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Systems Biology and Integrative Omics in Breast Cancer

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Omics Approaches in Breast Cancer

Abstract

Breast cancer is a complex pathology. The molecular origins of the disease can be traced back to DNA genomic alterations, gene expression deregulation, hormone disruption, metabolic abnormalities, protein failure, and signaling pathway alterations. Lifestyle and other exogenous influences may also modulate the onset, development, and outcome of breast carcinomas and their metastatic events. High-throughput omic technologies provide us with unprecedented tools to study such alterations at an extremely detailed level and have been established thus as essential instruments both in basic and clinical research and in translational medicine and therapeutics. A number of challenges arise when we consider how to interpret and optimize the results obtained from studying the data produced in such massive experiments. Considering this along with the multidimensional nature of the disease calls for new ways of reasoning. One of these new paradigms, maybe even the more relevant of them, is given by systems biology. Systems biology is the name given to the study of biological systems (such as cells, tissues, etc.) when we consider them as integrated units whose constituents parts interact, often in a complex nonlinear fashion. In this chapter, we will consider a number of successful systems biology approaches to breast cancer, firmly founded on the use and integration of data generated in high-throughput omic experiments.

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References

  1. Visvanathan M, Baumgartner C, Tilg B, Lushington GH. Systems biology approach for mapping TNFα-NFκB mathematical model to a protein interaction map. Open Syst Biol J. 2010;3(1):1–8.

    CAS  Google Scholar 

  2. Jin VX, O’Geen H, Iyengar S, Green R, Farnham PJ. Identification of an OCT4 and SRY regulatory module using integrated computational and experimental genomics approaches. Genome Res. 2007;17:807–17.

    PubMed  CAS  PubMed Central  Google Scholar 

  3. You L. Toward computational systems biology. Cell Biochem Biophys. 2004;40(2):167–84.

    PubMed  CAS  Google Scholar 

  4. Kitano H. Computational systems biology. Nature. 2002;420:206–10.

    PubMed  CAS  Google Scholar 

  5. Hernández Patiño CE, Jaime-Muñoz G, Resendis-Antonio O. Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells. Front Physiol. 2012;3:481. doi:10.3389/fphys.2012.00481.

    PubMed  PubMed Central  Google Scholar 

  6. Tretyakov K, Laur S, Smant G, Vilo J, Prins P. Fast probabilistic file fingerprinting for big data. BMC Genomics. 2013;14 Suppl 2:S8. doi:10.1186/1471-2164-14-S2-S8.

    PubMed  PubMed Central  Google Scholar 

  7. Schouten P. Big data in health care. Healthc Financ Manage. 2013;67(2):40–2.

    PubMed  Google Scholar 

  8. Baca-López K, Mayorga M, Hidalgo-Miranda A, Gutiérrez-Nájera N, Hernández-Lemus E. The role of master regulators in the metabolic/transcriptional coupling in breast carcinomas. PLoS One. 2012;7(8):e42678. doi:10.1371/journal.pone.0042678.

    PubMed  PubMed Central  Google Scholar 

  9. Critchley-Thorne RJ, Miller SM, Taylor DL, Lingle WL. Applications of cellular systems biology in breast cancer patient stratification and diagnostics. Comb Chem High Throughput Screen. 2009;12(9):860–9.

    PubMed  CAS  Google Scholar 

  10. Xu Y, Hu W, Chang Z, Duanmu H, Zhang S, Li Z, et al. Prediction of human protein-protein interaction by a mixed Bayesian model and its application to exploring underlying cancer-related pathway crosstalk. J R Soc Interface. 2011;8(57):555–67. doi:10.1098/rsif.2010.0384.

    PubMed  PubMed Central  Google Scholar 

  11. Ventura AC, Jackson TL, Merajver SD. On the role of cell signaling models in cancer research. Cancer Res. 2009;69(2):400–2. doi:10.1158/0008-5472.CAN-08-4422.

    PubMed  CAS  Google Scholar 

  12. Ernst J, Vainas O, Harbison CT, Simon I, Bar-Joseph Z. Reconstructing dynamic regulatory maps. Mol Syst Biol. 2007;3:74.

    PubMed  PubMed Central  Google Scholar 

  13. Itadani H, Mizuarai S, Kotani H. Can systems biology understand pathway activation? Gene expression signatures as surrogate markers for understanding the complexity of pathway activation. Curr Genomics. 2008;9(5):349–60.

    PubMed  CAS  PubMed Central  Google Scholar 

  14. Entschladen F, Palm D, Drell TLT, Lang K, Zaenker KS. Connecting a tumor to the environment. Curr Pharm Des. 2007;13(33):3440–4.

    PubMed  CAS  Google Scholar 

  15. Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–52.

    PubMed  CAS  Google Scholar 

  16. Faratian D. Systems pathology. Breast Cancer Res. 2010;12 Suppl 4:S4.

    PubMed  PubMed Central  Google Scholar 

  17. Mosca E, Alfieri R, Merelli I, Viti F, Calabria A, Milanesi L. A multilevel data integration resource for breast cancer study. BMC Syst Biol. 2010;4:76. doi:10.1186/1752-0509-4-76.

