Abstract

During the progression of pancreatic ductal adenocarcinoma (PDAC), heterogeneous subclonal populations emerge that drive primary tumor growth, regional spread, distant metastasis, and patient death. However, the genetics of metastases largely reflects that of the primary tumor in untreated patients, and PDAC driver mutations are shared by all subclones. This raises the possibility that an epigenetic process might operate during metastasis. Here we report large-scale reprogramming of chromatin modifications during the natural evolution of distant metastasis. Changes were targeted to thousands of large chromatin domains across the genome that collectively specified malignant traits, including euchromatin and large organized chromatin histone H3 lysine 9 (H3K9)-modified (LOCK) heterochromatin. Remarkably, distant metastases co-evolved a dependence on the oxidative branch of the pentose phosphate pathway (oxPPP), and oxPPP inhibition selectively reversed reprogrammed chromatin, malignant gene expression programs, and tumorigenesis. These findings suggest a model whereby linked metabolic–epigenetic programs are selected for enhanced tumorigenic fitness during the evolution of distant metastasis.

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References

  1. 1.

    et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).

  2. 2.

    et al. Immortalizing the complexity of cancer metastasis: genetic features of lethal metastatic pancreatic cancer obtained from rapid autopsy. Cancer Biol. Ther. 4, 548–554 (2005).

  3. 3.

    et al. DPC4 gene status of the primary carcinoma correlates with patterns of failure in patients with pancreatic cancer. J. Clin. Oncol. 27, 1806–1813 (2009).

  4. 4.

    et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 74, 2913–2921 (2014).

  5. 5.

    , , , & Large histone H3 lysine 9 dimethylated chromatin blocks distinguish differentiated from embryonic stem cells. Nat. Genet. 41, 246–250 (2009).

  6. 6.

    et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768–775 (2011).

  7. 7.

    et al. Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med. 6, 61 (2014).

  8. 8.

    , & Epigenetic modulators, modifiers and mediators in cancer aetiology and progression. Nat. Rev. Genet. 17, 284–299 (2016).

  9. 9.

    et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321, 1801–1806 (2008).

  10. 10.

    et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).

  11. 11.

    et al. Limited heterogeneity of known driver gene mutations among the metastases of individual pancreatic cancer patients. Nat. Genet. (2017).

  12. 12.

    , , , & Genome-scale epigenetic reprogramming during epithelial-to-mesenchymal transition. Nat. Struct. Mol. Biol. 18, 867–874 (2011).

  13. 13.

    et al. Human pancreatic adenocarcinoma: in vitro and in vivo morphology of a new tumor line established from ascites. In Vitro 18, 24–34 (1982).

  14. 14.

    et al. Antigens of human pancreatic adenocarcinoma cells defined by murine monoclonal antibodies. Cancer Res. 42, 601–608 (1982).

  15. 15.

    , , , & Morphological, biological, biochemical, and karyotypic characteristics of human pancreatic ductal adenocarcinoma Capan-2 in tissue culture and the nude mouse. Cancer Res. 46, 5810–5815 (1986).

  16. 16.

    Chromatin modifications and their function. Cell 128, 693–705 (2007).

  17. 17.

    et al. Histone methyltransferases direct different degrees of methylation to define distinct chromatin domains. Mol. Cell 12, 1591–1598 (2003).

  18. 18.

    et al. Establishment and maintenance of a heterochromatin domain. Science 297, 2232–2237 (2002).

  19. 19.

    et al. Origin of metastases: subspecies of cancers generated by intrinsic karyotypic variations. Cell Cycle 11, 1151–1166 (2012).

  20. 20.

    et al. ChIP–seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

  21. 21.

    et al. Euchromatin islands in large heterochromatin domains are enriched for CTCF binding and differentially DNA-methylated regions. BMC Genomics 13, 566 (2012).

  22. 22.

    et al. Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 22, 246–258 (2012).

  23. 23.

    et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell 25, 379–392 (2014).

  24. 24.

    et al. ATP–citrate lyase links cellular metabolism to histone acetylation. Science 324, 1076–1080 (2009).

  25. 25.

    et al. Akt-dependent metabolic reprogramming regulates tumor cell histone acetylation. Cell Metab. 20, 306–319 (2014).

  26. 26.

    et al. Histone methylation dynamics and gene regulation occur through the sensing of one-carbon metabolism. Cell Metab. 22, 861–873 (2015).

  27. 27.

    et al. The rate of glycolysis quantitatively mediates specific histone acetylation sites. Cancer Metab. 3, 10 (2015).

  28. 28.

    & Influence of metabolism on epigenetics and disease. Cell 153, 56–69 (2013).

  29. 29.

    , , , & Intracellular α-ketoglutarate maintains the pluripotency of embryonic stem cells. Nature 518, 413–416 (2015).

  30. 30.

    , & Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

  31. 31.

    & The Warburg effect: how does it benefit cancer cells? Trends Biochem. Sci. 41, 211–218 (2016).

  32. 32.

    et al. Lysine acetylation activates 6-phosphogluconate dehydrogenase to promote tumor growth. Mol. Cell 55, 552–565 (2014).

  33. 33.

    & 6-Aminonicotinamide—a potent nicotinamide antagonist. Science 122, 834 (1955).

  34. 34.

    , & Inhibition of NADP dependent oxidoreductases by the 6-aminonicotinamide analogue of NADP. FEBS Lett. 6, 225–228 (1970).

