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The transcriptional network for mesenchymal transformation of brain tumours

Abstract

The inference of transcriptional networks that regulate transitions into physiological or pathological cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumour aggressiveness in human malignant glioma, but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here we show that reverse-engineering and an unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPβ and STAT3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPβ and STAT3 reprograms neural stem cells along the aberrant mesenchymal lineage, whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumour aggressiveness. In human glioma, expression of C/EBPβ and STAT3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results show that the activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.

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Figure 1: The mesenchymal signature of HGGs is controlled by six TFs.
Figure 2: A hierarchical transcriptional module regulates the MGES.
Figure 3: Ectopic expression of C/EBPβ and STAT3C in NSCs induces mesenchymal transformation and inhibits neural differentiation.
Figure 4: C/EBPβ and STAT3 maintain the mesenchymal phenotype of human glioma cells.
Figure 5: C/EBPβ and STAT3 are essential for glioma tumour aggressiveness in mice and humans.

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Gene Expression Omnibus

Data deposits

Gene expression data have been deposited in Gene Expression Omnibus (GEO) with the following accession numbers: GSE19113 for mouse and GSE19114 for human data.

References

  1. Ohgaki, H. & Kleihues, P. Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J. Neuropathol. Exp. Neurol. 64, 479–489 (2005)

    Article  CAS  Google Scholar 

  2. Demuth, T. & Berens, M. E. Molecular mechanisms of glioma cell migration and invasion. J. Neurooncol. 70, 217–228 (2004)

    Article  Google Scholar 

  3. Kargiotis, O., Rao, J. S. & Kyritsis, A. P. Mechanisms of angiogenesis in gliomas. J. Neurooncol. 78, 281–293 (2006)

    Article  CAS  Google Scholar 

  4. Phillips, H. S. et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157–173 (2006)

    Article  CAS  Google Scholar 

  5. Tso, C. L. et al. Primary glioblastomas express mesenchymal stem-like properties. Mol. Cancer Res. 4, 607–619 (2006)

    Article  CAS  Google Scholar 

  6. Takashima, Y. et al. Neuroepithelial cells supply an initial transient wave of MSC differentiation. Cell 129, 1377–1388 (2007)

    Article  CAS  Google Scholar 

  7. Wurmser, A. E. et al. Cell fusion-independent differentiation of neural stem cells to the endothelial lineage. Nature 430, 350–356 (2004)

    Article  ADS  CAS  Google Scholar 

  8. Rhodes, D. R. & Chinnaiyan, A. M. Integrative analysis of the cancer transcriptome. Nature Genet. 37 (suppl.). S31–S37 (2005)

    Article  CAS  Google Scholar 

  9. Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nature Genet. 37, 382–390 (2005)

    Article  CAS  Google Scholar 

  10. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008)

    Article  ADS  CAS  Google Scholar 

  11. Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7 (suppl. 1). S7 (2006)

    Article  Google Scholar 

  12. Margolin, A. A. et al. Reverse engineering cellular networks. Nature Protocols 1, 662–671 (2006)

    Article  CAS  Google Scholar 

  13. Zhao, X. et al. The N-Myc-DLL3 cascade is suppressed by the ubiquitin ligase Huwe1 to inhibit proliferation and promote neurogenesis in the developing brain. Dev. Cell 17, 210–221 (2009)

    Article  CAS  Google Scholar 

  14. Lim, W. K., Lyashenko, E. & Califano, A. Master regulators used as breast caqncer metastasis classifier. Pac. Symp. Biocomput. 14, 504–519 (2009)

    Google Scholar 

  15. Mani, K. M. et al. A systems biology approach to prediction of oncogenes and perturbation targets in B cell lymphomas. Mol. Syst. Biol. 4, 169–178 (2008)

    Article  Google Scholar 

  16. Palomero, T. et al. NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc. Natl Acad. Sci. USA 103, 18261–18266 (2006)

    Article  ADS  CAS  Google Scholar 

  17. Taylor, R. C., Acquaah-Mensah, G., Singhal, M., Malhotra, D. & Biswal, S. Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress. PLoS Comput. Biol. 4, e1000166 (2008)

    Article  Google Scholar 

  18. Hanauer, D. A., Rhodes, D. R., Sinha-Kumar, C. & Chinnaiyan, A. M. Bioinformatics approaches in the study of cancer. Curr. Mol. Med. 7, 133–141 (2007)

