Epioncogene Networks: Identification of Epigenomic and Transcriptomic Cooperation by Multi-omics Integration of ChIP-Seq and RNA-Seq Data

  • Fabian Volker FilippEmail author
Part of the RNA Technologies book series (RNATECHN)


Next generation sequencing and systems biology have changed our understanding of oncogenesis. Master regulators at the transcriptional and epigenomic level have the ability to affect how an entire network of cancer genes behaves, and thereby taking on an oncogenic role. Further, epigenetic factors cooperate and team up with transcription factors to control specific gene target networks. Transcriptomics in combination with epigenomic profiling and measurement of chromatin accessibility enables global detection of epigenetic modifications and characterization of transcriptional and epigenetic footprints. Chromatin remodelers and transcription factors are in close communication via recognition of posttranslational histone modifications, DNA methylation marks, and sequence motifs to coordinate dynamic exchange of chromatin between open transcriptionally active conformations and compacted silences ones. Integration of complementary high-throughput sequencing platforms (HiC, DNAseI-Seq, MNase-Seq, FAIRE-Seq, ATAC-Seq, ChIP-Seq, ChIA-PET, TBS-Seq, WGBS-Seq, RNA-Seq, GRID-Seq) including chromatin higher-order structures, DNase hypersensitive sites, chromatin accessibility, histone modification, chromatin binding, and DNA methylation enables identification of cooperation and gene target networks. In cancer, due to the ability to team up with transcription factors, epigenetic factors concert mitogenic and metabolic gene networks claiming the role of a cancer master regulators or epioncogenes.


ATAC-Seq Cancer systems biology ChIA-PET ChIP-Seq Chromatin accessibility Coactivation Cooperation CpG DNAseI-Seq Epigenetics Epigenome Epigenomics Epioncogene FAIRE-Seq Gene set enrichment GRID-Seq HAT HDAC HiC Histone modification KDM KMT Master regulator MNase-Seq Motif enrichment Multi-omics Omics Precision medicine PRMT Regulome Resistance Rewiring RNA-Seq Target gene TBS-Seq Transcription factor target Transcriptomics Upstream regulator WGBS-Seq 



The power of systems biology comprises that information content of a network is greater than the sum of all individual nodes. The same principle applies to a team and highlights why teamwork is so valuable. To all students of the Systems Biology and Cancer Metabolism Laboratory, who taught me about friendship, selflessness, and—above all—unwavering loyalty. This work on cancer systems biology is generously supported by the National Institutes of Health and the National Science Foundation. F.V.F. is grateful for the support of grants CA154887 from the National Institutes of Health, National Cancer Institute, CRN-17-427258 by the University of California, Office of the President, Cancer Research Coordinating Committee, the Goethe Institute, Washington, DC, USA, and the Federal Foreign Office, Berlin, Germany.


  1. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106CrossRefPubMedPubMedCentralGoogle Scholar
  2. Ay A, Gong D, Kahveci T (2015) Hierarchical decomposition of dynamically evolving regulatory networks. BMC Bioinf 16:161CrossRefGoogle Scholar
  3. Bailey TL (2002) Discovering novel sequence motifs with MEME. Curr Protoc Bioinformatics Chapter 2:Unit 2.4. doi:10.1002/0471250953.bi0204s00Google Scholar
  4. Bailey TL, Gribskov M (1998) Combining evidence using p-values: application to sequence homology searches. Bioinformatics 14:48–54CrossRefPubMedGoogle Scholar
  5. Bailey TL, Johnson J, Grant CE et al (2015) The MEME suite. Nucleic Acids Res 43:W39–W49CrossRefPubMedPubMedCentralGoogle Scholar
  6. Barski A, Cuddapah S, Cui K et al (2007) High-resolution profiling of histone methylations in the human genome. Cell 129:823–837CrossRefPubMedGoogle Scholar
  7. Baylin SB, Makos M, Wu JJ et al (1991) Abnormal patterns of DNA methylation in human neoplasia: potential consequences for tumor progression. Cancer Cells 3:383–390PubMedGoogle Scholar
  8. Buenrostro JD, Giresi PG, Zaba LC et al (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10:1213–1218CrossRefPubMedPubMedCentralGoogle Scholar
  9. Buenrostro JD, Wu B, Litzenburger UM et al (2015) Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523:486–490CrossRefPubMedPubMedCentralGoogle Scholar
