Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature

Pharmacological Research

Volumn 111, Issue , 2016, Pages 290-302

  Poornima, Paramasivan ^a   Kumar, Jothi Dinesh ^b   Zhao, Qiaoli ^c   Blunder, Martina ^d   Efferth, Thomas ^c  

^a BANGOR UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

^c JOHANNES GUTENBERG UNIVERSITY   (Germany)

^d UPPSALA UNIVERSITY   (Sweden)

Author keywords

Eleutherococcus senticosus; Genomics; Green tea polyphenolics; Lithospermum erythrorhizon; Metabolomics; Polypharmacology; Proteomics; Rhodiola rosea; Schisandra chinensis; Synthetic lethality; Systems biology; Transcriptomics

Indexed keywords

BETA CATENIN; HERBACEOUS AGENT; LONG UNTRANSLATED RNA; MAMMALIAN TARGET OF RAPAMYCIN; MAMMALIAN TARGET OF RAPAMYCIN INHIBITOR; MESSENGER RNA; MICRORNA; MYC PROTEIN; PHOSPHATIDYLINOSITOL 3 KINASE; POLYPHENOL DERIVATIVE; PROTEIN KINASE B; REACTIVE OXYGEN METABOLITE; SHIKONIN; VASCULOTROPIN; ANTINEOPLASTIC AGENT; TUMOR MARKER;

AUTOPHAGY; CANCER CLASSIFICATION; CANCER RESISTANCE; CARCINOGENESIS; DNA DAMAGE; DRUG TARGETING; ENDOPLASMIC RETICULUM STRESS; EPIGENETICS; GENOMICS; HUMAN; METABOLOMICS; MITOCHONDRION; NONHUMAN; OXIDATIVE STRESS; PERSONALIZED MEDICINE; PRIORITY JOURNAL; PROTEOMICS; REVIEW; RNA SEQUENCE; TRANSCRIPTOMICS; TUMOR GROWTH; TUMOR VASCULARIZATION; UNFOLDED PROTEIN RESPONSE; WNT SIGNALING PATHWAY; ANIMAL; DRUG DEVELOPMENT; DRUG EFFECTS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; ISOLATION AND PURIFICATION; MEDICINAL PLANT; METABOLISM; MOLECULARLY TARGETED THERAPY; NEOPLASM; PATHOLOGY; PHYTOTHERAPY; PROCEDURES; PROTEIN PROTEIN INTERACTION; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

ANIMALS; ANTINEOPLASTIC AGENTS, PHYTOGENIC; BIOMARKERS, TUMOR; DRUG DISCOVERY; EPIGENOMICS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; HUMANS; METABOLOMICS; MOLECULAR TARGETED THERAPY; NEOPLASMS; PHYTOTHERAPY; PLANTS, MEDICINAL; PROTEIN INTERACTION MAPS; PROTEOMICS; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EID: 84976867169     PISSN: 10436618     EISSN: 10961186     Source Type: Journal    
DOI: 10.1016/j.phrs.2016.06.018     Document Type: Review

References (153)

1. [1] Scannell, J.W., Blanckley, A., Boldon, H., Warrington, B., Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11 (2012), 191–200.
2. [2] Hopkins, A.L., Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4 (2008), 682–690.
3. [3] Xie, L., Xie, L., Kinnings, S.L., Bourne, P.E., Novel computational approaches to polypharmacology as a means to define responses to individual drugs. Annu. Rev. Pharmacol. Toxicol. 52 (2012), 361–379.
4. [4] Hopkins, A.L., Drug discovery: predicting promiscuity. Nature 462 (2009), 167–168.
5. [5] Azmiand, A.S., Mohammad, R.M., Rectifying cancer drug discovery through network pharmacology. Future Med. Chem. 6 (2014), 529–539.
6. [6] Barabasi, A.L., Gulbahce, N., Loscalzo, J., Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12 (2011), 56–68.
7. [7] Espinoza-Fonseca, L.M., The benefits of the multi-target approach in drug design and discovery. Bioorg. Med. Chem. 14 (2006), 896–897.
8. [8] Mencherand, S.K., Wang, L.G., Promiscuous drugs compared to selective drugs (promiscuity can be a virtue). BMC Clin Pharmacol., 5, 2005, 3.
9. [9] Chandraand, N., Padiadpu, J., Network approaches to drug discovery. Expert Opin. Drug Discov. 8 (2013), 7–20.
10. [10] Azmi, A.S., Wang, Z., Philip, P.A., Mohammad, R.M., Sarkar, F.H., Proof of concept: network and systems biology approaches aid in the discovery of potent anticancer drug combinations. Mol. Cancer Ther. 9 (2010), 3137–3144.
11. [11] Goh, K.I., Cusick, M.E., Valle, D., Childs, B., Vidal, M., Barabasi, A.L., The human disease network. Proc. Natl. Acad. Sci. U. S. A. 104 (2007), 8685–8690.
12. [12] Rask-Andersen, M., Almen, M.S., Schioth, H.B., Trends in the exploitation of novel drug targets. Nat. Rev. Drug Discov. 10 (2011), 579–590.