    PubMed  PubMed Central  Google Scholar 

  18. Szabo C, Masiello A, Ryan JF, Brody LC. The breast cancer information core: database design, structure, and scope. Hum Mutat. 2000;16(2):123–31.

    PubMed  CAS  Google Scholar 

  19. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006;34:D354–7.

    PubMed  CAS  PubMed Central  Google Scholar 

  20. Emmert-Streib F, Tripathi S, de Matos Simoes R, Hawwa AF, Dehmer M. The human disease network opportunities for classification, diagnosis and prediction of disorders and disease genes. Syst Biomed. 2012;1(1):1–8.

    Google Scholar 

  21. Brunet JP, Tamayo P, Golub TR, Mesirov JP. Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci U S A. 2004;101:4164–9.

    PubMed  CAS  PubMed Central  Google Scholar 

  22. Lefebvre C, Rajbhandari P, Alvarez MJ, Bandaru P, Lim WK, Sato M, et al. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol Syst Biol. 2010;6:377. doi:10.1038/msb.2010.31.

    PubMed  PubMed Central  Google Scholar 

  23. Wang X, Gotoh O. Inference of cancer-specific gene regulatory networks using soft computing rules. Gene Regul Syst Biol. 2010;4:19–34.

    CAS  Google Scholar 

  24. Rosenthal DT, Merajver SD. Rethinking the war on cancer: multidisciplinary collaborations between biologists and physical scientists. Future Oncol. 2012;8(4):339–41. doi:10.2217/fon.12.13.

    PubMed  Google Scholar 

  25. Kitano H. Cancer as a robust system: implications for anticancer therapy. Nat Rev Cancer. 2004;4(3):227–35.

    PubMed  CAS  Google Scholar 

  26. Tonon G. From oncogene to network addiction: the new frontier of cancer genomics and therapeutics. Future Oncol. 2008;4(4):569–77.

    PubMed  CAS  Google Scholar 

  27. Malumbres M. miRNAs versus oncogenes: the power of social networking. Mol Syst Biol. 2012;8:569. doi:10.1038/msb.2012.2.

    PubMed  PubMed Central  Google Scholar 

  28. Lim WK, Lyashenko E, Califano A. Master regulators used as breast cancer metastasis classifier. Pac Symp Biocomput. 2009;14:504–15.

    Google Scholar 

  29. Locasale JW. Metabolic rewiring drives resistance to targeted cancer therapy. Mol Syst Biol. 2012;8:597. doi:10.1038/msb.2012.30.

    PubMed  PubMed Central  Google Scholar 

  30. Tennant DA, Durán RV, Gottlieb E. Targeting metabolic transformation for cancer therapy. Nat Rev Cancer. 2010;10(4):267–77.

    PubMed  CAS  Google Scholar 

  31. Sethi JK, Vidal-Puig A. Wnt signalling and the control of cellular metabolism. Biochem J. 2010;427(1):1–17.

    PubMed  CAS  Google Scholar 

  32. Jones RG, Thompson CB. Tumor suppressors and cell metabolism: a recipe for cancer growth. Genes Dev. 2009;23:537–48.

    PubMed  CAS  PubMed Central  Google Scholar 

  33. Levine AJ, Puzio-Kuter AM. The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science. 2010;330:1340–4.

    PubMed  CAS  Google Scholar 

  34. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian Cellular context. BMC Bioinformatics. 2006;7 Suppl 1:S7. doi:10.1186/1471-2105-7-S1-S7.

    PubMed  PubMed Central  Google Scholar 

  35. Hernández-Lemus E, Velázquez-Fernández D, Estrada-Gil JK, Silva-Zolezzi I, Herrera-Hernández MF, et al. Information theoretical methods to deconvolute genetic regulatory networks applied to thyroid neoplasms. Physica A. 2009;388:5057–69.

    Google Scholar 

  36. Hernández-Lemus E. Non-equilibrium thermodynamics of gene expression and transcriptional regulation. J Nonequilib Thermodyn. 2009;34(4):371–94.

    Google Scholar 

  37. Hernández-Lemus E. Non-equilibrium thermodynamics of transcriptional bursts. In: Macías A, Dagdug L, editors. New trends in statistical physics: Festschrift in honor of Leopoldo García-Colín’s 80th birthday. Singapore: World Scientific; 2010.

    Google Scholar 

  38. National Center for Biotechnology Information: gene expression omnibus. http://www.ncbi.nlm.nih.gov/geo/. Accessed 17 July 2012.

  39. Reactome: An open-source, open access, manually curated and peer-reviewed pathway database. http://www.reactome.org. Accessed 17 July 2012.

