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Synthetic lethal metabolic targeting of cellular senescence in cancer therapy

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Abstract

Activated oncogenes and anticancer chemotherapy induce cellular senescence, a terminal growth arrest of viable cells characterized by S-phase entry-blocking histone 3 lysine 9 trimethylation (H3K9me3)1,2. Although therapy-induced senescence (TIS) improves long-term outcomes3, potentially harmful properties of senescent tumour cells make their quantitative elimination a therapeutic priority. Here we use the Eµ-myc transgenic mouse lymphoma model in which TIS depends on the H3K9 histone methyltransferase Suv39h1 to show the mechanism and therapeutic exploitation of senescence-related metabolic reprogramming in vitro and in vivo. After senescence-inducing chemotherapy, TIS-competent lymphomas but not TIS-incompetent Suv39h1 lymphomas show increased glucose utilization and much higher ATP production. We demonstrate that this is linked to massive proteotoxic stress, which is a consequence of the senescence-associated secretory phenotype (SASP) described previously4,5,6. SASP-producing TIS cells exhibited endoplasmic reticulum stress, an unfolded protein response (UPR), and increased ubiquitination, thereby targeting toxic proteins for autophagy in an acutely energy-consuming fashion. Accordingly, TIS lymphomas, unlike senescence models that lack a strong SASP response, were more sensitive to blocking glucose utilization or autophagy, which led to their selective elimination through caspase-12- and caspase-3-mediated endoplasmic-reticulum-related apoptosis. Consequently, pharmacological targeting of these metabolic demands on TIS induction in vivo prompted tumour regression and improved treatment outcomes further. These findings unveil the hypercatabolic nature of TIS that is therapeutically exploitable by synthetic lethal metabolic targeting.

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Figure 1: TIS is a highly glucose-consuming and energy-producing condition.
Figure 2: Selective vulnerability of TIS cells to inhibition of energy-generating catabolic pathways.
Figure 3: TIS shows a proteotoxic stress cascade.
Figure 4: Synthetic lethal targeting of TIS-related metabolic liabilties.

Accession codes

Accessions

Gene Expression Omnibus

Data deposits

Microarray data are deposited at the Gene Expression Omnibus under accession numbers GSE31099 and GSE44355.

Change history

  • 20 August 2013

    Source Data files for Figs 1–4 were added.

References

  1. 1

    Narita, M. et al. Rb-mediated heterochromatin formation and silencing of E2F target genes during cellular senescence. Cell 113, 703–716 (2003)

    CAS  PubMed  Google Scholar 

  2. 2

    Braig, M. et al. Oncogene-induced senescence as an initial barrier in lymphoma development. Nature 436, 660–665 (2005)

    CAS  PubMed  ADS  Google Scholar 

  3. 3

    Schmitt, C. A. et al. A senescence program controlled by p53 and p16INK4a contributes to the outcome of cancer therapy. Cell 109, 335–346 (2002)

    CAS  PubMed  Google Scholar 

  4. 4

    Acosta, J. C. et al. Chemokine signaling via the CXCR2 receptor reinforces senescence. Cell 133, 1006–1018 (2008)

    CAS  PubMed  Google Scholar 

  5. 5

    Coppe, J. P. et al. Senescence-associated secretory phenotypes reveal cell-nonautonomous functions of oncogenic RAS and the p53 tumor suppressor. PLoS Biol. 6, e301 (2008)

    PubMed Central  Google Scholar 

  6. 6

    Kuilman, T. et al. Oncogene-induced senescence relayed by an interleukin-dependent inflammatory network. Cell 133, 1019–1031 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Campisi, J. & d'Adda di Fagagna, F. Cellular senescence: when bad things happen to good cells. Nature Rev. Mol. Cell Biol. 8, 729–740 (2007)

    CAS  Google Scholar 

  8. 8

    Collado, M. & Serrano, M. Senescence in tumours: evidence from mice and humans. Nature Rev. Cancer 10, 51–57 (2010)

    CAS  Google Scholar 

  9. 9

    Kuilman, T., Michaloglou, C., Mooi, W. J. & Peeper, D. S. The essence of senescence. Genes Dev. 24, 2463–2479 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Nardella, C., Clohessy, J. G., Alimonti, A. & Pandolfi, P. P. Pro-senescence therapy for cancer treatment. Nature Rev. Cancer 11, 503–511 (2011)

    CAS  Google Scholar 

  11. 11

    Dimri, G. P. et al. A biomarker that identifies senescent human cells in culture and in aging skin in vivo. Proc. Natl Acad. Sci. USA 92, 9363–9367 (1995)