13. [13] Kibble, M., Saarinen, N., Tang, J., Wennerberg, K., Makela, S., Aittokallio, T., Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat. Prod. Rep. 32 (2015), 1249–1266.
14. [14] Li, S., Fan, T.P., Jia, W., Lu, A., Zhang, W., Network pharmacology in traditional chinese medicine. Evid. Based Complement. Alternat. Med., 2014, 2014, 138460.
15. [15] Ainsworth, C., Networking for new drugs. Nat. Med. 17 (2011), 1166–1168.
16. [16] Zhang, G.B., Li, Q.Y., Chen, Q.L., Su, S.B., Network pharmacology: a new approach for chinese herbal medicine research. Evid. Based Complement. Alternat. Med., 2013, 2013, 621423.
17. [17] Wang, Z., Li, J., Dang, R., Liang, L., Lin, J., PhIN: a protein pharmacology interaction network database. CPT Pharmacomet. Syst. Pharmacol., 4, 2015, e00025.
18. [18] Huang, H., Wu, X., Pandey, R., Li, J., Zhao, G., Ibrahim, S., Chen, J.Y., C(2)Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships. BMC Genom., 13(Suppl. 6), 2012, S17.
19. [19] Liand, S., Zhang, B., Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin. J. Nat. Med. 11 (2013), 110–120.
20. [20] Gu, J., Gui, Y., Chen, L., Yuan, G., Xu, X., CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology. J. Cheminform., 5, 2013, 51.
21. [21] Pache, R.A., Ceol, A., Aloy, P., NetAligner—a network alignment server to compare complexes, pathways and whole interactomes. Nucleic Acids Res. 40 (2012), W157–W161.
22. [22] Anighoro, A., Bajorath, J., Rastelli, G., Polypharmacology: challenges and opportunities in drug discovery. J. Med. Chem. 57 (2014), 7874–7887.
23. [23] Rask-Andersen, M., Zhang, J., Fabbro, D., Schioth, H.B., Advances in kinase targeting: current clinical use and clinical trials. Trends Pharmacol. Sci. 35 (2014), 604–620.
24. [24] Azmi, A.S., Network pharmacology for cancer drug discovery: are we there yet?. Future Med. Chem. 4 (2012), 939–941.
25. [25] Schadt, E.E., Molecular networks as sensors and drivers of common human diseases. Nature 461 (2009), 218–223.
26. [26] Brennan, C.W., Verhaak, R.G., McKenna, A., Campos, B., Noushmehr, H., Salama, S.R., Zheng, S., Chakravarty, D., Sanborn, J.Z., Berman, S.H., Beroukhim, R., Bernard, B., Wu, C.J., Genovese, G., Shmulevich, I., Barnholtz-Sloan, J., Zou, L., Vegesna, R., Shukla, S.A., Ciriello, G., Yung, W.K., Zhang, W., Sougnez, C., Mikkelsen, T., Aldape, K., Bigner, D.D., Van Meir, E.G., Prados, M., Sloan, A., Black, K.L., Eschbacher, J., Finocchiaro, G., Friedman, W., Andrews, D.W., Guha, A., Iacocca, M., O'Neill, B.P., Foltz, G., Myers, J., Weisenberger, D.J., Penny, R., Kucherlapati, R., Perou, C.M., Hayes, D.N., Gibbs, R., Marra, M., Mills, G.B., Lander, E., Spellman, P., Wilson, R., Sander, C., Weinstein, J., Meyerson, M., Gabriel, S., Laird, P.W., Haussler, D., Getz, G., Chin, L., Network, T.R., The somatic genomic landscape of glioblastoma. Cell 155 (2013), 462–477.
27. [27] N. Cancer Genome Atlas Research, Integrated genomic analyses of ovarian carcinoma. Nature 474 (2011), 609–615.
28. [28] N. Cancer Genome Atlas, Comprehensive molecular characterization of human colon and rectal cancer. Nature 487 (2012), 330–337.
29. [29] N. Cancer Genome Atlas Research, Comprehensive genomic characterization of squamous cell lung cancers. Nature 489 (2012), 519–525.
30. [30] N. Cancer Genome Atlas, Comprehensive molecular portraits of human breast tumours. Nature 490 (2012), 61–70.
31. [31] N. Cancer Genome Atlas Research, Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368 (2013), 2059–2074.
32. [32] N. Cancer Genome Atlas Research, Kandoth, C., Schultz, N., Cherniack, A.D., Akbani, R., Liu, Y., Shen, H., Robertson, A.G., Pashtan, I., Shen, R., Benz, C.C., Yau, C., Laird, P.W., Ding, L., Zhang, W., Mills, G.B., Kucherlapati, R., Mardis, E.R., Levine, D.A., Integrated genomic characterization of endometrial carcinoma. Nature 497 (2013), 67–73.
