Search for Master Regulators in Walking Cancer Pathways

Part of the Methods in Molecular Biology book series (MIMB, volume 1613)


In this chapter, we present an approach that allows a causal analysis of multiple “-omics” data with the help of an “upstream analysis” strategy. The goal of this approach is to identify master regulators in gene regulatory networks as potential drug targets for a pathological process. The data analysis strategy includes a state-of-the-art promoter analysis for potential transcription factor (TF)-binding sites using the TRANSFAC® database combined with an analysis of the upstream signal transduction pathways that control the activity of these TFs. When applied to genes that are associated with a switch to a pathological process, the approach identifies potential key molecules (master regulators) that may exert major control over and maintenance of transient stability of the pathological state. We demonstrate this approach on examples of analysis of multi-omics data sets that contain transcriptomics and epigenomics data in cancer. The results of this analysis helped us to better understand the molecular mechanisms of cancer development and cancer drug resistance. Such an approach promises to be very effective for rapid and accurate identification of cancer drug targets with true potential. The upstream analysis approach is implemented as an automatic workflow in the geneXplain platform ( using the open-source BioUML framework (

Key words

Upstream analysis Promoter analysis Pathway analysis Microarray data ChIP-seq RNA-seq Pathway rewiring 



This work was supported by a grant of the Federal Targeted Program “Research and development on priority directions of science and technology in Russia, 2014–2020,” grant number: 14.604.21.0101 to the Institute of Chemical Biology and Fundamental Medicine, Novosibirsk, Russia. This work was also supported by the following grants of the EU FP7 program: “SysMedIBD,” “RESOLVE,” and “MIMOMICS.” We are also very grateful to my colleague at the former Biobase GmbH, Niko Voss, for ideas on the algorithm on pathway analysis; my colleagues from Dr. Tagir Valeev and Dr. Fedor Kolpakov for development of the BioUML framework; my colleagues at geneXplain: Dr. Holger Michael for the critical reading of the manuscript, Philip Stegmaier for development of the algorithms of TF site analysis, Dr. Jeannette Koschmann for creation workflows, Dr. Olga Kel-Margoulis and Prof. Edgar Wingender for the fruitful discussions of the work described here.

Conflicts of Interest: AK is an employee of geneXplain GmbH, which maintains and distributes the BioUML/geneXplain platform used in this study.