  35. 35.

    et al. Control of alveolar differentiation by the lineage transcription factors GATA6 and HOPX inhibits lung adenocarcinoma metastasis. Cancer Cell 23, 725–738 (2013).

  36. 36.

    et al. c-Met is a marker of pancreatic cancer stem cells and therapeutic target. Gastroenterology 141, 2218–2227.e5 (2011).

  37. 37.

    , , & Invasive three-dimensional organotypic neoplasia from multiple normal human epithelia. Nat. Med. 16, 1450–1455 (2010).

  38. 38.

    et al. Epidermal growth factor receptor mediates increased cell proliferation, migration, and aggregation in esophageal keratinocytes in vitro and in vivo. J. Biol. Chem. 278, 1824–1830 (2003).

  39. 39.

    , & Development and quantitative evaluation of a high-resolution metabolomics technology. Anal. Chem. 86, 2175–2184 (2014).

  40. 40.

    , & BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13, R83 (2012).

  41. 41.

    , , & OSA: a fast and accurate alignment tool for RNA–Seq. Bioinformatics 28, 1933–1934 (2012).

  42. 42.

    & Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

  43. 43.

    & Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  44. 44.

    , , & Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  45. 45.

    et al. Model-based analysis of ChIP–Seq (MACS). Genome Biol. 9, R137 (2008).

  46. 46.

    & Identifying dispersed epigenomic domains from ChIP–Seq data. Bioinformatics 27, 870–871 (2011).

  47. 47.

    et al. Distinct epigenomic landscapes of pluripotent and lineage-committed human cells. Cell Stem Cell 6, 479–491 (2010).

  48. 48.

    et al. Exploring massive, genome scale datasets with the GenometriCorr package. PLoS Comput. Biol. 8, e1002529 (2012).

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Acknowledgements

We thank J. Zacharias for technical assistance with sample processing and members of the Johns Hopkins DNA sequencing core facility for ChIP–seq on SOLiD formats. This work was supported by NIH grant CA38548 (A.P.F.), NIH grants CA140599 and CA179991 (C.A.I.-D.), the AACR Pancreatic Cancer Action Network Pathway to Leadership grant (O.G.M.), the Vanderbilt GI SPORE (O.G.M.), the Vanderbilt–Ingram Cancer Center (O.G.M.), and NIH grant CA180682 (A.M.-M.).

Author information

Author notes

    • Oliver G McDonald
    •  & Xin Li

    These authors contributed equally to this work.

Affiliations

  1. Department of Pathology, Microbiology and Immunology, Vanderbilt–Ingram Cancer Center, and Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

    • Oliver G McDonald
    • , Anna E Word
    • , Sonoko Natsume
    •  & Kimberly M Stauffer
  2. Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Xin Li
    • , Rakel Tryggvadottir
    • , Tal H Salz
    •  & Andrew P Feinberg
  3. Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Tyler Saunders
    • , Alvin Makohon-Moore
    •  & Yi Zhong
  4. Duke Cancer Institute, Duke Molecular Physiology Institute, Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, North Carolina, USA.

    • Samantha J Mentch
    • , Marc O Warmoes
    •  & Jason W Locasale
  5. Department of Cancer Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

    • Alessandro Carrer
    •  & Kathryn E Wellen
  6. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA.

    • Hao Wu
  7. Departments of Pathology and Human Oncology and Pathogenesis Program, David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Christine A Iacobuzio-Donahue
  8. Departments of Medicine, Biomedical Engineering and Mental Health, Johns Hopkins University Schools of Medicine, Engineering and Public Health, Baltimore, Maryland, USA.

    • Andrew P Feinberg

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Contributions

O.G.M., C.A.I.-D., and A.P.F. conceived the work and wrote the manuscript. A.P.F. oversaw epigenomic sequencing performed at Johns Hopkins University. C.A.I.-D. selected patient samples, performed pathological review, analyzed immunostaining data, and oversaw whole-genome sequencing studies performed at MSKCC. O.G.M. designed and performed or oversaw all experiments and data analysis conducted at Vanderbilt. X.L. and H.W. performed the bioinformatic and statistical analyses. O.G.M. and A.P.F. guided the bioinformatic analyses. T.S. performed immunohistochemical and immunofluorescence experiments. R.T. prepared sequencing libraries and performed sequencing runs, assisted by T.H.S. S.J.M., M.O.W., and J.W.L. performed the LC–HRMS measurements of metabolites. O.G.M. and A.E.W. analyzed and plotted LC–HRMS data. A.C. and K.E.W. performed and analyzed YSI glucose and lactate measurements. S.N. and K.M.S. maintained cell culture and performed NADPH assays and a subset of immunoblotting. Y.Z. performed animal injection experiments. A.M.-M. performed whole-genome sequencing. O.G.M. prepared the figures. All authors approved the final version of the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Christine A Iacobuzio-Donahue or Andrew P Feinberg.

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Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–15, Supplementary Tables 1, 5, 6 and 8–16, and Supplementary Note

Excel files

  1. 1.

    Supplementary Table 2

    Data sets and correlation coefficients.

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    Supplementary Table 3

    Domain characteristics.

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    Supplementary Table 4

    Reprogramming within domains and sensitivity analyses.

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    Supplementary Table 7

    Gene expression changes.

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DOI

https://doi.org/10.1038/ng.3753

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