33. [33] Davis, C.F., Ricketts, C.J., Wang, M., Yang, L., Cherniack, A.D., Shen, H., Buhay, C., Kang, H., Kim, S.C., Fahey, C.C., Hacker, K.E., Bhanot, G., Gordenin, D.A., Chu, A., Gunaratne, P.H., Biehl, M., Seth, S., Kaipparettu, B.A., Bristow, C.A., Donehower, L.A., Wallen, E.M., Smith, A.B., Tickoo, S.K., Tamboli, P., Reuter, V., Schmidt, L.S., Hsieh, J.J., Choueiri, T.K., Hakimi, A.A., N. Cancer Genome Atlas Research, Chin, L., Meyerson, M., Kucherlapati, R., Park, W.Y., Robertson, A.G., Laird, P.W., Henske, E.P., Kwiatkowski, D.J., Park, P.J., Morgan, M., Shuch, B., Muzny, D., Wheeler, D.A., Linehan, W.M., Gibbs, R.A., Rathmell, W.K., Creighton, C.J., The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26 (2014), 319–330.
34. [34] N. Cancer Genome Atlas Research, Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513 (2014), 202–209.
35. [35] N. Cancer Genome Atlas Research, Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507 (2014), 315–322.
36. [36] Hoadley, K.A., Yau, C., Wolf, D.M., Cherniack, A.D., Tamborero, D., Ng, S., Leiserson, M.D., Niu, B., McLellan, M.D., Uzunangelov, V., Zhang, J., Kandoth, C., Akbani, R., Shen, H., Omberg, L., Chu, A., Margolin, A.A., Van't Veer, L.J., Lopez-Bigas, N., Laird, P.W., Raphael, B.J., Ding, L., Robertson, A.G., Byers, L.A., Mills, G.B., Weinstein, J.N., Van Waes, C., Chen, Z., Collisson, E.A., N. Cancer Genome Atlas Research, Benz, C.C., Perou, C.M., Stuart, J.M., Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158 (2014), 929–944.
37. [37] Tian, X., Wang, X., Chen, J., Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction. Cancer Inform. 13 (2014), 25–33.
38. [38] Zhang, W., Wan, Y.W., Allen, G.I., Pang, K., Anderson, M.L., Liu, Z., Molecular pathway identification using biological network-regularized logistic models. BMC Genomics., 14(Suppl. 8), 2013, S7.
39. [39] Cerami, E., Demir, E., Schultz, N., Taylor, B.S., Sander, C., Automated network analysis identifies core pathways in glioblastoma. PLoS One, 5, 2010, e8918.
40. [40] Manda, G., Isvoranu, G., Comanescu, M.V., Manea, A., Debelec Butuner, B., Korkmaz, K.S., The redox biology network in cancer pathophysiology and therapeutics. Redox Biol. 5 (2015), 347–357.
41. [41] Landriscina, M., Maddalena, F., Laudiero, G., Esposito, F., Adaptation to oxidative stress, chemoresistance, and cell survival. Antioxid. Redox Signal. 11 (2009), 2701–2716.
42. [42] Reuter, S., Gupta, S.C., Chaturvedi, M.M., Aggarwal, B.B., Oxidative stress, inflammation, and cancer: how are they linked?. Free Radic. Biol. Med. 49 (2010), 1603–1616.
43. [43] Eng, L., Azad, A.K., Qiu, X., Kong, Q.Q., Cheng, D., Ying, N., Tse, A., Kuang, Q., Dodbiba, L., Renouf, D.J., Marsh, S., Savas, S., Mackay, H.J., Knox, J.J., Darling, G.E., Wong, R.K., Xu, W., Liu, G., Faluyi, O.O., Discovery and validation of vascular endothelial growth factor (VEGF) pathway polymorphisms in esophageal adenocarcinoma outcome. Carcinogenesis 36 (2015), 956–962.
44. [44] Eng, L., Azad, A.K., Habbous, S., Pang, V., Xu, W., Maitland-van der Zee, A.H., Savas, S., Mackay, H.J., Amir, E., Liu, G., Vascular endothelial growth factor pathway polymorphisms as prognostic and pharmacogenetic factors in cancer: a systematic review and meta-analysis. Clin. Cancer Res. 18 (2012), 4526–4537.
45. [45] Prager, G.W., Poettler, M., Unseld, M., Zielinski, C.C., Angiogenesis in cancer: anti-VEGF escape mechanisms. Transl. Lung Cancer Res. 1 (2012), 14–25.
46. [46] Boland, M.L., Chourasia, A.H., Macleod, K.F., Mitochondrial dysfunction in cancer. Front. Oncol., 3, 2013, 292.
47. [47] Chanetonand, B., Gottlieb, E., Rocking cell metabolism: revised functions of the key glycolytic regulator PKM2 in cancer. Trends Biochem. Sci. 37 (2012), 309–316.
48. [48] Vander Heiden, M.G., Cantley, L.C., Thompson, C.B., Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324 (2009), 1029–1033.
49. [49] Wong, R.S., Apoptosis in cancer: from pathogenesis to treatment. J. Exp. Clin. Cancer Res., 30, 2011, 87.
50. [50] Karch, J., Kanisicak, O., Brody, M.J., Sargent, M.A., Michael, D.M., Molkentin, J.D., Necroptosis interfaces with MOMP and the MPTP in mediating cell death. PLoS One, 10, 2015, e0130520.
51. [51] Matic, I., Strobbe, D., Frison, M., Campanella, M., Controlled and impaired mitochondrial quality in neurons: molecular physiology and prospective pharmacology. Pharmacol. Res. 99 (2015), 410–424.
