Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures

Nature Communications

Volumn 8, Issue 1, 2017, Pages

  Khan, Faiz M ^a   Marquardt, Stephan ^b   Gupta, Shailendra K ^a,c   Knoll, Susanne ^b   Schmitz, Ulf ^a,d,e   Spitschak, Alf ^b   Engelmann, David ^b   Vera, Julio ^f   Wolkenhauer, Olaf ^a,g   Pützer, Brigitte M ^b  

^a UNIVERSITY OF ROSTOCK   (Germany)

^b ROSTOCK UNIVERSITY MEDICAL CENTER   (Germany)

^c CSIR INDIAN INSTITUTE OF TOXICOLOGY RESEARCH   (India)

^d CENTENARY INSTITUTE   (Australia)

^e UNIVERSITY OF SYDNEY   (Australia)

^f UNIVERSITY HOSPITAL ERLANGEN   (Germany)

^g STELLENBOSCH UNIVERSITY   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

RECEPTOR PROTEIN; TRANSCRIPTION FACTOR E2F1;

CANCER; CELLS AND CELL COMPONENTS; COHORT ANALYSIS; GENE EXPRESSION; PROTEIN; TUMOR;

ARTICLE; BLADDER TUMOR; BREAST TUMOR; CANCER CELL LINE; EPITHELIAL MESENCHYMAL TRANSITION; GENE EXPRESSION PROFILING; HUMAN; HUMAN CELL; IN VITRO STUDY; PREDICTIVE VALUE; PROTEIN INTERACTION; REGULATORY MECHANISM; TUMOR DIFFERENTIATION; TUMOR INVASION;

EID: 85026773047     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-017-00268-2     Document Type: Article

References (75)