    CAS  ADS  Google Scholar 

  12. 12

    Moskowitz, C. H. et al. Risk-adapted dose-dense immunochemotherapy determined by interim FDG-PET in advanced-stage diffuse large B-cell lymphoma. J. Clin. Oncol. 28, 1896–1903 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Jones, R. G. et al. AMP-activated protein kinase induces a p53-dependent metabolic checkpoint. Mol. Cell 18, 283–293 (2005)

    CAS  PubMed  Google Scholar 

  14. 14

    Warburg, O., Posener, K. & Negelein, E. Über den Stoffwechsel der Carcinomzelle. Biochem. Z. 152, 319–344 (1924)

    Google Scholar 

  15. 15

    Vander Heiden, M. G. et al. Evidence for an alternative glycolytic pathway in rapidly proliferating cells. Science 329, 1492–1499 (2010)

    CAS  PubMed  ADS  Google Scholar 

  16. 16

    Young, A. R. et al. Autophagy mediates the mitotic senescence transition. Genes Dev. 23, 798–803 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Chien, Y. et al. Control of the senescence-associated secretory phenotype by NF-κB promotes senescence and enhances chemosensitivity. Genes Dev. 25, 2125–2136 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Jing, H. et al. Opposing roles of NF-κB in anti-cancer treatment outcome unveiled by cross-species investigations. Genes Dev. 25, 2137–2146 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Kroemer, G., Marino, G. & Levine, B. Autophagy and the integrated stress response. Mol. Cell 40, 280–293 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Pankiv, S. et al. p62/SQSTM1 binds directly to Atg8/lymphoma cells3 to facilitate degradation of ubiquitinated protein aggregates by autophagy. J. Biol. Chem. 282, 24131–24145 (2007)

    CAS  PubMed  Google Scholar 

  21. 21

    Nakagawa, T. et al. Caspase-12 mediates endoplasmic-reticulum-specific apoptosis and cytotoxicity by amyloid-beta. Nature 403, 98–103 (2000)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  22. 22

    Kaelin, W. G., Jr The concept of synthetic lethality in the context of anticancer therapy. Nature Rev. Cancer 5, 689–698 (2005)

    CAS  Google Scholar 

  23. 23

    Xue, W. et al. Senescence and tumour clearance is triggered by p53 restoration in murine liver carcinomas. Nature 445, 656–660 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Kang, T. W. et al. Senescence surveillance of pre-malignant hepatocytes limits liver cancer development. Nature 479, 547–551 (2011)

    CAS  ADS  Google Scholar 

  25. 25

    Reimann, M. et al. Tumor stroma-derived TGF-β limits Myc-driven lymphomagenesis via Suv39h1-dependent senescence. Cancer Cell 17, 262–272 (2010)

    CAS  PubMed  Google Scholar 

  26. 26

    Adams, J. M. et al. The c-myc oncogene driven by immunoglobulin enhancers induces lymphoid malignancy in transgenic mice. Nature 318, 533–538 (1985)

    CAS  PubMed  ADS  Google Scholar 

  27. 27

    Peters, A. H. et al. Loss of the Suv39h histone methyltransferases impairs mammalian heterochromatin and genome stability. Cell 107, 323–337 (2001)

    CAS  PubMed  Google Scholar 

  28. 28

    Schmitt, C. A. et al. Dissecting p53 tumor suppressor functions in vivo. Cancer Cell 1, 289–298 (2002)

    CAS  PubMed  Google Scholar 

  29. 29

    Schmitt, C. A., McCurrach, M. E., de Stanchina, E., Wallace-Brodeur, R. R. & Lowe, S. W. INK4a/ARF mutations accelerate lymphomagenesis and promote chemoresistance by disabling p53. Genes Dev. 13, 2670–2677 (1999)

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Shields, A. F. et al. Imaging proliferation in vivo with [F-18]FLT and positron emission tomography. Nature Med. 4, 1334–1336 (1998)

    CAS  PubMed  Google Scholar 

  31. 31

    Marciniak, S. J. et al. CHOP induces death by promoting protein synthesis and oxidation in the stressed endoplasmic reticulum. Genes Dev. 18, 3066–3077 (2004)

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Reimann, M. et al. The Myc-evoked DNA damage response accounts for treatment resistance in primary lymphomas in vivo. Blood 110, 2996–3004 (2007)

    CAS  PubMed  Google Scholar 

  33. 33

    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)

    CAS  ADS  Google Scholar 

  34. 34

    Walenta, S. et al. High lactate levels predict likelihood of metastases, tumor recurrence, and restricted patient survival in human cervical cancers. Cancer Res. 60, 916–921 (2000)

    CAS  PubMed  Google Scholar 

  35. 35

    Liu, L. et al. Deregulated MYC expression induces dependence upon AMPK-related kinase 5. Nature 483, 608–612 (2012)