52. [52] Liao, X.H., Zheng, L., He, H.P., Zheng, D.L., Wei, Z.Q., Wang, N., Dong, J., Ma, W.J., Zhang, T.C., STAT3 regulated ATR via microRNA-383 to control DNA damage to affect apoptosis in A431 cells. Cell. Signal. 27 (2015), 2285–2295.
53. [53] Demoulin, B., Hermant, M., Castrogiovanni, C., Staudt, C., Dumont, P., Resveratrol induces DNA damage in colon cancer cells by poisoning topoisomerase II and activates the ATM kinase to trigger p53-dependent apoptosis. Toxicol. In Vitro 29 (2015), 1156–1165.
54. [54] Chene, G., Ouellet, V., Rahimi, K., Barres, V., Caceres, K., Meunier, L., Cyr, L., De Ladurantaye, M., Provencher, D., Mes Masson, A.M., DNA damage signaling and apoptosis in preinvasive tubal lesions of ovarian carcinoma. Int. J. Gynecol. Cancer 25 (2015), 761–769.
55. [55] Haynes, B., Saadat, N., Myung, B., Shekhar, M.P.V., Crosstalk between translesion synthesis Fanconi anemia network, and homologous recombination repair pathways in interstrand DNA crosslink repair and development of chemoresistance. Mutat. Res./Rev. Mutat. Res. 763 (2015), 258–266.
56. [56] Burris, H.A. 3rd, Overcoming acquired resistance to anticancer therapy: focus on the PI3 K/AKT/mTOR pathway. Cancer Chemother. Pharmacol. 71 (2013), 829–842.
57. [57] Nahtaand, R., O'Regan, R.M., Evolving strategies for overcoming resistance to HER2-directed therapy: targeting the PI3K/Akt/mTOR pathway. Clin. Breast Cancer 10:Suppl 3 (2010), S72–S78.
58. [58] Cheaib, B., Auguste, A., Leary, A., The PI3K/Akt/mTOR pathway in ovarian cancer: therapeutic opportunities and challenges. Chin. J. Cancer 34 (2015), 4–16.
59. [59] Yang, J., Zhao, Y., Ma, K., Jiang, F.J., Liao, W., Zhang, P., Zhou, J., Tu, B., Wang, L., Kampinga, H.H., Xie, Z., Zhu, W.G., Deficiency of hepatocystin induces autophagy through an mTOR-dependent pathway. Autophagy 7 (2011), 748–759.
60. [60] Yang, Z.J., Chee, C.E., Huang, S., Sinicrope, F.A., The role of autophagy in cancer: therapeutic implications. Mol. Cancer Ther. 10 (2011), 1533–1541.
61. [61] Lebovitz, C.B., Robertson, A.G., Goya, R., Jones, S.J., Morin, R.D., Marra, M.A., Gorski, S.M., Cross-cancer profiling of molecular alterations within the human autophagy interaction network. Autophagy 11 (2015), 1668–1687.
62. [62] Denton, D., Xu, T., Kumar, S., Autophagy as a pro-death pathway. Immunol. Cell Biol. 93 (2015), 35–42.
63. [63] Giansanti, V., Torriglia, A., Scovassi, A.L., Conversation between apoptosis and autophagy: is it your turn or mine?. Apoptosis 16 (2011), 321–333.
64. [64] Inguscio, V., Panzarini, E., Dini, L., Autophagy contributes to the death/survival balance in cancer. PhotoDyn. Ther. 1 (2012), 464–491.
65. [65] Bhutia, S.K., Mukhopadhyay, S., Sinha, N., Das, D.N., Panda, P.K., Patra, S.K., Maiti, T.K., Mandal, M., Dent, P., Wang, X.Y., Das, S.K., Sarkar, D., Fisher, P.B., Autophagy: cancer's friend or foe?. Adv. Cancer Res. 118 (2013), 61–95.
66. [66] Park, C.C., Bissell, M.J., Barcellos-Hoff, M.H., The influence of the microenvironment on the malignant phenotype. Mol. Med. Today 6 (2000), 324–329.
67. [67] Hanahanand, D., Weinberg, R.A., Hallmarks of cancer: the next generation. Cell 144 (2011), 646–674.
68. [68] Hanahanand, D., Weinberg, R.A., The hallmarks of cancer. Cell 100 (2000), 57–70.
69. [69] Oh, E.Y., Christensen, S.M., Ghanta, S., Jeong, J.C., Bucur, O., Glass, B., Montaser-Kouhsari, L., Knoblauch, N.W., Bertos, N., Saleh, S.M., Haibe-Kains, B., Park, M., Beck, A.H., Extensive rewiring of epithelial-stromal co-expression networks in breast cancer. Genome Biol., 16, 2015, 128.
70. [70] Chevet, E., Hetz, C., Samali, A., Endoplasmic reticulum stress-activated cell reprogramming in oncogenesis. Cancer Discov. 5 (2015), 586–597.
71. [71] Kato, H., Nishitoh, H., Stress responses from the endoplasmic reticulum in cancer. Front. Oncol., 5, 2015, 93.
72. [72] Wang, J., Yang, X., Zhang, J., Bridges between mitochondria oxidative stress, ER stress and mTOR signaling in pancreatic β cells. Cell. Signal., 2016 (Epub ahead of print).