  1. 1.
    Sanyal AJ, Yoon SK, Lencioni R (2010) The etiology of hepatocellular carcinoma and consequences for treatment. Oncologist 15(Suppl. 4):14–22CrossRefPubMedGoogle Scholar
  2. 2.
    Colussi D, Brandi G, Bazzoli F, Ricciardiello L (2013) Molecular pathways involved in colorectal cancer: implications for disease behavior and prevention. Int J Mol Sci 14:16365–16385CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674CrossRefPubMedGoogle Scholar
  4. 4.
    Guinney J, Dienstmann R et al (2015) The consensus molecular subtypes of colorectal cancer. Nat Med 21:1350–1356CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Carro MS, Lim WK, Alvarez MJ et al (2010) The transcriptional network for mesenchymal transformation of brain tumours. Nature 463:318–325CrossRefPubMedGoogle Scholar
  6. 6.
    Kolesnikov N, Hastings E, Keays M et al (2015) ArrayExpress update—simplifying data submissions. Nucleic Acids Res 43:D1113–D1116CrossRefPubMedGoogle Scholar
  7. 7.
    Barrett T, Wilhite SE, Ledoux P et al (2013) NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res 41:D991–D995CrossRefPubMedGoogle Scholar
  8. 8.
    Petryszak R, Burdett T, Fiorelli B et al (2014) Expression atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments. Nucleic Acids Res 42:D926–D932CrossRefPubMedGoogle Scholar
  9. 9.
    Smith CM, Finger JH, Hayamizu TF et al (2014) The mouse Gene expression database (GXD): 2014 update. Nucleic Acids Res 42:D818–D824CrossRefPubMedGoogle Scholar
  10. 10.
    Fu J, Allen W, Xia A, Ma Z, Qi X (2014) Identification of biomarkers in breast cancer by Gene expression profiling using human tissues. Genom Data 2:299–301CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    de Gramont A, Watson S, Ellis LM et al (2015) Pragmatic issues in biomarker evaluation for targeted therapies in cancer. Nat Rev Clin Oncol 12(4):197–212. doi: 10.1038/nrclinonc.2014.202 CrossRefPubMedGoogle Scholar
  12. 12.
    Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550CrossRefPubMedCentralGoogle Scholar
  13. 13.
    Kanehisa M, Goto S, Sato Y et al (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114CrossRefPubMedGoogle Scholar
  14. 14.
    Kel A, Voss N, Jauregui R, Kel-Margoulis O, Wingender E (2006) Beyond microarrays: find key transcription factors controlling signal transduction pathways. BMC Bioinformatics 7:S13CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Michael H, Hogan J, Kel A, Kel-Margoulis O, Schacherer F, Voss N, Wingender E (2008) Building a knowledge base for systems pathology. Brief Bioinform 9:518–531CrossRefPubMedGoogle Scholar
  16. 16.
    Stegmaier P, Voss N, Meier T, Kel A, Wingender E, Borlak J (2011) Advanced computational biology methods identify molecular switches for malignancy in an EGF mouse model of liver cancer. PLoS One 6:e17738CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Koschmann J, Bhar A, Stegmaier P, Kel AE, Wingender E (2015) “Upstream analysis”: an integrated promoter-pathway analysis approach to causal interpretation of microarray data. Microarrays 4:270–286. doi: 10.3390/microarrays4020270 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Wingender E (2008) The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief Bioinform 9:326–332CrossRefPubMedGoogle Scholar
  19. 19.
    Kel AE, Gössling E, Reuter I, Cheremushkin E, Kel-Margoulis OV, Wingender E (2003) MATCH: a tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res 31:3576–3579CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Waleev T, Shtokalo D, Konovalova T, Voss N, Cheremushkin E, Stegmaier P, Kel-Margoulis O, Wingender E, Kel A (2006) Composite module analyst: identification of transcription factor binding site combinations using genetic algorithm. Nucleic Acids Res 34(Web Server issue):W541–W545CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Krull M, Pistor S, Voss N, Kel A, Reuter I, Kronenberg D, Michael H, Schwarzer K, Potapov A, Choi C, Kel-Margoulis O, Wingender E (2006) TRANSPATH: an information resource for storing and visualizing signaling pathways and their pathological aberrations. Nucleic Acids Res 34:D546–D551CrossRefPubMedGoogle Scholar
  22. 22.
    Kel A, Stegmaier P, Valeev T, Koschmann J, Kel-Margoulis O, Wingender E (2016) Multi-omics “upstream analysis” of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer. EuPA Open Proteomics 13:1–13CrossRefGoogle Scholar
  23. 23.
    Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW (2013) Cancer genome landscapes. Science 339(6127):1546–1558. doi: 10.1126/science.1235122 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Osborn MJ, Freeman M, Huennekens FM (1958) Inhibition of dihydrofolic reductase by aminopterin and amethopterin. Proc Soc Exp Blot Med 97:429CrossRefGoogle Scholar
  25. 25.
    Morales C, Ribas M, Aiza G, Peinado MA (2005) Genetic determinants of methotrexate responsiveness and resistance in colon cancer cells. Oncogene 24(45):6842–6847CrossRefPubMedGoogle Scholar
  26. 26.