    CAS  PubMed  ADS  Google Scholar 

  36. 36

    Kempa, S. et al. An automated GCxGC-TOF-MS protocol for batch-wise extraction and alignment of mass isotopomer matrixes from differential 13C-labelling experiments: a case study for photoautotrophic-mixotrophic grown Chlamydomonas reinhardtii cells. J. Basic Microbiol. 49, 82–91 (2009)

    CAS  PubMed  Google Scholar 

  37. 37

    Giavalisco, P. et al. High-resolution direct infusion-based mass spectrometry in combination with whole 13C metabolome isotope labeling allows unambiguous assignment of chemical sum formulas. Anal. Chem. 80, 9417–9425 (2008)

    CAS  PubMed  Google Scholar 

  38. 38

    Lisec, J., Schauer, N., Kopka, J., Willmitzer, L. & Fernie, A. R. Gas chromatography mass spectrometry-based metabolite profiling in plants. Nature Protocols 1, 387–396 (2006)

    CAS  PubMed  Google Scholar 

  39. 39

    Cuadros-Inostroza, A. et al. TargetSearch—a Bioconductor package for the efficient preprocessing of GC-MS metabolite profiling data. BMC Bioinformatics 10, 428 (2009)

    PubMed  PubMed Central  Google Scholar 

  40. 40

    Lisec, J. et al. Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns. Plant J. 68, 326–336 (2011)

    CAS  PubMed  Google Scholar 

  41. 41

    Stacklies, W., Redestig, H., Scholz, M., Walther, D. & Selbig, J. pcaMethods—a bioconductor package providing PCA methods for incomplete data. Bioinformatics 23, 1164–1167 (2007)

    CAS  Article  Google Scholar 

  42. 42

    Bode, C. & Graler, M. H. Quantification of sphingosine-1-phosphate and related sphingolipids by liquid chromatography coupled to tandem mass spectrometry. Methods Mol. Biol. 874, 33–44 (2012)

    CAS  PubMed  Google Scholar 

  43. 43

    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)

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Berns, K. et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428, 431–437 (2004)

    CAS  PubMed  ADS  Google Scholar 

  45. 45

    Reimer, T. A. et al. Reevaluation of the 22-1-1 antibody and its putative antigen, EBAG9/RCAS1, as a tumor marker. BMC Cancer 17, 47 (2005)

    Google Scholar 

  46. 46

    Castro, F. et al. High-throughput SNP-based authentication of human cell lines. Int. J. Cancer 132, 308–314 (2013)

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the late A. Harris, T. Jacks, T. Jenuwein, P. A. Khavari, N. Mizushima, D. Peeper, and M. Vander Heiden for mice, cells and materials; the flow cytometry facility at the Berlin-Brandenburg Center for Regenerative Therapies; N. Burbach, J. Dräger, A. Herrmann, K. Kirste, S. Maßwig, B. Teichmann and S. Spiesicke-Wegener for technical assistance; and members of the Schmitt laboratory for discussions and editorial advice. This work was supported by a Ph.D. fellowship to J.R.D. from the Boehringer Ingelheim Foundation, and grants from the Deutsche Forschungsgemeinschaft to W.M.-K. (MK576/15-1), to U.K. and A.K.B. (SFB 824), to U.K., B.D., S.L. and C.A.S. (SFB/TRR 54), and to C.A.S. from the Helmholtz Association (Helmholtz Alliance ‘Preclinical Comprehensive Cancer Center’; grant no. HA-305) and the Deutsche Krebshilfe (grant no. 108789). This interdisciplinary work was made possible by the structural framework of the inter-institutional cooperation between Charité and MDC (now represented by the Berlin Institute of Health (BIH)), the Berlin School of Integrative Oncology (BSIO) graduate program funded within the Excellence Initiative, and the German Cancer Consortium (GCC).

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J.R.D., S.L. and C.A.S. conceived the project, designed the experiments, and analysed the data, and W.M.-K., U.K., B.D., L.W. and St.K. provided critical input. Y.Y., G.B., C.Z., J.H.M.D., J.L., A.G., K.S., Su.K., S.W., M.G. and M.R. conducted experiments, M.M. compiled GSEA data, D.L. generated gene expression profiling data, M.H. analysed GEP data, B.P. carried out electron microscopy, and A.K.B. performed PET imaging. C.A.S., with editorial assistance from S.L., wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Clemens A. Schmitt.

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Dörr, J., Yu, Y., Milanovic, M. et al. Synthetic lethal metabolic targeting of cellular senescence in cancer therapy. Nature 501, 421–425 (2013). https://doi.org/10.1038/nature12437

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