73. [73] Iurlaro, R., Muñoz-Pinedo, C., Cell death induced by endoplasmic reticulum stress. FEBS J., 2015 (Epub ahead of print).
74. [74] Dufey, E., Urra, H., Hetz, C., ER proteostasis addiction in cancer biology: novel concepts. Semin. Cancer Biol. 33 (2015), 40–47.
75. [75] Tai, D., Wells, K., Arcaroli, J., Vanderbilt, C., Aisner, D.L., Messersmith, W.A., Lieu, C.H., Targeting the WNT signaling pathway in cancer therapeutics. Oncologist 20 (2015), 1189–1198.
76. [76] S. Basu, G. Haase, A. Ben-Ze'ev, Wnt signaling in cancer stem cells and colon cancer metastasis, F1000Res. 5. pii: F1000 Faculty Rev-699 (2016).
77. [77] Morris, S.L., Huang, S., Crosstalk of the Wnt/β-catenin pathway with other pathways in cancer cells. Genes Dis. 3 (2016), 41–47.
78. [78] Yang, K., Wang, X., Zhang, H., Wang, Z., Nan, G., Li, Y., Zhang, F., Mohammed, M.K., Haydon, R.C., Luu, H.H., Bi, Y., He, T.C., The evolving roles of canonical WNT signaling in stem cells and tumorigenesis: implications in targeted cancer therapies. Lab. Invest. 96 (2016), 116–136.
79. [79] Jaeger, S., Min, J., Nigsch, F., Camargo, M., Hutz, J., Cornett, A., Cleaver, S., Buckler, A., Jenkins, J.L., Causal network models for predicting compound targets and driving pathways in cancer. J. Biomol. Screen. 19 (2014), 791–802.
80. [80] Neapolitanand, R., Jiang, X., Inferring aberrant signal transduction pathways in ovarian cancer from TCGA data. Cancer Inform. 13 (2014), 29–36.
81. [81] Leiserson, M.D., Vandin, F., Wu, H.T., Dobson, J.R., Eldridge, J.V., Thomas, J.L., Papoutsaki, A., Kim, Y., Niu, B., McLellan, M., Lawrence, M.S., Gonzalez-Perez, A., Tamborero, D., Cheng, Y., Ryslik, G.A., Lopez-Bigas, N., Getz, G., Ding, L., Raphael, B.J., Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47 (2015), 106–114.
82. [82] Knaack, S.A., Siahpirani, A.F., Roy, S., A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components. Cancer Inform. 13 (2014), 69–84.
83. [83] Gevaert, O., Villalobos, V., Sikic, B.I., Plevritis, S.K., Identification of ovarian cancer driver genes by using module network integration of multi-omics data. Interface Focus., 3, 2013, 20130013.
84. [84] Hofree, M., Shen, J.P., Carter, H., Gross, A., Ideker, T., Network-based stratification of tumor mutations. Nat. Methods 10 (2013), 1108–1115.
85. [85] Marsh, S., Cancer pharmacogenetics. Methods Mol. Biol. 448 (2008), 437–446.
86. [86] Efferth, T., Volm, M., Pharmacogenetics for individualized cancer chemotherapy. Pharmacol. Ther. 107 (2005), 155–176.
87. [87] Giacomini, K.M., Brett, C.M., Altman, R.B., Benowitz, N.L., Dolan, M.E., Flockhart, D.A., Johnson, J.A., Hayes, D.F., Klein, T., Krauss, R.M., Kroetz, D.L., McLeod, H.L., Nguyen, A.T., Ratain, M.J., Relling, M.V., Reus, V., Roden, D.M., Schaefer, C.A., Shuldiner, A.R., Skaar, T., Tantisira, K., Tyndale, R.F., Wang, L., Weinshilboum, R.M., Weiss, S.T., Zineh, I., N. Pharmacogenetics Research, The pharmacogenetics research network: from SNP discovery to clinical drug response. Clin. Pharmacol. Ther. 81 (2007), 328–345.
88. [88] Changand, J.T., Nevins, J.R., GATHER: a systems approach to interpreting genomic signatures. Bioinformatics 22 (2006), 2926–2933.
89. [89] Weisenberger, D.J., Characterizing DNA methylation alterations from The Cancer Genome Atlas. J. Clin. Invest. 124 (2014), 17–23.
90. [90] Pradhan, M.P., Desai, A., Palakal, M.J., Systems biology approach to stage-wise characterization of epigenetic genes in lung adenocarcinoma. BMC Syst. Biol., 7, 2013, 141.
91. [91] Huang, T., Yang, J., Cai, Y.D., Novel candidate key drivers in the integrative network of genes, microRNAs, methylations, and copy number variations in squamous cell lung carcinoma. Biomed. Res. Int., 2015, 2015, 358125.
92. [92] Gnad, F., Doll, S., Manning, G., Arnott, D., Zhang, Z., Bioinformatics analysis of thousands of TCGA tumors to determine the involvement of epigenetic regulators in human cancer. BMC Genom., 16(Suppl. 8), 2015, S5.
93. [93] Marin-Kuan, M., Cavin, C., Delatour, T., Schilter, B., Ochratoxin A carcinogenicity involves a complex network of epigenetic mechanisms. Toxicon 52 (2008), 195–202.