    Article  CAS  Google Scholar 

  19. Lander, A. D. A calculus of purpose. PLoS Biol. 2, e164 (2004)

    Article  Google Scholar 

  20. Freije, W. A. et al. Gene expression profiling of gliomas strongly predicts survival. Cancer Res. 64, 6503–6510 (2004)

    Article  CAS  Google Scholar 

  21. Nigro, J. M. et al. Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma. Cancer Res. 65, 1678–1686 (2005)

    Article  CAS  Google Scholar 

  22. The Gene Ontology Consortium Gene ontology: tool for the unification of biology. Nature Genet. 25, 25–29 (2000)

    Article  Google Scholar 

  23. Ramji, D. P. & Foka, P. CCAAT/enhancer-binding proteins: structure, function and regulation. Biochem. J. 365, 561–575 (2002)

    Article  CAS  Google Scholar 

  24. Aoki, K. et al. RP58 associates with condensed chromatin and mediates a sequence-specific transcriptional repression. J. Biol. Chem. 273, 26698–26704 (1998)

    Article  CAS  Google Scholar 

  25. Fuks, F., Burgers, W. A., Godin, N., Kasai, M. & Kouzarides, T. Dnmt3a binds deacetylases and is recruited by a sequence-specific repressor to silence transcription. EMBO J. 20, 2536–2544 (2001)

    Article  CAS  Google Scholar 

  26. Lee, J. P. et al. Stem cells act through multiple mechanisms to benefit mice with neurodegenerative metabolic disease. Nature Med. 13, 439–447 (2007)

    Article  CAS  Google Scholar 

  27. Park, K. I. et al. Acute injury directs the migration, proliferation, and differentiation of solid organ stem cells: evidence from the effect of hypoxia-ischemia in the CNS on clonal “reporter” neural stem cells. Exp. Neurol. 199, 156–178 (2006)

    Article  Google Scholar 

  28. Parker, M. A. et al. Expression profile of an operationally-defined neural stem cell clone. Exp. Neurol. 194, 320–332 (2005)

    Article  CAS  Google Scholar 

  29. Bromberg, J. F. et al. Stat3 as an oncogene. Cell 98, 295–303 (1999)

    Article  CAS  Google Scholar 

  30. Lee, J. et al. Tumor stem cells derived from glioblastomas cultured in bFGF and EGF more closely mirror the phenotype and genotype of primary tumors than do serum-cultured cell lines. Cancer Cell 9, 391–403 (2006)

    Article  CAS  Google Scholar 

  31. The Cancer Genome Atlas Research Network Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008)

    Article  Google Scholar 

  32. Pelloski, C. E. et al. YKL-40 expression is associated with poorer response to radiation and shorter overall survival in glioblastoma. Clin. Cancer Res. 11, 3326–3334 (2005)

    Article  CAS  Google Scholar 

  33. Ein-Dor, L., Kela, I., Getz, G., Givol, D. & Domany, E. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21, 171–178 (2005)

    Article  CAS  Google Scholar 

  34. Butte, A. J. & Kohane, I. S. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 5, 418–429 (2000)

    Google Scholar 

  35. Barnabé-Heider, F. et al. Evidence that embryonic neurons regulate the onset of cortical gliogenesis via cardiotrophin-1. Neuron 48, 253–265 (2005)

    Article  Google Scholar 

  36. Bonni, A. et al. Regulation of gliogenesis in the central nervous system by the JAK-STAT signaling pathway. Science 278, 477–483 (1997)

    Article  ADS  CAS  Google Scholar 

  37. Sterneck, E. & Johnson, P. F. CCAAT/enhancer binding protein β is a neuronal transcriptional regulator activated by nerve growth factor receptor signaling. J. Neurochem. 70, 2424–2433 (1998)

    Article  CAS  Google Scholar 

  38. Nadeau, S., Hein, P., Fernandes, K. J., Peterson, A. C. & Miller, F. D. A transcriptional role for C/EBP beta in the neuronal response to axonal injury. Mol. Cell. Neurosci. 29, 525–535 (2005)

    Article  CAS  Google Scholar 

  39. Ménard, C. et al. An essential role for a MEK-C/EBP pathway during growth factor-regulated cortical neurogenesis. Neuron 36, 597–610 (2002)

    Article  Google Scholar 

  40. Nakashima, K. et al. Synergistic signaling in fetal brain by STAT3-Smad1 complex bridged by p300. Science 284, 479–482 (1999)