1. Kreeger, P. K. & Lauffenburger, D. A. Cancer systems biology: a network modeling perspective. Carcinogenesis 31, 2-8 (2010).
2. Krogan, N. J., Lippman, S., Agard, D. A., Ashworth, A. & Ideker, T. The cancer cell map initiative: defining the hallmark networks of cancer. Mol. Cell 58, 690-698 (2015).
3. Wachi, S., Yoneda, K. & Wu, R. Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics 21, 4205-4208 (2005).
4. Jonsson, P. F. & Bates, P. A. Global topological features of cancer proteins in the human interactome. Bioinformatics 22, 2291-2297 (2006).
5. Hofree, M., Shen, J. P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 1108-1115 (2013).
6. Alon, U. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450-461 (2007).
7. Kitano, H. A robustness-based approach to systems-oriented drug design. Nat. Rev. Drug Discov. 6, 202-210 (2007).
8. Polager, S. & Ginsberg, D. E2F-at the crossroads of life and death. Trends Cell Biol. 18, 528-535 (2008).
9. Alla, V. et al. E2F1 in melanoma progression and metastasis. J. Natl Cancer Inst. 102, 127-133 (2010).
10. Pützer, B. M., Steder, M. & Alla, V. Predicting and preventing melanoma invasiveness: advances in clarifying E2F1 function. Expert Rev. Anticancer Ther. 10, 1707-1720 (2010).
11. Alla, V. et al. E2F1 confers anticancer drug resistance by targeting ABC transporter family members and Bcl-2 via the p73/DNp73-miR-205 circuitry. Cell Cycle 11, 3067-3078 (2012).
12. Engelmann, D. & Pützer, B. M. The dark side of E2F1: in transit beyond apoptosis. Cancer Res. 72, 571-575 (2012).
13. Vera, J. et al. Kinetic modeling-based detection of genetic signatures that provide chemoresistance via the E2F1-p73/DNp73-miR-205 network. Cancer Res. 73, 3511-3524 (2013).
14. Lee, J.-S. et al. Expression signature of E2F1 and its associated genes predict superficial to invasive progression of bladder tumors. J. Clin. Oncol. 28, 2660-2667 (2010).
15. Sharma, A. et al. The retinoblastoma tumor suppressor controls androgen signaling and human prostate cancer progression. J. Clin. Invest. 120, 4478-4492 (2010).
16. Knoll, S., Emmrich, S. & Pützer, B. M. The E2F1-miRNA cancer progression network. Adv. Exp. Med. Biol. 774, 135-147 (2013).
17. Knoll, S. et al. E2F1 induces miR-224/452 expression to drive EMT through TXNIP downregulation. EMBO Rep. 15, 1315-1329 (2014).
18. Gregory, P. A. et al. The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat. Cell Biol. 10, 593-601 (2008).
19. Andrechek, E. R. HER2/Neu tumorigenesis and metastasis is regulated by E2F activator transcription factors. Oncogene 34, 217-225 (2015).
20. Shackney, S. E., Chowdhury, S. A. & Schwartz, R. A novel subset of human tumors that simultaneously overexpress multiple E2F-responsive genes found in breast, ovarian, and prostate cancers. Cancer Inform. 13, 89-100 (2014).
21. Engelmann, D. et al. E2F1 promotes angiogenesis through the VEGF-C/ VEGFR-3 axis in a feedback loop for cooperative induction of PDGF-B. J. Mol. Cell Biol. 5, 391-403 (2013).
22. Desmedt, C. et al. Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin. Cancer Res. 13, 3207-3214 (2007).
23. Rennhack, J. & Andrechek, E. Conserved E2F mediated metastasis in mouse models of breast cancer and HER2 positive patients. Oncoscience 2, 867-871 (2015).
24. Lamouille, S., Xu, J. & Derynck, R. Molecular mechanisms of epithelial-mesenchymal transition. Nat. Rev. Mol. Cell Biol. 15, 178-196 (2014).
25. Le, D.-H. & Kwon, Y.-K. GPEC: a Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection. Comput. Biol. Chem. 37, 17-23 (2012).
26. Le, D.-H. & Kwon, Y.-K. NetDS: a Cytoscape plugin to analyze the robustness of dynamics and feedforward/feedback loop structures of biological networks. Bioinformatics 27, 2767-2768 (2011).
27. Meier, C. et al. Association of RHAMM with E2F1 promotes tumour cell extravasation by transcriptional up-regulation of fibronectin. J. Pathol. 234, 351-364 (2014).
28. Calzone, L., Gelay, A., Zinovyev, A., Radvanyi, F. & Barillot, E. A comprehensive modular map of molecular interactions in RB/E2F pathway. Mol. Syst. Biol. 4, 173 (2008).
29. Guebel, D. V., Schmitz, U., Wolkenhauer, O. & Vera, J. Analysis of cell adhesion during early stages of colon cancer based on an extended multi-valued logic approach. Mol. Biosyst. 8, 1230-1242 (2012).
30. Matsuoka, Y. et al. A comprehensive map of the influenza A virus replication cycle. BMC Syst. Biol. 7, 97 (2013).
31. Oda, K., Matsuoka, Y., Funahashi, A. & Kitano, H. A comprehensive pathwaymap of epidermal growth factor receptor signaling. Mol. Syst. Biol. 1, 2005. 0010 (2005).
32. Wu, G., Zhu, L., Dent, J. E. & Nardini, C. A comprehensive molecular interaction map for rheumatoid arthritis. PLoS ONE 5, e10137 (2010).
33. Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838-47 (2016).
34. Steinway, S. N. et al. Network modeling of TGFb signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition reveals joint sonic hedgehog and Wnt pathway activation. Cancer Res. 74, 5963-5977 (2014).
35. Lu, J. et al. Network modelling reveals the mechanism underlying colitisassociated colon cancer and identifies novel combinatorial anti-cancer targets. Sci. Rep. 5, 14739 (2015).
36. Gursoy, A., Keskin, O. & Nussinov, R. Topological properties of protein interaction networks from a structural perspective. Biochem. Soc. Trans. 36, 1398-1403 (2008).
37. He, X. & Zhang, J. Why do hubs tend to be essential in protein networks? PLoS Genet. 2, e88 (2006).