94. [94] Jones, A., Teschendorff, A.E., Li, Q., Hayward, J.D., Kannan, A., Mould, T., West, J., Zikan, M., Cibula, D., Fiegl, H., Lee, S.H., Wik, E., Hadwin, R., Arora, R., Lemech, C., Turunen, H., Pakarinen, P., Jacobs, I.J., Salvesen, H.B., Bagchi, M.K., Bagchi, I.C., Widschwendter, M., Role of DNA methylation and epigenetic silencing of HAND2 in endometrial cancer development. PLoS Med., 10, 2013, e1001551.
95. [95] Fisher, J.N., Terao, M., Fratelli, M., Kurosaki, M., Paroni, G., Zanetti, A., Gianni, M., Bolis, M., Lupi, M., Tsykin, A., Goodall, G.J., Garattini, E., MicroRNA networks regulated by all-trans retinoic acid and Lapatinib control the growth: survival and motility of breast cancer cells. Oncotarget 6 (2015), 13176–13200.
96. [96] Mashima, T., Ushijima, M., Matsuura, M., Tsukahara, S., Kunimasa, K., Furuno, A., Saito, S., Kitamura, M., Soma-Nagae, T., Seimiya, H., Dan, S., Yamori, T., Tomida, A., Comprehensive transcriptomic analysis of molecularly targeted drugs in cancer for target pathway evaluation. Cancer Sci. 106 (2015), 909–920.
97. [97] Liand, Y., Zhang, Z., Potential microRNA-mediated oncogenic intercellular communication revealed by pan-cancer analysis. Sci. Rep., 4, 2014, 7097.
98. [98] Paci, P., Colombo, T., Farina, L., Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer. BMC Syst. Biol., 8, 2014, 83.
99. [99] Matsumura, N., Huang, Z., Mori, S., Baba, T., Fujii, S., Konishi, I., Iversen, E.S., Berchuck, A., Murphy, S.K., Epigenetic suppression of the TGF-beta pathway revealed by transcriptome profiling in ovarian cancer. Genome Res. 21 (2011), 74–82.
100. [100] Wang, J., Wu, G., Chen, L., Zhang, W., Integrated analysis of transcriptomic and proteomic datasets reveals information on protein expressivity and factors affecting translational efficiency. Methods Mol. Biol., 2015.
101. [101] Haiderand, S., Pal, R., Integrated analysis of transcriptomic and proteomic data. Curr. Genom. 14 (2013), 91–110.
102. [102] Naya, H., Urioste, J.I., Chang, Y.M., Rodrigues-Motta, M., Kremer, R., Gianola, D., A comparison between Poisson and zero-inflated Poisson regression models with an application to number of black spots in Corriedale sheep. Genet. Select. Evol.: GSE 40 (2008), 379–394.
103. [103] Thomas, P.D., Kejariwal, A., Campbell, M.J., Mi, H., Diemer, K., Guo, N., Ladunga, I., Ulitsky-Lazareva, B., Muruganujan, A., Rabkin, S., Vandergriff, J.A., Doremieux, O., PANTHER: a browsable database of gene products organized by biological function: using curated protein family and subfamily classification. Nucleic Acids Res. 31 (2003), 334–341.
104. [104] Huang da, W., Sherman, B.T., Lempicki, R.A., Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4 (2009), 44–57.
105. [105] Akbani, R., Ng, P.K., Werner, H.M., Shahmoradgoli, M., Zhang, F., Ju, Z., Liu, W., Yang, J.Y., Yoshihara, K., Li, J., Ling, S., Seviour, E.G., Ram, P.T., Minna, J.D., Diao, L., Tong, P., Heymach, J.V., Hill, S.M., Dondelinger, F., Stadler, N., Byers, L.A., Meric-Bernstam, F., Weinstein, J.N., Broom, B.M., Verhaak, R.G., Liang, H., Mukherjee, S., Lu, Y., Mills, G.B., A pan-cancer proteomic perspective on The Cancer Genome Atlas. Nat. Commun., 5, 2014, 3887.
106. [106] Akbani, R., Ng, P.K., Werner, H.M., Shahmoradgoli, M., Zhang, F., Ju, Z., Liu, W., Yang, J.Y., Yoshihara, K., Li, J., Ling, S., Seviour, E.G., Ram, P.T., Minna, J.D., Diao, L., Tong, P., Heymach, J.V., Hill, S.M., Dondelinger, F., Stadler, N., Byers, L.A., Meric-Bernstam, F., Weinstein, J.N., Broom, B.M., Verhaak, R.G., Liang, H., Mukherjee, S., Lu, Y., Mills, G.B., Corrigendum a pan-cancer proteomic perspective on the Cancer Genome Atlas. Nat. Commun., 6, 2015, 4852.
107. [107] Reimand, J., Wagih, O., Bader, G.D., The mutational landscape of phosphorylation signaling in cancer. Sci. Rep., 3, 2013, 2651.
108. [108] Barkaiand, N., Leibler, S., Robustness in simple biochemical networks. Nature 387 (1997), 913–917.
109. [109] Iadevaia, S., Lu, Y., Morales, F.C., Mills, G.B., Ram, P.T., Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res. 70 (2010), 6704–6714.
