Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

PLoS Computational Biology

Volumn 11, Issue 8, 2015, Pages

  Flobak, Åsmund ^a   Baudot, Anaïs ^b   Remy, Elisabeth ^b   Thommesen, Liv ^a   Thieffry, Denis ^c,d,e   Kuiper, Martin ^a   Lægreid, Astrid ^a  

^a NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^b AIX MARSEILLE UNIVERSITY   (France)

^c ECOLE NORMALE SUPÉRIEURE   (France)

^d PSL RESEARCH UNIVERSITY   (France)

^e INSERM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION PROGRAMS; CELL CULTURE; CELL SIGNALING; CELLS; DISEASES; PATIENT TREATMENT; SIGNALING;

ANTICANCER DRUG; BACKGROUND KNOWLEDGE; CELL FATE DECISION; DECISION NETWORK; DRUG COMBINATIONS; DYNAMICAL MODELING; EXPERIMENTAL TESTING; GASTRIC CANCER CELLS; LOGICAL MODELS; SEARCH SPACES;

CANCER CELLS;

MITOGEN ACTIVATED PROTEIN KINASE KINASE INHIBITOR; PHOSPHATIDYLINOSITOL 3 KINASE INHIBITOR; PROTEIN KINASE B INHIBITOR; PROTEIN SERINE THREONINE KINASE INHIBITOR; TAK1 INHIBITOR; TRANSCRIPTION FACTOR FOXO; UNCLASSIFIED DRUG; ANTINEOPLASTIC AGENT;

ARTICLE; CANCER COMBINATION CHEMOTHERAPY; CELL ASSAY; CELL FATE; CELL PROLIFERATION; COMPUTER MODEL; COMPUTER PROGRAM; COMPUTER SIMULATION; CONTROLLED STUDY; DRUG POTENTIATION; ENZYME INHIBITION; GASTRIC CANCER CELL LINE; HUMAN; HUMAN CELL; INTRACELLULAR SIGNALING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PROCESS MODEL; BIOLOGICAL MODEL; BIOLOGY; DRUG DEVELOPMENT; DRUG EFFECTS; PROCEDURES; STOMACH NEOPLASMS; TUMOR CELL LINE;

ANTINEOPLASTIC AGENTS; CELL LINE, TUMOR; CELL PROLIFERATION; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DRUG DISCOVERY; DRUG SYNERGISM; HUMANS; MODELS, BIOLOGICAL; STOMACH NEOPLASMS;

EID: 84940778330     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004426     Document Type: Article

References (68)