  40. Subramanian A, Tamayo P, Mootha KV, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.

    PubMed  CAS  PubMed Central  Google Scholar 

  41. Broad Institute of MIT and Harvard: Gene Set Enrichment Analysis. http://www.broadinstitute.org/gsea/downloads.jsp. Accessed 17 July 2012.

  42. Broad Institute of MIT and Harvard: Molecular Signature Database. http://www.broadinstitute.org/gsea/msigdb/collections.jsp. Accessed 17 July 2012.

  43. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6.

    Google Scholar 

  44. Farmer P, Bonnefoi H, Becette V, Tubiana-Hulin M, Fumoleau P, Larsimont D, et al. Identification of molecular apocrine breast tumours by microarray analysis. Oncogene. 2005;24(29):4660–71.

    PubMed  CAS  Google Scholar 

  45. Pau Ni IB, Zakaria Z, Muhammad R, Abdullah N, Ibrahim N, Aina Emran N, et al. Gene expression patterns distinguish breast carcinomas from normal breast tissues: the Malaysian context. Pathol Res Pract. 2010;206(4):223–8.

    PubMed  Google Scholar 

  46. Ruckhaberle E, Rody A, Engels K, Gaetje R, von Minckwitz G, Schiffmann S, et al. Microarray analysis of altered sphingolipid metabolism reveals prognostic significance of sphingosine kinase 1 in breast cancer. Breast Cancer Res Treat. 2008;112:41–52. doi:10.1007/s10549-007-9836-9.

    PubMed  Google Scholar 

  47. Tripathi A, King C, de la Morenas A, Perry VK, Burke B, Antoine GA, et al. Gene expression abnormalities in histologically normal breast epithelium of breast cancer patients. Int J Cancer. 2008;122(7):1557–66.

    PubMed  CAS  Google Scholar 

  48. Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, et al. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci U S A. 2002;99:12963–8.

    PubMed  CAS  PubMed Central  Google Scholar 

  49. Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006;98:262–72.

    PubMed  CAS  Google Scholar 

  50. Satih S, Chalabi N, Rabiau N, Bosviel R, Fontana L, Bignon YJ, et al. Gene expression profiling of breast cancer cell lines in response to soy isoflavones using a pangenomic microarray approach. OMICS. 2010;14(3):231–8. doi:10.1089/omi.2009.0124.

    PubMed  CAS  PubMed Central  Google Scholar 

  51. Liu R, Wang X, Chen GY, Dalerba P, Gurney A, Hoey T, et al. The prognostic role of a gene signature from tumorigenic breast-cancer cells. N Engl J Med. 2007;356(3):217–26.

    PubMed  CAS  Google Scholar 

  52. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A. 2003;100:8418–23.

    PubMed  CAS  PubMed Central  Google Scholar 

  53. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365:671–9.

    PubMed  CAS  Google Scholar 

  54. Carroll JS, Meyer CA, Song J, Li W, Geistlinger TR, Eeckhoute J, et al. Genome-wide analysis of estrogen receptor binding sites. Nat Genet. 2006;38:1289–97.

    PubMed  CAS  Google Scholar 

  55. Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M. Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics. 2010;6(1):78–95.

    PubMed  CAS  PubMed Central  Google Scholar 

  56. Tworoger SS, Eliassen AH, Kelesidis T. Plasma adiponectin concentrations and risk of incident breast cancer. J Clin Endocrinol Metab. 2007;92:1510–6.

    PubMed  CAS  Google Scholar 

  57. Teiten MH, Gaigneaux A, Chateauvieux S, Billing AM, Planchon S, Fack F, et al. Identification of differentially expressed proteins in curcumin-treated prostate cancer cell lines. OMICS. 2012;16(6):289–300. doi:10.1089/omi.2011.0136.

    PubMed  CAS  Google Scholar 

  58. Hicks J, Krasnitz A, Lakshmi B, Navin NE, Riggs M, Leibu E, et al. Novel patterns of genome rearrangement and their association with survival in breast cancer. Genome Res. 2006;16:1465–79.

    PubMed  CAS  PubMed Central  Google Scholar 

  59. Bachman KE, Argani P, Samuels Y, Silliman N, Ptak J, Szabo S, et al. The PIK3CA gene is mutated with high frequency in human breast cancers. Cancer Biol Ther. 2004;3:772–5.

    PubMed  CAS  Google Scholar 

  60. Fu Y, Sun Y, Li Y, Li J, Rao X, Chen C, et al. Differential genome-wide profiling of tandem 30 UTRs among human breast cancer and normal cells by high-throughput sequencing. Genome Res. 2011;21:741–7.

    PubMed  CAS  PubMed Central  Google Scholar 

  61. Andre F, Job B, Dessen P, Tordai A, Michiels S, Liedtke C, et al. Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array. Clin Cancer Res. 2009;15:441–51.

    PubMed  CAS  Google Scholar 

  62. Baca-López K, Correa-Rodríguez MD, Flores-Espinosa R, Garcia-Herrera R, Hernández-Armenta CI, Hidalgo-Miranda A, et al. A three-state model for multidimensional genomic data integration. In: Proceedings of the ninth international conference for the Critical Assessment of Massive Data Analysis, CAMDA. 2011. Available from: http://camda.bioinfo.cipf.es/camda2012/_media/camda2011_baca-lopez.pdf.