110. [110] Wulfkuhle, J.D., Edmiston, K.H., Liotta, L.A., Petricoin, E.F. 3rd, Technology insight: pharmacoproteomics for cancer–promises of patient-tailored medicine using protein microarrays. Nat. Clin. Pract. Oncol. 3 (2006), 256–268.
111. [111] Dang, C.V., O'Donnell, K.A., Zeller, K.I., Nguyen, T., Osthus, R.C., Li, F., The c-Myc target gene network. Semin. Cancer Biol. 16 (2006), 253–264.
112. [112] Yizhak, K., Chaneton, B., Gottlieb, E., Ruppin, E., Modeling cancer metabolism on a genome scale. Mol. Syst. Biol., 11, 2015, 817.
113. [113] Pornputtapong, N., Nookaew, I., Nielsen, J., Human metabolic atlas: an online resource for human metabolism. Database (Oxford), 2015, 2015, bav068.
114. [114] Li, L., Zhou, X., Ching, W.K., Wang, P., Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines. BMC Bioinform., 11, 2010, 501.
115. [115] Hur, M., Campbell, A.A., Almeida-de-Macedo, M., Li, L., Ransom, N., Jose, A., Crispin, M., Nikolau, B.J., Wurtele, E.S., A global approach to analysis and interpretation of metabolic data for plant natural product discovery. Nat. Prod. Rep. 30 (2013), 565–583.
116. [116] Asgari, Y., Zabihinpour, Z., Salehzadeh-Yazdi, A., Schreiber, F., Masoudi-Nejad, A., Alterations in cancer cell metabolism: the Warburg effect and metabolic adaptation. Genomics 105 (2015), 275–281.
117. [117] Gertsch, J., Botanical drugs, synergy, and network pharmacology: forth and back to intelligent mixtures. Planta Med. 77 (2011), 1086–1098.
118. [118] Xu, X., New concepts and approaches for drug discovery based on traditional Chinese medicine. Drug Discov. Today Technol. 3 (2006), 247–253.
119. [119] Cheung, F., TCM. Made in China. Nature 480 (2011), S82–83.
120. [120] Tangand, J., Aittokallio, T., Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr. Pharm. Des. 20 (2014), 23–36.
121. [121] Boranand, A.D., Iyengar, R., Systems approaches to polypharmacology and drug discovery. Curr. Opin. Drug Discov. Devel. 13 (2010), 297–309.
122. [122] Chakraborty, C., Doss, C.G., Chen, L., Zhu, H., Evaluating protein–protein interaction (PPI) networks for diseases pathway, target discovery, and drug-design using ‘in silico pharmacology'. Curr. Protein Pept. Sci. 15 (2014), 561–571.
123. [123] Ekins, S., Mestres, J., Testa, B., In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br. J. Pharmacol. 152 (2007), 9–20.
124. [124] Morrow, J.K., Tian, L., Zhang, S., Molecular networks in drug discovery. Crit. Rev. Biomed. Eng. 38 (2010), 143–156.
125. [125] Engin, H.B., Gursoy, A., Nussinov, R., Keskin, O., Network-based strategies can help mono- and poly-pharmacology drug discovery: a systems biology view. Curr. Pharm. Des. 20 (2014), 1201–1207.
126. [126] Azmi, A.S., Adopting network pharmacology for cancer drug discovery. Curr. Drug Discov. Technol. 10 (2013), 95–105.
127. [127] Korcsmáros, T., Szalay, M.S., Böde, C., Kovács, I.A., Csermely, P., How to design multi-target drugs. Expert Opin. Drug Discov. 2 (2007), 799–808.
128. [128] Jeong, H., Mason, S.P., Barabási, A.L., Oltvai, Z.N., Lethality and centrality in protein networks. Nature 411 (2001), 41–42.
129. [129] Hao da, C., Xiao, P.G., Network pharmacology: a Rosetta Stone for traditional Chinese medicine. Drug Dev. Res. 75 (2014), 299–312.
130. [130] Morphy, R., Kay, C., Rankovic, Z., From magic bullets to designed multiple ligands. Drug Discov. Today 9 (2004), 641–651.
131. [131] Hopkins, A.L., Mason, J.S., Overington, J.P., Can we rationally design promiscuous drugs?. Curr. Opin. Struct. Biol. 16 (2006), 127–136.
132. [132] Jackson, R.A., Chen, E.S., Synthetic lethal approaches for assessing combinatorial efficacy of chemotherapeutic drugs. Pharmacol. Ther., 2016 pii: S0163-7258(16)00015-2.
133. [133] Kitano, H., Towards a theory of biological robustness. Mol. Syst. Biol., 3, 2007, 137.
134. [134] Zhang, S., Shan, L., Li, Q., Wang, X., Li, S., Zhang, Y., Fu, J., Liu, X., Li, H., Zhang, W., Systematic analysis of the multiple bioactivities of green tea through a network pharmacology approach. Evid. Based Complement. Alternat. Med., 2014, 2014, 512081.
135. [135] Luo, F., Gu, J., Chen, L., Xu, X., Systems pharmacology strategies for anticancer drug discovery based on natural products. Mol. Biosyst. 10 (2014), 1912–1917.