    Article  ADS  CAS  Google Scholar 

  41. Paquin, A., Barnabe-Heider, F., Kageyama, R. & Miller, F. D. CCAAT/enhancer-binding protein phosphorylation biases cortical precursors to generate neurons rather than astrocytes in vivo . J. Neurosci. 25, 10747–10758 (2005)

    Article  CAS  Google Scholar 

  42. Bachoo, R. M. et al. Epidermal growth factor receptor and Ink4a/Arf: convergent mechanisms governing terminal differentiation and transformation along the neural stem cell to astrocyte axis. Cancer Cell 1, 269–277 (2002)

    Article  CAS  Google Scholar 

  43. 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)

    Article  ADS  CAS  Google Scholar 

  44. Tegner, J., Yeung, M. K., Hasty, J. & Collins, J. J. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc. Natl Acad. Sci. USA 100, 5944–5949 (2003)

    Article  ADS  CAS  Google Scholar 

  45. Bussemaker, H. J., Li, H. & Siggia, E. D. Regulatory element detection using correlation with expression. Nature Genet. 27, 167–174 (2001)

    Article  CAS  Google Scholar 

  46. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  47. Frank, S. R., Schroeder, M., Fernandez, P., Taubert, S. & Amati, B. Binding of c-Myc to chromatin mediates mitogen-induced acetylation of histone H4 and gene activation. Genes Dev. 15, 2069–2082 (2001)

    Article  CAS  Google Scholar 

  48. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT Method. Methods 25, 402–408 (2001)

    Article  CAS  Google Scholar 

  49. Du, P., Kibbe, W. A. & Lin, S. M. lumi: a pipeline for processing Illumina microarray. Bioinformatics 24, 1547–1548 (2008)

    Article  CAS  Google Scholar 

  50. Rothschild, G., Zhao, X., Iavarone, A. & Lasorella, A. E proteins and Id2 converge on p57Kip2 to regulate cell cycle in neural cells. Mol. Cell. Biol. 26, 4351–4361 (2006)

    Article  CAS  Google Scholar 

  51. Zhao, X. et al. The HECT-domain ubiquitin ligase Huwe1 controls neural differentiation and proliferation by destabilizing the N-Myc oncoprotein. Nature Cell Biol. 10, 643–653 (2008)

    Article  CAS  Google Scholar 

  52. Simmons, M. L. et al. Analysis of complex relationships between age, p53, epidermal growth factor receptor, and survival in glioblastoma patients. Cancer Res. 61, 1122–1128 (2001)

    CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by National Institute of Health grants R01CA109755 (A.C.), R01CA101644 (A.L.), R01CA085628 and R01NS061776 (A.I.), NCI Grand Opportunities TDDN Network 1RC2CA148308-01 (A.C.), In Silico Research Centre of Excellence NCI-caBIG 29XS192 (A.C.), National Centers for Biomedical Computing NIH Roadmap Initiative U54CA121852 (A.C.) and National Institute of General Medical Sciences grant P20GM075059 (E.Y.S.). M.S.C. is supported by a fellowship from the Italian Ministry of Welfare/Provincia di Benevento and S.L.A. by a fellowship from Fondation de Recherche Medicale. We thank N. Ramirez-Martinez for technical assistance with mouse husbandry and in vivo procedures.

Author Contributions A.C. and A.I. conceived the ideas for this study. A.C. designed the computational systems biology approach and A.I. the experimental platform. M.S.C. prepared constructs, performed the biochemical experiments and the microarrays, conducted biological experiments and analyses, assisted in mouse intracranial injections and performed tumour xenograft immunohistochemistry and tumour analysis. W.K.L. performed reverse engineering, master regulator, and statistical analyses. M.J.A. conducted gene expression, bioinformatics and statistical analyses. R.J.B. and E.Y.S. provided experimental material. X.Z. and F.D. assisted in mouse intracranial injections. E.P.S., H.C. and K.A. provided reagents, performed the arrayCGH/expression analysis and primary human tumour immunohistochemistry. S.L.A. performed cell culture immunofluorescence microscopy and analysis. A.L. assisted in primary NSC experiments, performed intracranial injections and assisted in the analysis of mouse xenografts. A.I. and A.C. wrote the manuscript with contributions from all other authors. M.S.C., W.K.L. and M.J.A. contributed equally to this work.

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Carro, M., Lim, W., Alvarez, M. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318–325 (2010). https://doi.org/10.1038/nature08712

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