  63. Sun Z, Asmann YW, Kalari KR, Bot B, Eckel-Passow JE, Baker TR, et al. Integrated analysis of gene expression, CpG island methylation, and gene copy number in breast cancer cells by deep sequencing. PLoS One. 2011;6:e17490.

    PubMed  CAS  PubMed Central  Google Scholar 

  64. Leary RJ, Lin JC, Cummins J, Boca S, Wood LD, Parsons DW, et al. Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers. Proc Natl Acad Sci U S A. 2008;105:16224–9.

    PubMed  CAS  PubMed Central  Google Scholar 

  65. Baca-López K, Hernández-Lemus E, Mayorga M. Information-theoretical analysis of gene expression to infer transcriptional interactions. Revista Mexicana de Fsica. 2009;55(6):456–66.

    Google Scholar 

  66. Dexter TJ, Sims D, Mitsopoulos C, Mackay A, Grigoriadis A, Ahmad AS, et al. Genomic distance entrained clustering and regression modelling highlights interacting genomic regions contributing to proliferation in breast cancer. BMC Syst Biol. 2010;4:127. doi:10.1186/1752-0509-4-127.

    PubMed  PubMed Central  Google Scholar 

  67. Evans SC, Kourtidis A, Markham TS, Miller J, Conklin DS, Torres AS. MicroRNA target detection and analysis for genes related to breast cancer using MDLcompress. EURASIP J Bioinform Syst Biol. 2007. doi:10.1155/2007/43670.

    PubMed  PubMed Central  Google Scholar 

  68. Shi Z, Derow CK, Zhang B. Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression. BMC Syst Biol. 2010;4:74. doi:10.1186/1752-0509-4-74.

    PubMed  PubMed Central  Google Scholar 

  69. Mosca E, Bertoli G, Piscitelli E, Vilardo L, Reinbold RA, Zucchi I, et al. Identification of functionally related genes using data mining and data integration: a breast cancer case study. BMC Bioinformatics. 2009;10:S8.

    PubMed  PubMed Central  Google Scholar 

  70. Rosen LS, Ashurst HL, Chap L. Targeting signal transduction pathways in metastatic breast cancer: a comprehensive review. Oncologist. 2010;15:216–35.

    PubMed  CAS  PubMed Central  Google Scholar 

  71. Tran LM, Zhang B, Zhang Z, Zhang C, Xie T, Lamb JR, et al. Inferring causal genomic alterations in breast cancer using gene expression data. BMC Syst Biol. 2011. doi:10.1186/1752-0509-5-121.

    PubMed  PubMed Central  Google Scholar 

  72. Ein-Dor L, Kela I, Getz G, Givol D, Domany E. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics. 2005;21:171–8.

    PubMed  CAS  Google Scholar 

  73. Tabchy A, Hennessy BT, Hortobagyi G, Mills GB. Systems biology of breast cancer. Curr Breast Cancer Rep. 2009;1:238–45.

    CAS  Google Scholar 

  74. Wang J, Chen G, Li M, Pan Y. Integration of breast cancer gene signatures based on graph centrality. BMC Syst Biol. 2011. doi:10.1186/1752-0509-5-S3-S10.

    Google Scholar 

  75. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009;27:199–204.

    PubMed  CAS  Google Scholar 

  76. Perou CM, Børresen-Dale AL. Systems biology and genomics of breast cancer. Cold Spring Harb Perspect Biol. 2011. doi:10.1101/cshperspect.a003293.

    PubMed  PubMed Central  Google Scholar 

  77. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140.

    PubMed  PubMed Central  Google Scholar 

  78. Tang MH, Varadan V, Kamalakaran S, Zhang MQ, Dimitrova N, Hicks J. Major chromosomal breakpoint intervals in breast cancer co-localize with differentially methylated regions. Front Oncol. 2012. doi:10.3389/fonc.2012.00197.

    Google Scholar 

  79. Trapp O, Seeliger K, Puchta H. Homologs of breast cancer genes in plants. Front Plant Sci. 2011. doi:10.3389/fpls.2011.00019.

    PubMed  PubMed Central  Google Scholar 

  80. Schuetz CS, Bonin M, Clare SE, Nieselt K, Sotlar K, Walter M, et al. Progression-specific genes identified by expression profiling of matched ductal carcinomas in situ and invasive breast tumors, combining laser capture microdissection and oligonucleotide microarray analysis. Cancer Res. 2006;66(10):5278–86.

    PubMed  CAS  Google Scholar 

  81. Uhlmann S, Mannsperger H, Zhang JD, Horvat E-A, Schmidt C, Kublbeck M, et al. Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer. Mol Syst Biol. 2012;8:570.