136. [136] Liu, J., Pei, M., Zheng, C., Li, Y., Wang, Y., Lu, A., Yang, L., A systems-pharmacology analysis of herbal medicines used in health improvement treatment: predicting potential new drugs and targets. Evid. Based Complement. Alternat. Med., 2013, 2013, 938764.
137. [137] Wongand, A., Ma, B.B., Personalizing therapy for colorectal cancer. Clin. Gastroenterol. Hepatol. 12 (2014), 139–144.
138. [138] Teh, L.K., Hamzah, S., Hashim, H., Bannur, Z., Zakaria, Z.A., Hasbullani, Z., Shia, J.K., Fijeraid, H., Md Nor, A., Zailani, M., Ramasamy, P., Ngow, H., Sood, S., Salleh, M.Z., Potential of dihydropyrimidine dehydrogenase genotypes in personalizing 5-fluorouracil therapy among colorectal cancer patients. Ther. Drug Monit. 35 (2013), 624–630.
139. [139] Aksoy, B.A., Demir, E., Babur, O., Wang, W., Jing, X., Schultz, N., Sander, C., Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles. Bioinformatics 30 (2014), 2051–2059.
140. [140] Hanahan, D., Rethinking the war on cancer. Lancet 383 (2014), 558–563.
141. [141] Roskoski, R. Jr., The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacol. Res. 79 (2014), 34–74.
142. [142] Serkovaand, N., Boros, L.G., Detection of resistance to imatinib by metabolic profiling: clinical and drug development implications. Am. J. Pharmacogenom. 5 (2005), 293–302.
143. [143] Kim, D., Li, R., Dudek, S.M., Ritchie, M.D., Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer. J. Biomed. Inform. 56 (2015), 220–222.
144. [144] Jeong, H.H., Leem, S., Wee, K., Sohn, K.A., Integrative network analysis for survival-associated gene–gene interactions across multiple genomic profiles in ovarian cancer. J Ovarian Res., 8, 2015, 42.
145. [145] Wiench, B., Eichhorn, T., Paulsen, M., Efferth, T., Shikonin directly targets mitochondria and causes mitochondrial dysfunction in cancer cells. Evid. Based Complement. Alternat. Med., 2012, 2012, 726025.
146. [146] Wiench, B., Chen, Y.R., Paulsen, M., Hamm, R., Schröder, S., Yang, N.S., Efferth, T., Integration of different—omics technologies identifies inhibition of the IGF1R-Akt-mTOR signaling cascade involved in the cytotoxic effect of shikonin against leukemia cells. Evid. Based Complement. Alternat. Med., 2013, 2013, 818709.
147. [147] Zhao, Q., Assimopoulou, A.N., Klauck, S.M., Damianakos, H., Chinou, I., Kretschmer, N., Rios, J.L., Papageorgiou, V.P., Bauer, R., Efferth, T., Inhibition of c-MYC with involvement of ERK/JNK/MAPK and AKT pathways as a novel mechanism for shikonin and its derivatives in killing leukemia cells. Oncotarget 6 (2015), 38934–38951.
148. [148] Panossian, A., Hamm, R., Kadioglu, O., Wikman, G., Efferth, T., Synergy and antagonism of active constituents of ADAPT-232 on transcriptional level of metabolic regulation of isolated neuroglial cells. Front. Neurosci., 7, 2013, 16.
149. [149] Panossian, A., Hamm, R., Wikman, G., Efferth, T., Mechanism of action of Rhodiola, salidroside, tyrosol and triandrin in isolated neuroglial cells: an interactive pathway analysis of the downstream effects using RNA microarray data. Phytomedicine 21 (2014), 1325–1348.
150. [150] Panossian, A., Seo, E.J., Wikman, G., Efferth, T., Synergy assessment of fixed combinations of Herba Andrographidis and Radix Eleutherococci extracts by transcriptome-wide microarray profiling. Phytomedicine 22 (2015), 981–989.
151. [151] Martinez-Ledesma, E., Verhaak, R.G., Trevino, V., Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm. Sci. Rep., 5, 2015, 11966.
152. [152] Suarez-Kurtz, G., Vargens, D.D., Santoro, A.B., Hutz, M.H., de Moraes, M.E., Pena, S.D., Ribeiro-dos-Santos, A., Romano-Silva, M.A., Struchiner, C.J., Global pharmacogenomics: distribution of CYP3A5 polymorphisms and phenotypes in the Brazilian population. PLoS One, 9, 2014, e83472.
153. [153] Radovich, M., Clare, S.E., Atale, R., Pardo, I., Hancock, B.A., Solzak, J.P., Kassem, N., Mathieson, T., Storniolo, A.M., Rufenbarger, C., Lillemoe, H.A., Blosser, R.J., Choi, M.R., Sauder, C.A., Doxey, D., Henry, J.E., Hilligoss, E.E., Sakarya, O., Hyland, F.C., Hickenbotham, M., Zhu, J., Glasscock, J., Badve, S., Ivan, M., Liu, Y., Sledge, G.W., Schneider, B.P., Characterizing the heterogeneity of triple-negative breast cancers using microdissected normal ductal epithelium and RNA-sequencing. Breast Cancer Res. Treat. 143 (2014), 57–68.