figure 4 Effect of RSL3, ML162 and ML210 on GPX4 activity in cellular lysates.

Treatment of cells with RSL3, ML162 or ML210 inhibits the ability of cellular lysates to reduce exogenous phosphatidylcholine hydroperoxide (m/z of 790.6). Data reflect the results of single biological experiment.

Extended Data Figure 5 Modulation of statins by mevalonate pathway intermediates and antioxidants.

a, b, The effect of statins on HT-1080 fibrosarcoma-derived cells is rescued by co-treatment with mevalonic acid (a), but not by co-treatment with a lipophilic antioxidant (b). Data for two technical replicates are plotted; data represent two separate biological experiments.

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Extended Data Figure 6 Relative GPX4 inhibitor sensitivity of cell lines modelling EMT driven by inducible expression of EMT transcription factors.

a, Engineered MCF-7 breast cancer cells induced with a small molecule (4-hydroxytamoxifen; 4-OHT) to express high levels of SNAIL1 and undergo EMT (red curve). b, Engineered H358 lung cancer cells induced with 4-OHT to express high levels of TWIST1 and undergo EMT (red curve). Data plotted are mean ± s.d. of four technical replicates and are representative of two biological replicates.

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Extended Data Figure 7 Protein-level validation of successful gene knockout in CRISPR–Cas9-engineered cells.

a, GPX4 protein levels in _GPX4_-wild-type (WT) and _GPX4_-knockout (k/o) clones generated using CRISPR–Cas9 technology. b, ZEB1 protein levels in KP4 pancreatic cancer cells exposed to _ZEB1_-targeting CRISPR–Cas9 technology.

Extended Data Figure 8 Relative compound sensitivity of epithelial versus mesenchymal state cancer models.

a, HCC4006 lung cancer cells that have undergone EMT as a mechanism of resistance to gefitinib (red curve). Erastin and buthionine sulfoximine (BSO) are ferroptosis inducers, while piperlongumine is an electrophile that induces a non-ferroptotic form of oxidative cell death. b, Mesenchymal state patient-derived pancreatic cancer cells (AA01) undergo ferroptosis in response to GPX4 inhibition as evidenced by the ability of ferrostatin-1 to rescue loss of viability due to GPX4 inhibition. c, Patient-derived prostate cancer organoid lines show sensitivity to a GPX4 inhibitor (RSL3) in a manner correlated with mesenchymal gene-expression score (Fig. 3d), in both collagen-based and Matrigel-based culture conditions. This correlation with mesenchymal score is not seen for a control lethal agent (5-fluorouracil). d, Scatter plot with linear regression line (red) showing the correlation between a melanoma-specific high mesenchymal state score and sensitivity to a GPX4 inhibitor (ML210) across 49 melanoma-derived cell lines from CTRP. Data plotted in a–c are mean ± s.d. of four technical replicates (a and b) and three technical replicates (c) and are representative of two biological replicates.

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Extended Data Figure 9 Effect of lipophilic antioxidants on rescuing GPX4 inhibitor-mediated cell death in transformed versus non-transformed high-mesenchymal state cells.

a, Relative sensitivity of transformed and non-transformed high-mesenchymal state cell lines to GPX4 inhibition. Data for two technical replicates are plotted and represent two separate biological experiments. Concentration–response curves collected over a period of several months are plotted on a single set of axes to aid comparison of cell-line sensitivities. BJeH, foreskin fibroblasts; CD34+ cells, haematopoietic progenitor cells; HUVEC, human umbilical vein endothelial cells; LOXIMVI, melanoma-derived cells; MSC, mesenchymal stem cells; RKN, leiomyosarcoma-derived cells; WI38, lung fibroblasts. b, A single pre-treatment of cells with a lipophilic antioxidant (liproxstatin-1 or vitamin E) protects non-transformed mesenchymal state cells for a longer period of time than transformed high-mesenchymal state cells, from prolonged treatment with a high concentration of a GPX4 inhibitor (RSL3). c, d, Transformed high-mesenchymal state cells that are less sensitive to GPX4 inhibition (KP4) than non-transformed mesenchymal state cells (WI38, MSC) can be killed preferentially by pre-treating cells with a lipophilic antioxidant before initiating treatment with a GPX4 inhibitor. Data plotted in b–d are mean ± s.d. of four technical replicates and are representative of two biological replicates.

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Extended Data Figure 10 Relationship of GPX4 dependence and modulation of cellular lipid peroxides.

a, Cell-line sensitivity to exogenous lipid peroxides (for example, cholesterol peroxide) does not correlate with differential cell-line sensitivity to a GPX4 inhibitor (ML210). b, Cell-line sensitivity to GPX4 inhibition correlates positively with induction of lipid peroxidation upon GPX4 inhibition (GPX4i). c, d, Small-molecule inhibitors of arachidonic acid lipoxygenases (PD146176, zileuton) (c) and genetic knockout of two upstream regulators of arachidonic acid metabolism (ACSL4, LPCAT3) (d) prevent cell death induced by a GPX4 inhibitor (ML210) in KP4 cells. Data in a and c are two technical replicates whereas data in b and d are mean ± s.d. of three technical replicates. All panels represent two separate biological experiments.

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Supplementary information

Supplementary Data

This file contains raw western blot files for extended data figures 1, 2c, 7a and 7b. (PDF 811 kb)

Supplementary Table 1

This table contains mesenchymal scores for 516 carcinoma-derived cell lines calculated using single-sample gene set enrichment analysis from Taube, Goger and Byers EMT gene sets. (XLSX 32 kb)

Supplementary Table 2

This table contains mesenchymal score-correlations for 481 compounds computed from AUCs across 491 carcinoma-derived cell lines listed in Supplementary Table 1. (XLSX 100 kb)

Supplementary Table 3

This table contains gene-expression data for MCF-7 ER-Snail-1 cells (Haber Lab) treated with 4-hydroxytamoxifen (4-OHT) for 120 hours, allowed to recover from 4-OHT treatment for 24 hours, and then cultured in 384-well format in the absence of 4-OHT for 96 hours. These conditions model the compound exposure conditions for 4-OHT-induced MCF-7 ER-Snail-1 cells shown in Extended Data Fig. 6. Data are shown relative to ethanol-treated control cells. (XLSX 9 kb)

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Viswanathan, V., Ryan, M., Dhruv, H. et al. Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway.Nature 547, 453–457 (2017). https://doi.org/10.1038/nature23007

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