    PubMed  PubMed Central  Google Scholar 

  82. Wheeler DL, Dunn EF, Harari PM. Understanding resistance to EGFR inhibitors-impact on future treatment strategies. Nat Rev Clin Oncol. 2010;7:493–507.

    PubMed  CAS  PubMed Central  Google Scholar 

  83. Sahin O, Frohlich H, Lobke C, Korf U, Burmester S, Majety M, et al. Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance. BMC Syst Biol. 2009;3:1.

    PubMed  PubMed Central  Google Scholar 

  84. Harari D, Yarden Y. Molecular mechanisms underlying ErbB2/HER2 action in breast cancer. Oncogene. 2000;19:6102–14.

    PubMed  CAS  Google Scholar 

  85. Holbro T, Beerli RR, Maurer F, Koziczak M, Barbas III CF, Hynes NE. The ErbB2/ErbB3 heterodimer functions as an oncogenic unit: ErbB2 requires ErbB3 to drive breast tumor cell proliferation. Proc Natl Acad Sci U S A. 2003;100:8933–8.

    PubMed  CAS  PubMed Central  Google Scholar 

  86. Tsai MS, Shamon-Taylor LA, Mehmi I, Tang CK, Lupu R. Blockage of heregulin expression inhibits tumorigenicity and metastasis of breast cancer. Oncogene. 2003;22:761–8.

    PubMed  CAS  Google Scholar 

  87. Thottassery JV, Sun Y, Westbrook L, Rentz SS, Manuvakhova M, Qu Z, et al. Prolonged extracellular signal-regulated kinase 1/2 activation during fibroblast growth factor 1- or heregulin beta1-induced antiestrogen-resistant growth of breast cancer cells is resistant to mitogen-activated protein/extracellular regulated kinase kinase inhibitors. Cancer Res. 2004;64:4637–47.

    PubMed  CAS  Google Scholar 

  88. Leu YW, Yan PS, Fan M, Jin VX, Liu JC, Curran EM, et al. Loss of estrogen receptor signaling triggers epigenetic silencing of downstream targets in breast cancer. Cancer Res. 2004;64:8184–92.

    PubMed  CAS  Google Scholar 

  89. Gu F, Hsu HK, Hsu PY, Wu J, Ma Y, Parvin J, et al. Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data. BMC Syst Biol. 2010. doi:10.1186/1752-0509-4-170.

    Google Scholar 

  90. Piulats J, Tarrason G. E2F transcription factors and cancer. Clin Transl Oncol. 2001;3(5):241–9.

    CAS  Google Scholar 

  91. Thompson MR, Xu D. Williams BRATF3 transcription factor and its emerging roles in immunity and cancer. J Mol Med. 2009;87(11):1053–60.

    PubMed  CAS  PubMed Central  Google Scholar 

  92. Shen Q, Brown PH. Novel agents for the prevention of breast cancer: targeting transcription factors and signal transduction pathways. J Mammary Gland Biol Neoplasia. 2003;8(1):45–73.

    PubMed  Google Scholar 

  93. Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H, Mohamed YB, et al. An oestrogen-receptor-α-bound human chromatin interactome. Nature. 2009;462:58–64.

    PubMed  CAS  PubMed Central  Google Scholar 

  94. Fan M, Yan PS, Hartman-Frey C, Chen L, Paik H, Oyer SL, et al. Diverse gene expression and DNA methylation profiles correlate with differential adaptation of breast cancer cells to the antiestrogens tamoxifen and fulvestrant. Cancer Res. 2006;66:11954–66.

    PubMed  CAS  Google Scholar 

  95. Shen C, Huang Y, Liu Y, Wang G, Zhao Y, Wang Z, et al. A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer. BMC Syst Biol. 2011. doi:10.1186/1752-0509-5-67.

    Google Scholar 

  96. Sutherland RL, Mangrove EA. Cycling and breast cancer. J Mammary Gland Biol Neoplasia. 2004;9:95–104.

    PubMed  Google Scholar 

  97. Laganiere J, Deblois G, Giguere V. Functional genomics identifies a mechanism for estrogen activation of the retinoic acid receptor alpha1 gene in breast cancer cells. Mol Endocrinol. 2005;19:1584–92.

    PubMed  CAS  Google Scholar 

  98. Wolf-Yadlin A, Kumar N, Zhang Y, Hautaniemi S, Zaman M, Kim HD, et al. Effects of HER2 overexpression on cell signaling networks governing proliferation and migration. Mol Syst Biol. 2006;2:54.

    PubMed  PubMed Central  Google Scholar 

  99. Berezov A, Greene MI. Towards comprehensive characterization of HER2 overexpression. Mol Syst Biol. 2006;2:55.

    PubMed  PubMed Central  Google Scholar 

  100. Komurov K, Tseng JT, Muller M, Seviour EG, Moss TJ, Yang L, et al. The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells. Mol Syst Biol. 2012;8:596. doi:10.1038/msb.2012.25.

    PubMed  PubMed Central  Google Scholar 

  101. Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol. 2007;3:144.