  10. Cheng C, Andrews E, Yan KK et al (2015) An approach for determining and measuring network hierarchy applied to comparing the phosphorylome and the regulome. Genome Biol 16:63CrossRefPubMedPubMedCentralGoogle Scholar
  11. Cock PJ, Fields CJ, Goto N et al (2010) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38:1767–1771CrossRefPubMedGoogle Scholar
  12. Cong L, Ran FA, Cox D et al (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339:819–823CrossRefPubMedPubMedCentralGoogle Scholar
  13. Corces MR, Trevino AE, Hamilton EG et al (2017) An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat Methods 14:959–962CrossRefPubMedPubMedCentralGoogle Scholar
  14. Crick F (1970) Central dogma of molecular biology. Nature 227:561–563CrossRefPubMedGoogle Scholar
  15. Edwards L, Gupta R, Filipp FV (2016) Hypermutation of DPYD deregulates pyrimidine metabolism and promotes malignant progression. Mol Cancer Res 14:196–206. CrossRefPubMedGoogle Scholar
  16. Feng J, Liu T, Zhang Y (2011) Using MACS to identify peaks from ChIP-Seq data. Curr Protoc Bioinf Chapter 2:Unit 2 14Google Scholar
  17. Fernandez JM, de la Torre V, Richardson D et al (2016) The BLUEPRINT data analysis portal. Cell Syst 3(491–495):e495Google Scholar
  18. Filipp FV, Ratnikov B, De Ingeniis J, Smith JW, Osterman AL, Scott DA (2012a) Glutamine-fueled mitochondrial metabolism is decoupled from glycolysis in melanoma. Pigment Cell Melanoma Res 25(6):732–739. CrossRefPubMedPubMedCentralGoogle Scholar
  19. Filipp FV, Scott DA, Ronai ZA, Osterman AL, Smith JW (2012b) Reverse TCA cycle flux through isocitrate dehydrogenases 1 and 2 is required for lipogenesis in hypoxic melanoma cells. Pigment Cell Melanoma Res 25(3):375–383. CrossRefPubMedPubMedCentralGoogle Scholar
  20. Filipp FV (2013a) Cancer metabolism meets systems biology: pyruvate kinase isoform PKM2 is a metabolic master regulator. J Carcinog 12:14. CrossRefPubMedPubMedCentralGoogle Scholar
  21. Filipp FV (2013b) A gateway between omics data and systems biology. J Metabolomics Syst Biol 1:1. PubMedPubMedCentralGoogle Scholar
  22. Filipp FV (2017a) Crosstalk between epigenetics and metabolism—Yin and Yang of histone demethylases and methyltransferases in cancer. Brief Funct Genomics 16:320–325.
  23. Filipp FV (2017b) Precision medicine driven by cancer systems biology. Cancer Metastasis Rev 36:91–108.
  24. Fonseca NA, Marioni J, Brazma A (2014) RNA-Seq gene profiling: a systematic empirical comparison. PLoS One 9:e107026Google Scholar
  25. Fullwood MJ, Liu MH, Pan YF et al (2009) An oestrogen-receptor-alpha-bound human chromatin interactome. Nature 462:58–64CrossRefPubMedPubMedCentralGoogle Scholar
  26. Gasiunas G, Barrangou R, Horvath P et al (2012) Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria. Proc Natl Acad Sci USA 109:E2579–E2586CrossRefPubMedGoogle Scholar
  27. Grant CE, Bailey TL, Noble WS (2011) FIMO: scanning for occurrences of a given motif. Bioinformatics 27:1017–1018CrossRefPubMedPubMedCentralGoogle Scholar
  28. Guan J, Gupta R, Filipp FV (2015) Cancer systems biology of TCGA SKCM: efficient detection of genomic drivers in melanoma. Sci Rep 5:7857. Scholar
  29. Hiltunen MO, Alhonen L, Koistinaho J et al (1997) Hypermethylation of the APC (adenomatous polyposis coli) gene promoter region in human colorectal carcinoma. Int J Cancer 70:644–648Google Scholar
  30. Ishino Y, Shinagawa H, Makino K et al (1987) Nucleotide sequence of the iap gene, responsible for alkaline phosphatase isozyme conversion in Escherichia coli, and identification of the gene product. J Bacteriol 169:5429–5433Google Scholar
  31. Issa JP, Ottaviano YL, Celano P et al (1994) Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nat Genet 7:536–540CrossRefPubMedGoogle Scholar
  32. Jinek M, Chylinski K, Fonfara I et al (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337:816–821CrossRefGoogle Scholar
  33. Johnson DS, Mortazavi A, Myers RM et al (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science 316:1497–1502CrossRefPubMedGoogle Scholar
  34. Kent WJ, Sugnet CW, Furey TS et al (2002) The human genome browser at UCSC. Genome Res 12:996–1006CrossRefPubMedPubMedCentralGoogle Scholar
  35. Kharchenko PV, Tolstorukov MY, Park PJ (2008) Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol 26:1351–1359CrossRefPubMedPubMedCentralGoogle Scholar
  36. Kondo Y, Shen L, Cheng AS et al (2008) Gene silencing in cancer by histone H3 lysine 27 trimethylation independent of promoter DNA methylation. Nat Genet 40:741–750CrossRefPubMedGoogle Scholar