38. Jeong, H., Mason, S. P., Barabási, A.-L. & Oltvai, Z. N. Lethality and centrality in protein networks. Nature 411, 41-42 (2001).
39. Barabási, A.-L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113 (2004).
40. Tyson, J. J. & Novák, B. Functional motifs in biochemical reaction networks. Annu. Rev. Phys. Chem. 61, 219-240 (2010).
41. Zhang, Y., Xuan, J., de Los Reyes, B. G., Clarke, R. & Ressom, H. W. Network motif-based identification of breast cancer susceptibility genes. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008, 5696-5699 (2008).
42. Wang, P., Lü, J. & Yu, X. Identification of important nodes in directed biological networks: a network motif approach. PLoS ONE 9, e106132 (2014).
43. Yeger-Lotem, E. et al. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction. Proc. Natl Acad. Sci. USA 101, 5934-5939 (2004).
44. Koschützki, D., Schwöbbermeyer, H. & Schreiber, F. Ranking of network elements based on functional substructures. J. Theor. Biol. 248, 471-479 (2007).
45. Eduati, F. et al. Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic models. Cancer Res. 77, 3364-3375 (2017).
46. Medici, D., Hay, E. D. & Goodenough, D. A. Cooperation between snail and LEF-1 transcription factors is essential for TGF-beta1-induced epithelial-mesenchymal transition. Mol. Biol. Cell 17, 1871-1879 (2006).
47. Tomlinson, D. C., Baxter, E. W., Loadman, P. M., Hull, M. A. & Knowles, M. A. FGFR1-induced epithelial to mesenchymal transition through MAPK/PLC/ COX-2-mediated mechanisms. PLoS ONE 7, e38972 (2012).
48. Rizwani, W., Schaal, C., Kunigal, S., Coppola, D. & Chellappan, S. Mammalian lysine histone demethylase KDM2A regulates E2F1-mediated gene transcription in breast cancer cells. PLoS ONE 9, e100888 (2014).
49. Dhasarathy, A., Phadke, D., Mav, D., Shah, R. R. & Wade, P. A. The transcription factors Snail and Slug activate the transforming growth factorbeta signaling pathway in breast cancer. PLoS ONE 6, e26514 (2011).
50. Bhargava, R. et al. EGFR gene amplification in breast cancer: correlation with epidermal growth factor receptor mRNA and protein expression and HER-2 status and absence of EGFR-activating mutations. Mod. Pathol. 18, 1027-1033 (2005).
51. Mooso, B. A. et al. The role of EGFR family inhibitors in muscle invasive bladder cancer: a review of clinical data and molecular evidence. J. Urol. 193, 19-29 (2015).
52. Chaux, A. et al. High epidermal growth factor receptor immunohistochemical expression in urothelial carcinoma of the bladder is not associated with EGFR mutations in exons 19 and 21: a study using formalin-fixed, paraffin-embedded archival tissues. Hum. Pathol. 43, 1590-1595 (2012).
53. Helsten, T. et al. The FGFR landscape in cancer: analysis of 4, 853 tumors by next-generation sequencing. Clin. Cancer Res. 22, 259-267 (2016).
54. Guagnano, V. et al. FGFR genetic alterations predict for sensitivity to NVP-BGJ398, a selective pan-FGFR inhibitor. Cancer Discov 2, 1118-1133 (2012).
55. Jang, M. et al. FGFR1 is amplified during the progression of in situ to invasive breast carcinoma. Breast Cancer Res. 14, R115 (2012).
56. Holdman, X. B. et al. Upregulation of EGFR signaling is correlated with tumor stroma remodeling and tumor recurrence in FGFR1-driven breast cancer. Breast Cancer Res. 17, 141 (2015).
57. Katoh, M. & Nakagama, H. FGF receptors: cancer biology and therapeutics. Med. Res. Rev. 34, 280-300 (2014).
58. Franceschini, A. et al. STRING v9. 1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808-15 (2013).
59. Keshava Prasad, T. S. et al. Human protein reference database-2009 update. Nucleic Acids Res. 37, D767-772 (2009).
60. Wingender, E. et al. TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res. 28, 316-319 (2000).
61. Hsu, S.-D. et al. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 39, D163-D169 (2011).
62. Wang, J., Lu, M., Qiu, C. & Cui, Q. TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res. 38, D119-D122 (2010).
63. Aranda, B. et al. PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat. Methods 8, 528-529 (2011).
64. Hoffmann, R. & Valencia, A. A gene network for navigating the literature. Nat. Genet. 36, 664-664 (2004).
65. Funahashi, A., Morohashi, M., Kitano, H. & Tanimura, N. CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. Biosilico 1, 159-162 (2003).
66. Le Novère, N. et al. The systems biology graphical notation. Nat. Biotechnol. 27, 735-741 (2009).
67. Kuperstein, I. et al. NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps. BMC Syst. Biol. 7, 100 (2013).
68. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504 (2003).
69. Assenov, Y., Ramírez, F., Schelhorn, S. E. S. E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinformatics 24, 282-284 (2008).
70. Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306-312 (2015).
71. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
72. Vera, J., González-Alcón, C., Marín-Sanguino, A. & Torres, N. Optimization of biochemical systems through mathematical programming: methods and applications. Comput. Oper. Res. 37, 1427-1438 (2010).
73. Khan, F. M. et al. Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic. Biochim. Biophys. Acta Proteins Proteomics 1844, 289-298 (2014).
74. Klamt, S., Saez-Rodriguez, J., Lindquist, J. A., Simeoni, L. & Gilles, E. D. A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinformatics 7, 56 (2006).
75. Terfve, C. et al. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst. Biol. 6, 133 (2012).