    PubMed  PubMed Central  Google Scholar 

  102. van Golen KL, Wu ZF, Qiao XT, Bao LW, Merajver SD. RhoC GTPase, a novel transforming oncogene for human mammary epithelial cells that partially recapitulates the inflammatory breast cancer phenotype. Cancer Res. 2000;60:5832–8.

    PubMed  Google Scholar 

  103. Clark EA, Golub TR, Lander ES, Hynes RO. Genomic analysis of metastasis reveals an essential role for RhoC. Nature. 2000;406:532–5.

    PubMed  CAS  Google Scholar 

  104. Rosenthal DT, Zhang J, Bao L, Zhu L, Wu Z, Toy K, et al. RhoC impacts the metastatic potential and abundance of breast cancer stem cells. PLoS One. 2012. doi:10.1371/journal.pone.0040979.

    Google Scholar 

  105. Burrows C, Holly JM, Laurence NJ, Vernon EG, Carter JV, Clark MA, et al. Insulin-like growth factor binding protein 3 has opposing actions on malignant and nonmalignant breast epithelial cells that are each reversible and dependent upon cholesterol-stabilized integrin receptor complexes. Endocrinology. 2006;147:3484–500.

    PubMed  CAS  Google Scholar 

  106. Perks CM, Burrows C, Holly JM. Intrinsic, pro-apoptotic effects of IGFBP-3 on breast cancer cells are reversible: involvement of PKA, Rho, and ceramide. Front Endocrinol (Lausanne). 2011. doi:10.3389/fendo.2011.00013.

    Google Scholar 

  107. Debski MG, Pachucki J, Ambroziak M, Olszewski W, Bar-Andziak E. Human breast cancer tissue expresses high level of type 1 5′-deiodinase. Thyroid. 2007;17:3–10.

    PubMed  CAS  Google Scholar 

  108. Ostrander JH, Daniel AR, Lofgren K, Kleer CG, Lange CA. Breast tumor kinase (protein tyrosine kinase 6) regulates heregulin-induced activation of ERK5 and p38 MAP kinases in breast cancer cells. Cancer Res. 2007;67(9):199–209.

    Google Scholar 

  109. Casula S, Bianco AC. Thyroid hormone deiodinases and cancer. Front Endocrinol (Lausanne). 2012. doi:10.3389/fendo.2012.00074.

    Google Scholar 

  110. Roy D, Calaf G, Hei TK. Profiling of differentially expressed genes induced by high linear energy transfer radiation in breast epithelial cells. Mol Carcinog. 2001;31:4.

    Google Scholar 

  111. Cotta-Ramusino C, McDonald 3rd ER, Hurov K, Sowa ME, Harper JW, Elledge SJ. A DNA damage response screen identifies RHINO, a 9-1-1 and TopBP1 interacting protein required for ATR signaling. Science. 2011;332(6035):1313–7.

    PubMed  CAS  Google Scholar 

  112. Wang K, Ye Y, Xu Z, Zhang X, Hou Z, Cui Y, et al. Interaction between BRCA1/BRCA2 and ATM/ATR associate with breast cancer susceptibility in a Chinese Han population. Cancer Genet Cytogenet. 2010;200(1):40–6.

    PubMed  CAS  Google Scholar 

  113. Pedram A, Razandi M, Evinger AJ, Lee E, Levin ER. Estrogen inhibits ATR signaling to cell cycle checkpoints and DNA repair. Mol Biol Cell. 2009;20(14):3374–89.

    PubMed  CAS  PubMed Central  Google Scholar 

  114. Liu S, Ginestier C, Charafe-Jauffret E, Foco H, Kleer CG, Merajver SD, et al. BRCA1 regulates human mammary stem/progenitor cell fate. Proc Natl Acad Sci U S A. 2008. doi:10.1073/pnas.0711613105.

    Google Scholar 

  115. Gonzalez ME, DuPrie ML, Krueger H, Merajver SD, Ventura AC, Toy KA, et al. Histone methyltransferase EZH2 induces Akt-dependent genomic instability and BRCA1 inhibition in breast cancer. Cancer Res. 2011. doi:10.1158/0008-5472.CAN-10-1933.

    Google Scholar 

  116. Nakshatri H, Badve S. FOXA1 in breast cancer. Expert Rev Mol Med. 2009;11:e8.

    PubMed  Google Scholar 

  117. Robinson JL, Carroll JS. FoxA1 is a key mediator of hormonal response in breast and prostate cancer. Front Endocrinol (Lausanne). 2012;3:68. doi:10.3389/fendo.2012.00068.

    CAS  Google Scholar 

  118. Ashbury JE, Lévesque LE, Beck PA, Aronson KJ. Selective serotonin reuptake inhibitor (SSRI) antidepressants, prolactin and breast cancer. Front Oncol. 2012;2:177. doi:10.3389/fonc.2012.00177.

