Systems biology and integrative omics in breast cancer

Omics Approaches in Breast Cancer: Towards Next-Generation Diagnosis, Prognosis and Therapy

Volumn , Issue , 2014, Pages 333-352

  Hernández Lemus, Enrique ^a  

^a INSTITUTO NACIONAL DE MEDICINA GENÓMICA   (Mexico)

Author keywords

Breast cancer; Clinical applications; Molecular pathways; Omics; Systems biology; Tumor subtypes

Indexed keywords

EID: 84930741979     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-81-322-0843-3_17     Document Type: Chapter

References (153)

1. Visvanathan M, Baumgartner C, Tilg B, Lushington GH. Systems biology approach for mapping TNF NFB mathematical model to a protein interaction map. Open Syst Biol J. 2010;3(1):1-8.
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.
3. You L. Toward computational systems biology. Cell Biochem Biophys. 2004;40(2):167-84.
4. Kitano H. Computational systems biology. Nature. 2002;420:206-10.
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.
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.
7. Schouten P. Big data in health care. Healthc Financ Manage. 2013;67(2):40-2.
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.
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.
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.
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.
12. Ernst J, Vainas O, Harbison CT, Simon I, Bar-Joseph Z. Reconstructing dynamic regulatory maps. Mol Syst Biol. 2007;3:74.
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.
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.
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.
16. Faratian D. Systems pathology. Breast Cancer Res. 2010;12 Suppl 4:S4.
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.
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.
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.
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.
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.
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.
23. Wang X, Gotoh O. Inference of cancer-specific gene regulatory networks using soft computing rules. Gene Regul Syst Biol. 2010;4:19-34.
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.
25. Kitano H. Cancer as a robust system: implications for anticancer therapy. Nat Rev Cancer. 2004;4(3):227-35.
26. Tonon G. From oncogene to network addiction: The new frontier of cancer genomics and therapeutics. Future Oncol. 2008;4(4):569-77.
27. Malumbres M. miRNAs versus oncogenes: The power of social networking. Mol Syst Biol. 2012;8:569. doi: 10.1038/msb.2012.2.
28. Lim WK, Lyashenko E, Califano A. Master regulators used as breast cancer metastasis classifier. Pac Symp Biocomput. 2009;14:504-15.
29. Locasale JW. Metabolic rewiring drives resistance to targeted cancer therapy. Mol Syst Biol. 2012;8:597. doi: 10.1038/msb.2012.30.
30. Tennant DA, Durán RV, Gottlieb E. Targeting metabolic transformation for cancer therapy. Nat Rev Cancer. 2010;10(4):267-77.
31. Sethi JK, Vidal-Puig A. Wnt signalling and the control of cellular metabolism. Biochem J. 2010;427(1):1-17.
32. Jones RG, Thompson CB. Tumor suppressors and cell metabolism: A recipe for cancer growth. Genes Dev. 2009;23:537-48.
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.
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.
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.
36. Hernández-Lemus E. Non-equilibrium thermodynamics of gene expression and transcriptional regulation. J Nonequilib Thermodyn. 2009;34(4):371-94.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
55. Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M. Capillary electrophoresis mass spectrometrybased saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics. 2010;6(1):78-95.
56. Tworoger SS, Eliassen AH, Kelesidis T. Plasma adiponectin concentrations and risk of incident breast cancer. J Clin Endocrinol Metab. 2007;92:1510-6.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
70. Rosen LS, Ashurst HL, Chap L. Targeting signal transduction pathways in metastatic breast cancer: A comprehensive review. Oncologist. 2010;15:216-35.
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.
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.
73. Tabchy A, Hennessy BT, Hortobagyi G, Mills GB. Systems biology of breast cancer. Curr Breast Cancer Rep. 2009;1:238-45.
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.
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.
76. Perou CM, Børresen-Dale AL. Systems biology and genomics of breast cancer. Cold Spring Harb Perspect Biol. 2011. doi: 10.1101/cshperspect.a003293.
77. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140.
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.
79. Trapp O, Seeliger K, Puchta H. Homologs of breast cancer genes in plants. Front Plant Sci. 2011. doi: 10.3389/fpls.2011.00019.
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.
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.
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.
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.
84. Harari D, Yarden Y. Molecular mechanisms underlying ErbB2/HER2 action in breast cancer. Oncogene. 2000;19:6102-14.
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.
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.
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.
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.
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.
90. Piulats J, Tarrason G. E2F transcription factors and cancer. Clin Transl Oncol. 2001;3(5):241-9.
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.
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.
93. Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H, Mohamed YB, et al. An oestrogen-receptor-bound human chromatin interactome. Nature. 2009;462:58-64.
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.
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 regulatory networks in breast cancer. BMC Syst Biol. 2011. doi: 10.1186/1752-0509-5-67.
96. Sutherland RL, Mangrove EA. Cycling and breast cancer. J Mammary Gland Biol Neoplasia. 2004;9:95-104.
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.
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.
99. Berezov A, Greene MI. Towards comprehensive characterization of HER2 overexpression. Mol Syst Biol. 2006;2:55.
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.
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.
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.
103. Clark EA, Golub TR, Lander ES, Hynes RO. Genomic analysis of metastasis reveals an essential role for RhoC. Nature. 2000;406:532-5.
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.
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.
106. Perks CM, Burrows C, Holly JM. Intrinsic, proapoptotic 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.
107. Debski MG, Pachucki J, Ambroziak M, Olszewski W, Bar-Andziak E. Human breast cancer tissue expresses high level of type 1 5deiodinase. Thyroid. 2007;17:3-10.
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.
109. Casula S, Bianco AC. Thyroid hormone deiodinases and cancer. Front Endocrinol (Lausanne). 2012. doi: 10.3389/fendo.2012.00074.
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.
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.
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.
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.
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.
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.
116. Nakshatri H, Badve S. FOXA1 in breast cancer. Expert Rev Mol Med. 2009;11:e8.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
128. Yao C, Li H, Zhou C, Zhang L, Zou J, Guo Z. Multilevel reproducibility of signature hubs in human interactome for breast cancer metastasis. BMC Syst Biol. 2010;4:151. doi: 10.1186/1752-0509-4-151.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
143. Fox EM, Arteaga CL, Miller TW. Abrogating endocrine resistance by targeting ER and PI3K in breast cancer. Front Oncol. 2012;2:145. doi: 10.3389/fonc.2012.00145.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.