  37. Kundaje A, Meuleman W, Ernst J et al (2015) Integrative analysis of 111 reference human epigenomes. Nature 518:317–330CrossRefPubMedPubMedCentralGoogle Scholar
  38. Laird PW, Jackson-Grusby L, Fazeli A et al (1995) Suppression of intestinal neoplasia by DNA hypomethylation. Cell 81:197–205CrossRefPubMedGoogle Scholar
  39. Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921CrossRefPubMedGoogle Scholar
  40. Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25CrossRefPubMedPubMedCentralGoogle Scholar
  41. Lanning NJ, Castle JP, Singh SJ et al (2017) Metabolic profiling of triple-negative breast cancer cells reveals metabolic vulnerabilities. Cancer Metab 5:6. 0168-xCrossRefPubMedPubMedCentralGoogle Scholar
  42. Li G, Chen Y, Snyder MP et al (2017a) ChIA-PET2: a versatile and flexible pipeline for ChIA-PET data analysis. Nucleic Acids Res 45:e4CrossRefPubMedGoogle Scholar
  43. Li X, Zhou B, Chen L et al (2017b) GRID-seq reveals the global RNA–chromatin interactome. Nat Biotechnol 35:940–950Google Scholar
  44. Lieberman-Aiden E, van Berkum NL, Williams L et al (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326:289–293Google Scholar
  45. Mali P, Yang L, Esvelt KM et al (2013) RNA-guided human genome engineering via Cas9. Science 339:823–826Google Scholar
  46. Maunakea AK, Nagarajan RP, Bilenky M et al (2010) Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466:253–257CrossRefPubMedPubMedCentralGoogle Scholar
  47. McLeay RC, Bailey TL (2010) Motif enrichment analysis: a unified framework and an evaluation on ChIP data. BMC Bioinf 11:165CrossRefGoogle Scholar
  48. Mortazavi A, Williams BA, McCue K et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628CrossRefPubMedGoogle Scholar
  49. Musselman CA, Lalonde ME, Cote J et al (2012) Perceiving the epigenetic landscape through histone readers. Nat Struct Mol Biol 19:1218–1227CrossRefPubMedPubMedCentralGoogle Scholar
  50. Pan Q, Shai O, Lee LJ et al (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40:1413–1415CrossRefPubMedGoogle Scholar
  51. Pearson WR, Lipman DJ (1988) Improved tools for biological sequence comparison. Proc Natl Acad Sci USA 85:2444–2448CrossRefPubMedGoogle Scholar
  52. Qi J, Filipp FV (2017) An epigenetic master regulator teams up to become an epioncogene. Oncotarget 8:29538–29539. Scholar
  53. Qu K, Zaba LC, Satpathy AT et al (2017) Chromatin accessibility landscape of Cutaneous T cell lymphoma and dynamic response to HDAC inhibitors. Cancer Cell 32(27-41):e24Google Scholar
  54. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841–842CrossRefPubMedPubMedCentralGoogle Scholar
  55. Rea S, Eisenhaber F, O’Carroll D et al (2000) Regulation of chromatin structure by site-specific histone H3 methyltransferases. Nature 406:593–599CrossRefPubMedGoogle Scholar
  56. Robinson MD, Smyth GK (2007) Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23:2881–2887CrossRefPubMedGoogle Scholar
  57. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140CrossRefPubMedGoogle Scholar
  58. Robinson JT, Thorvaldsdottir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26CrossRefPubMedPubMedCentralGoogle Scholar
  59. Sharrard RM, Royds JA, Rogers S et al (1992) Patterns of methylation of the c-myc gene in human colorectal cancer progression. Br J Cancer 65:667–672CrossRefPubMedPubMedCentralGoogle Scholar
  60. Shi Y, Lan F, Matson C et al (2004) Histone demethylation mediated by the nuclear amine oxidase homolog LSD1. Cell 119:941–953CrossRefPubMedGoogle Scholar
  61. Strahl BD, Allis CD (2000) The language of covalent histone modifications. Nature 403:41–45CrossRefPubMedGoogle Scholar
  62. Sultan M, Schulz MH, Richard H et al (2008) A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321:956–960CrossRefPubMedGoogle Scholar
  63. Taverna SD, Li H, Ruthenburg AJ et al (2007) How chromatin-binding modules interpret histone modifications: lessons from professional pocket pickers. Nat Struct Mol Biol 14:1025–1040CrossRefPubMedPubMedCentralGoogle Scholar
  64. Tehranchi AK, Myrthil M, Martin T et al (2016) Pooled ChIP-Seq links variation in transcription factor binding to complex disease risk. Cell 165:730–741CrossRefPubMedPubMedCentralGoogle Scholar
  65. Tiffen JC, Gunatilake D, Gallagher SJ et al (2015) Targeting activating mutations of EZH2 leads to potent cell growth inhibition in human melanoma by derepression of tumor suppressor genes. Oncotarget 6:27023-27036.