    PubMed  PubMed Central  Google Scholar 

  119. Lovato A, Panasci L, Witcher M. Is there an epigenetic component underlying the resistance of triple-negative breast cancers to Parp inhibitors? Front Pharmacol. 2012;3:202. doi:10.3389/fphar.2012.00202.

    PubMed  PubMed Central  Google Scholar 

  120. Knight LA, Kurbacher CM, Glaysher S, Fernando A, Reichelt R, Dexel S, et al. Activity of mevalonate pathway inhibitors against breast and ovarian cancers in the ATP-based tumour chemosensitivity assay. BMC Cancer. 2009;9:38. doi:10.1186/1471-2407-9-38.

    PubMed  PubMed Central  Google Scholar 

  121. Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, 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. 2007;13(11):3207–14.

    PubMed  CAS  Google Scholar 

  122. Chang HY, Nuyten DS, Sneddon JB, Hastie T, Tibshirani R, Sorlie T, et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A. 2005;102:3738–43.

    PubMed  CAS  PubMed Central  Google Scholar 

  123. Minn AJ, Gupta GP, Siegel PM, Bos PD, Shu W, Giri DD, et al. Genes that mediate breast cancer metastasis to lung. Nature. 2005;436(7050):518–24.

    PubMed  CAS  PubMed Central  Google Scholar 

  124. Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, et al. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci. 2005;102(38):13550–5.

    PubMed  CAS  PubMed Central  Google Scholar 

  125. Tan EY, Campo L, Han C, Turley H, Pezella F, Gatter KC, et al. BNIP3 as a progression marker in primary human breast cancer; opposing functions in situ versus invasive cancer. Clin Cancer Res. 2007;13(2):467–74.

    PubMed  CAS  Google Scholar 

  126. Matsuura I, Lai CY, Chiang KN. Functional interaction between Smad3 and S100A4 (metastatin-1) for TGF-beta-mediated cancer cell invasiveness. Biochem J. 2010;426(3):327–35.

    PubMed  CAS  Google Scholar 

  127. Petersen M, Pardali E, van der Horst G, Cheung H, van den Hoogen C, van der Pluijm G, et al. Smad2 and Smad3 have opposing roles in breast cancer bone metastasis by differentially affecting tumor angiogenesis. Oncogene. 2010;29(9):1351–61.

    PubMed  CAS  Google Scholar 

  128. Yao C, Li H, Zhou C, Zhang L, Zou J, Guo Z. Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis. BMC Syst Biol. 2010;4:151. doi:10.1186/1752-0509-4-151.

    PubMed  PubMed Central  Google Scholar 

  129. Faratian D, Goltsov A, Lebedeva G, Sorokin A, Moodie S, Mullen P, et al. Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab. Cancer Res. 2009;69(16):6713–20. doi:10.1158/0008-5472.CAN-09-0777.

    PubMed  CAS  Google Scholar 

  130. Laubenbacher R, Hower V, Jarrah A, Torti SV, Shulaev V, Mendes P, et al. A systems biology view of cancer. Biochim Biophys Acta. 2009;1796(2):129–39.

    PubMed  CAS  PubMed Central  Google Scholar 

  131. Krebs EE, Taylor BC, Cauley JA, Stone KL, Bowman PJ, Ensrud KE. Measures of adiposity and risk of breast cancer in older post-menopausal women. J Am Geriatr Soc. 2006;54:63–9.

    PubMed  Google Scholar 

  132. Oh SW, Park CY, Lee ES, Yoon YS, Lee ES, Park SS, et al. Adipokines, insulin resistance, metabolic syndrome, and breast cancer recurrence: a cohort study. Breast Cancer Res. 2011;13:R34.

    PubMed  PubMed Central  Google Scholar 

  133. Gunter MJ, Hoover DR, Yu H, Wassertheil-Smoller S, Rohan TE, Manson JE, et al. Insulin, insulin-like growth factor-I, and risk of breast cancer in postmenopausal women. J Natl Cancer Inst. 2009;101:48–60.

    PubMed  CAS  PubMed Central  Google Scholar 

  134. Hernández-Lemus E, Mejía C. Inference and analysis of apoptotic pathways in papillary thyroid cancer. In: Kreuger E, Trommler B, editors. Thyroid cancer: diagnosis, treatment and prognosis. Hauppauge: Nova Science Publishers; 2012. p. 127–60.

    Google Scholar 

  135. Alokail MS, Al-Daghri N, Abdulkareem A, Draz HM, Yakout SM, Alnaami AM, et al. Metabolic syndrome biomarkers and early breast cancer in Saudi women: evidence for the presence of a systemic stress response and/or a pre-existing metabolic syndrome-related neoplasia risk? BMC Cancer. 2013;13:54. doi:10.1186/1471-2407-13-54.

    PubMed  CAS  PubMed Central  Google Scholar 

  136. Kim S, Nam H, Lee D. Exploring molecular links between lymph node invasion and cancer prognosis in human breast cancer. BMC Syst Biol. 2011;5 Suppl 2:S4. doi:10.1186/1752-0509-5-S2-S4.