    Messier T, Jonathan G, Boyd J, Tye C, Browne G, Stein J, Lian J, Stein G (2016) Histone H3 lysine 4 acetylation and methylation dynamics define breast cancer subtypes. Oncotarget 7(5):5094–5109. doi: 10.18632/oncotarget.6922 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and computational biology solutions using R and Bioconductor. Springer, New York, pp 397–420CrossRefGoogle Scholar
  28. 28.
    Selga E, Morales C, Noé V, Peinado MA et al (2008) Role of caveolin 1, E-cadherin, Enolase 2 and PKCalpha on resistance to methotrexate in human HT29 colon cancer cells. BMC Med Genet 1:35Google Scholar
  29. 29.
    Allen BL, Taatjes DJ (2015) The mediator complex: a central integrator of transcription. Nat Rev Mol Cell Biol 16:155–166CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Zhang Y, Liu T, Meyer CA et al (2008) Model-based analysis of ChIP-seq (MACS). Genome Biol 9(9):R137. doi: 10.1186/gb-2008-9-9-r137 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Dijkstra EW (1959) A note on two problems in connexion with graphs, vol 1. Numerische Mathematik, Mathematisch Centrum, Amsterdam, pp 269–271Google Scholar
  33. 33.
    Viemann D, Goebeler M, Schmid S et al (2004) Transcriptional profiling of IKK2/NF-kappa B- and p38 MAP kinase-dependent gene expression in TNF-alpha-stimulated primary human endothelial cells. Blood 103:3365–3373CrossRefPubMedGoogle Scholar
  34. 34.
    Schimke RT, Kaufman RS, Alt FW, Kellems RF (1978) Gene amplification and drug resistance in cultured murine cells. Science 202:1051CrossRefPubMedGoogle Scholar
  35. 35.
    Bertino JR, Göker E, Gorlick R, Li WW, Banerjee D (1996) Resistance mechanisms to methotrexate in tumors. Oncologist 1(4):223–226PubMedGoogle Scholar
  36. 36.
    Firestein R, Bass AJ, Kim SY et al (2008) CDK8 is a colorectal cancer oncogene that regulates beta-catenin activity. Nature 455(7212):547–551. doi: 10.1038/nature07179 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Koike T, Shimada T, Fujii Y et al (2007) Up-regulation of TFF1 (pS2) expression by TNF-alpha in gastric epithelial cells. J Gastroenterol Hepatol 22(6):936–942CrossRefPubMedGoogle Scholar
  38. 38.
    Good L, Dimri GP, Campisi J, Chen KY (1996) Regulation of dihydrofolate reductase gene expression and E2F components in human diploid fibroblasts during growth and senescence. J Cell Physiol 168(3):580–588CrossRefPubMedGoogle Scholar
  39. 39.
    Lin SY, Black AR, Kostic D, Pajovic S, Hoover CN, Azizkhan JC (1996) Cell cycle-regulated association of E2F1 and Sp1 is related to their functional interaction. Mol Cell Biol 16(4):1668–1675CrossRefPubMedCentralGoogle Scholar
  40. 40.
    Kel-Margoulis OV, Kel AE, Reuter I, Deineko IV, Wingender E (2002) TRANSCompel: a database on composite regulatory elements in eukaryotic genes. Nucleic Acids Res 30(1):332–334CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Zwang Y, Oren M, Yarden Y (2012) Consistency test of the cell cycle: roles for p53 and EGR1. Cancer Res 72:1051–1054CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Hoesel B, Schmid JA (2013) The complexity of NF-κB signaling in inflammation and cancer. Mol Cancer 12:86. doi: 10.1186/1476-4598-12-86 CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Kundu M, Guermah M, Roeder RG, Amini S, Khalili K (1997) Interaction between cell cycle regulator, E2F-1, and NF-kappaB mediates repression of HIV-1 gene transcription. J Biol Chem 272(47):29468–29474CrossRefGoogle Scholar
  44. 44.
    Pandolfi S, Montagnani V, Lapucci A, Stecca B (2015) HEDGEHOG/GLI-E2F1 axis modulates iASPP expression and function and regulates melanoma cell growth. Cell Death Differ 22(12):2006–2019. doi: 10.1038/cdd.2015.56 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Oshimori N, Oristian D, Fuchs E (2015) TGF-β promotes heterogeneity and drug resistance in squamous cell carcinoma. Cell 160(5):963–976. doi: 10.1016/j.cell.2015.01.043 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Mostovich LA, Prudnikova TY, Kondratov AG et al (2011) Integrin alpha9 (ITGA9) expression and epigenetic silencing in human breast tumors. Cell Adh Migr 5(5):395–401. doi: 10.4161/cam.5.5.17949 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Hatakeyama H, Parker J, Wheeler D, Harari P, Levy S, Chung CH (2009) Effect of insulin-like growth factor 1 receptor inhibitor on sensitization of head and neck cancer cells to cetuximab and methotrexate. J Clin Oncol ASCO Annual Meeting Proceedings (Post-Meeting Edition) 27(15S):6079Google Scholar
  48. 48.
    Gevaert O, Plevritis S (2013) Identifying master regulators of cancer and their downstream targets by integrating genomic and epigenomic features. In: Proceedings of Pacific Symposium Biocomputing, USA, pp 123–134Google Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Institute of Chemical Biology and Fundamental Medicine, SBRANNovosibirskRussia
  2., Ltd.NovosibirskRussia
  3. 3.geneXplain GmbHWolfenbüttelGermany

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