  66. Tiffen J, Wilson S, Gallagher SJ et al (2016a) Somatic copy number amplification and hyperactivating somatic mutations of EZH2 correlate with DNA methylation and drive epigenetic silencing of genes involved in tumor suppression and immune responses in melanoma. Neoplasia 18:121–132. CrossRefPubMedPubMedCentralGoogle Scholar
  67. Tiffen JC, Gallagher SJ, Tseng HY et al (2016b) EZH2 as a mediator of treatment resistance in melanoma. Pigment Cell Melanoma Res 29:500–507. Scholar
  68. Timp W, Feinberg AP (2013) Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat Rev Cancer 13:497–510Google Scholar
  69. Torres-Ruiz R, Rodriguez-Perales S (2017) CRISPR-Cas9 technology: applications and human disease modelling. Brief Funct Genomics 16:4–12Google Scholar
  70. Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515CrossRefPubMedPubMedCentralGoogle Scholar
  71. Trapnell C, Roberts A, Goff L et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7:562–578CrossRefPubMedPubMedCentralGoogle Scholar
  72. Trapnell C, Hendrickson DG, Sauvageau M et al (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31:46–53CrossRefPubMedGoogle Scholar
  73. Turner BM (1993) Decoding the nucleosome. Cell 75:5–8CrossRefPubMedGoogle Scholar
  74. Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304–1351CrossRefPubMedGoogle Scholar
  75. Wang H, Yang H, Shivalila CS et al (2013) One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153:910–918CrossRefPubMedPubMedCentralGoogle Scholar
  76. Whetstine JR, Nottke A, Lan F et al (2006) Reversal of histone lysine trimethylation by the JMJD2 family of histone demethylases. Cell 125:467–481CrossRefPubMedGoogle Scholar
  77. Wilson S, Qi J, Filipp FV (2016) Refinement of the androgen response element based on ChIP-Seq in androgen-insensitive and androgen-responsive prostate cancer cell lines. Sci Rep 6:32611. Scholar
  78. Wilson S, Fan L, Sahgal N et al (2017) The histone demethylase KDM3A regulates the transcriptional program of the androgen receptor in prostate cancer cells. Oncotarget 8:30328–30343. Scholar
  79. Wilson S, Filipp FV (2018) A network of epigenomic and transcriptional cooperation encompassing an epigenomic master regulator in cancer. NPJ Syst Biol Appl 4:24. CrossRefPubMedPubMedCentralGoogle Scholar
  80. Yamane K, Toumazou C, Tsukada Y et al (2006) JHDM2A, a JmjC-containing H3K9 demethylase, facilitates transcription activation by androgen receptor. Cell 125:483–495CrossRefPubMedGoogle Scholar
  81. Zecena H, Tveit D, Wang Z, Farhat A, Panchal P, Liu J, Singh SJ, Sanghera A, Bainiwal A, Teo SY, Meyskens FL Jr, Liu-Smith F, Filipp FV (2018) Systems biology analysis of mitogen activated protein kinase inhibitor resistance in malignant melanoma. BMC Syst Biol 12 (1):33.
  82. Zentner GE, Henikoff S (2013) Regulation of nucleosome dynamics by histone modifications. Nat Struct Mol Biol 20:259–266CrossRefPubMedGoogle Scholar
  83. Zhang Y, Liu T, Meyer CA et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9:R137CrossRefPubMedPubMedCentralGoogle Scholar
  84. Zheng W, Zhao H, Mancera E et al (2010) Genetic analysis of variation in transcription factor binding in yeast. Nature 464:1187–1191CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Systems Biology and Cancer MetabolismUniversity of California MercedMercedUSA

Personalised recommendations