    PubMed  CAS  PubMed Central  Google Scholar 

  137. Carrivick L, Rogers S, Clark J, Campbell C, Girolami M, Cooper C. Identification of prognostic signatures in breast cancer microarray data using Bayesian techniques. J R Soc Interface. 2006;3(8):367–81.

    PubMed  CAS  PubMed Central  Google Scholar 

  138. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci U S A. 2003;100:3983–8.

    PubMed  CAS  PubMed Central  Google Scholar 

  139. Bergamaschi A, Kim YH, Wang P, Sorlie T, Hernandez-Boussard T, Lonning PE, et al. Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer. Genes Chromosomes Cancer. 2006;45:1033–40.

    PubMed  CAS  Google Scholar 

  140. Ivshina AV, George J, Senko O, Mow B, Putti TC, Smeds J, et al. Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res. 2006;66(21):10292–301.

    PubMed  CAS  Google Scholar 

  141. Macció A, Madeddu C, Mantovani G. Adipose tissue as target organ in the treatment of hormone dependent breast cancer: new therapeutic perspectives. Obes Rev. 2009;10:660–70.

    PubMed  Google Scholar 

  142. Komurov K, Tseng J-T, Muller M, Seviour EG, Moss TJ, Yang L, et al. The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells. Mol Syst Biol. 2012;8:596.

    PubMed  PubMed Central  Google Scholar 

  143. Fox EM, Arteaga CL, Miller TW. Abrogating endocrine resistance by targeting ER α and PI3K in breast cancer. Front Oncol. 2012;2:145. doi:10.3389/fonc.2012.00145.

    PubMed  PubMed Central  Google Scholar 

  144. Ursini-Siegel J. Can pharmacological receptor tyrosine kinase inhibitors sensitize poor outcome breast tumors to immune-based therapies? Front Oncol. 2013;3:23. doi:10.3389/fonc.2013.00023.

    PubMed  PubMed Central  Google Scholar 

  145. Merry C, Fu K, Wang J, Yeh IJ, Zhang Y. Targeting the checkpoint kinase Chk1 in cancer therapy. Cell Cycle. 2010;9(2):279–83.

    PubMed  CAS  PubMed Central  Google Scholar 

  146. Peasland A, Wang LZ, Rowling E, Kyle S, Chen T, Hopkins A, et al. Identification and evaluation of a potent novel ATR inhibitor, NU6027, in breast and ovarian cancer cell lines. Br J Cancer. 2011;105(3):372–81. doi:10.1038/bjc.2011.243.

    PubMed  CAS  PubMed Central  Google Scholar 

  147. Pawitan Y, Bjöhle J, Amler L, Borg AL, Egyhazi S, Hall P, et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res. 2005;7(6):R953–64.

    PubMed  CAS  PubMed Central  Google Scholar 

  148. Azuma K, Tsurutani J, Sakai K, Kaneda H, Fujisaka Y, Takeda M, et al. Switching addictions between HER2 and FGFR2 in HER2-positive breast tumor cells: FGFR2 as a potential target for salvage after lapatinib failure. Biochem Biophys Res Commun. 2011;407:219–24.

    PubMed  CAS  Google Scholar 

  149. Ciardiello F, Troiani T, Bianco R, Orditura M, Morgillo F, Martinelli E, et al. Interaction between the epidermal growth factor receptor (EGFR) and the vascular endothelial growth factor (VEGF) pathways: a rational approach for multi-target anticancer therapy. Ann Oncol. 2006;17 Suppl 7:vii109–14.

    PubMed  Google Scholar 

  150. Liu L, Greger J, Shi H, Liu Y, Greshock J, Annan R, et al. Novel mechanism of lapatinib resistance in HER2-positive breast tumor cells: activation of AXL. Cancer Res. 2009;69:6871–8.

    PubMed  CAS  Google Scholar 

  151. Pan Q, Rosenthal DT, Bao L, Kleer CG, Merajver SD. Antiangiogenic tetrathiomolybdate protects against Her2/neu-induced breast carcinoma by hypoplastic remodeling of the mammary gland. Clin Cancer Res. 2009;15(23):7441–6. doi:10.1158/1078-0432.CCR-09-1361.

    PubMed  CAS  PubMed Central  Google Scholar 

  152. Wang Z, Fukushima H, Inuzuka H, Wan L, Liu P, Gao D, et al. Skp2 is a promising therapeutic target in breast cancer. Front Oncol. 2012;1(57). pii:18702.

    Google Scholar 

  153. Ressler S, Mlineritsch B, Greil R. Zoledronic acid for adjuvant use in patients with breast cancer. Expert Rev Anticancer Ther. 2011;11(3):333–49.

    PubMed  CAS  Google Scholar 

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Hernández-Lemus, E. (2014). Systems Biology and Integrative Omics in Breast Cancer. In: Barh, D. (eds) Omics Approaches in Breast Cancer. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0843-3_17

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