1. Hanahan D, Weinberg R a, Hallmarks of cancer: the next generation. Cell. Elsevier Inc.; 2011;144: 646–74. doi: 10.1016/j.cell.2011.02.013 21376230
2. Al-Lazikani B, Banerji U, Workman P, Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol. Nature Publishing Group; 2012;30: 679–92. doi: 10.1038/nbt.2284 22781697
3. Creixell P, Schoof EM, Erler JT, Linding R, Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol. 2012;30: 842–8. doi: 10.1038/nbt.2345 22965061
4. Crystal AS, Shaw AT, Sequist L V, Friboulet L, Niederst MJ, Lockerman EL, et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science. 2014; 1–12. doi: 10.1126/science.1254721 25394791
5. Borisy A a, Elliott PJ, Hurst NW, Lee MS, Lehar J, Price ER, et al. Systematic discovery of multicomponent therapeutics. Proc Natl Acad Sci U S A. 2003;100: 7977–82. doi: 10.1073/pnas.1337088100 12799470
6. Klinger B, Sieber A, Fritsche-Guenther R, Witzel F, Berry L, Schumacher D, et al. Network quantification of EGFR signaling unveils potential for targeted combination therapy. Mol Syst Biol. 2013;9: 673. doi: 10.1038/msb.2013.29 23752269
7. Bansal M, Yang J, Karan C, Menden MP, Costello JC, Tang H, et al. A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol. Nature Publishing Group; 2014; doi: 10.1038/nbt.3052 25419740
8. Mathews Griner L a, Guha R, Shinn P, Young RM, Keller JM, Liu D, et al. High-throughput combinatorial screening identifies drugs that cooperate with ibrutinib to kill activated B-cell-like diffuse large B-cell lymphoma cells. Proc Natl Acad Sci U S A. 2014;111: 2349–2354. doi: 10.1073/pnas.1311846111 24469833
9. Miller ML, Molinelli EJ, Nair JS, Sheikh T, Samy R, Jing X, et al. Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma Identifies CDK4 and IGF1R as Synergistic Drug Targets. Sci Signal. 2013;6: ra85. doi: 10.1126/scisignal.2004014 24065146
10. Albert I, Thakar J, Li S, Zhang R, Albert R, Boolean network simulations for life scientists. Source Code Biol Med. 2008;3: 16. doi: 10.1186/1751-0473-3-16 19014577
11. Sahin O, Fröhlich H, Löbke 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. doi: 10.1186/1752-0509-3-1 19118495
12. Calzone L, Tournier L, Fourquet S, Thieffry D, Zhivotovsky B, Barillot E, et al. Mathematical modelling of cell-fate decision in response to death receptor engagement. PLoS Comput Biol. 2010;6: e1000702. doi: 10.1371/journal.pcbi.1000702 20221256
13. Wang R-S, Saadatpour A, Albert R, Boolean modeling in systems biology: an overview of methodology and applications. Phys Biol. 2012;9: 055001. doi: 10.1088/1478-3975/9/5/055001 23011283
14. Bérenguier D, Chaouiya C, Monteiro PT, Naldi A, Remy E, Thieffry D, et al. Dynamical modeling and analysis of large cellular regulatory networks. Chaos. 2013;23: 025114. doi: 10.1063/1.4809783 23822512
15. Le Novère N, Quantitative and logic modelling of molecular and gene networks. Nat Rev Genet. Nature Publishing Group; 2015;16: 146–58. doi: 10.1038/nrg3885 25645874
16. Zhang R, Shah MV, Yang J, Nyland SB, Liu X, Yun JK, et al. Network model of survival signaling in large granular lymphocyte leukemia. Proc Natl Acad Sci U S A. 2008;105: 16308–16313. doi: 10.1073/pnas.0806447105 18852469
17. Schlatter R, Schmich K, Avalos Vizcarra I, Scheurich P, Sauter T, Borner C, et al. ON/OFF and beyond—a boolean model of apoptosis. PLoS Comput Biol. 2009;5: e1000595. doi: 10.1371/journal.pcbi.1000595 20011108
18. Saadatpour A, Wang R-S, Liao A, Liu X, Loughran TP, Albert I, et al. Dynamical and Structural Analysis of a T Cell Survival Network Identifies Novel Candidate Therapeutic Targets for Large Granular Lymphocyte Leukemia. PLoS Computational Biology. 2011;7: e1002267. doi: 10.1371/journal.pcbi.1002267 22102804
19. Schlatter R, Schmich K, Lutz A, Trefzger J, Sawodny O, Ederer M, et al. Modeling the TNFα-induced apoptosis pathway in hepatocytes. PLoS One. 2011;6: e18646. doi: 10.1371/journal.pone.0018646 21533085
20. Poltz R, Naumann M, Dynamics of p53 and NF-κB regulation in response to DNA damage and identification of target proteins suitable for therapeutic intervention. BMC Systems Biology. 2012;6: 125. doi: 10.1186/1752-0509-6-125 22979979
21. Grieco L, Calzone L, Bernard-Pierrot I, Radvanyi F, Kahn-Perlès B, Thieffry D, Miyano S, Integrative Modelling of the Influence of MAPK Network on Cancer Cell Fate Decision. PLoS Computational Biology. 2013;9: e1003286. doi: 10.1371/journal.pcbi.1003286 24250280
22. Fumiã HF, Martins ML, Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes. PLoS One. 2013;8: e69008. doi: 10.1371/journal.pone.0069008 23922675
23. Von der Heyde S, Bender C, Henjes F, Sonntag J, Korf U, Beißbarth T, Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines. BMC Syst Biol. 2014;8: 75. doi: 10.1186/1752-0509-8-75 24970389
24. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2011;39: D945–50. doi: 10.1093/nar/gkq929 20952405
25. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin A a, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483: 603–7. doi: 10.1038/nature11003 22460905
26. Naldi A, Remy E, Thieffry D, Chaouiya C, Dynamically consistent reduction of logical regulatory graphs. Theoretical Computer Science. Elsevier B.V.; 2011;412: 2207–2218. doi: 10.1016/j.tcs.2010.10.021
27. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41: D955–61. doi: 10.1093/nar/gks1111 23180760
28. Loewe S, The problem of synergism and antagonism of combined drugs. Arzneimittelforschung. 1953;3: 285–90. 13081480
29. Chou T-C, Talalay P, Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv Enzyme Regul. 1984;22: 27–55. 6382953
30. Shelton JG, Steelman LS, Lee JT, Knapp SL, Blalock WL, Moye PW, et al. Effects of the RAF/MEK/ERK and PI3K/AKT signal transduction pathways on the abrogation of cytokine-dependence and prevention of apoptosis in hematopoietic cells. Oncogene. 2003;22: 2478–92. doi: 10.1038/sj.onc.1206321 12717425
31. Iadevaia S, Lu Y, Morales FC, Mills GB, Ram PT, Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res. 2010;70: 6704–14. doi: 10.1158/0008-5472.CAN-10-0460 20643779
32. Meng J, Dai B, Fang B, Bekele BN, Bornmann WG, Sun D, et al. Combination treatment with MEK and AKT inhibitors is more effective than each drug alone in human non-small cell lung cancer in vitro and in vivo. PLoS One. 2010;5: e14124. doi: 10.1371/journal.pone.0014124 21124782
33. Migliardi G, Sassi F, Torti D, Galimi F, Zanella ER, Buscarino M, et al. Inhibition of MEK and PI3K/mTOR suppresses tumor growth but does not cause tumor regression in patient-derived xenografts of RAS-mutant colorectal carcinomas. Clin Cancer Res. 2012;18: 2515–25. doi: 10.1158/1078-0432.CCR-11-2683 22392911
34. Kandil E, Tsumagari K, Ma J, Abd Elmageed ZY, Li X, Slakey D, et al. Synergistic inhibition of thyroid cancer by suppressing MAPK/PI3K/AKT pathways. J Surg Res. Elsevier Ltd; 2013;184: 898–906. doi: 10.1016/j.jss.2013.03.052 23602735
35. Eijkelenboom A, Burgering BMT, FOXOs: signalling integrators for homeostasis maintenance. Nat Rev Mol Cell Biol. 2013;14: 83–97. doi: 10.1038/nrm3507 23325358
36. Shankar S, Chen Q, Srivastava RK, Inhibition of PI3K/AKT and MEK/ERK pathways act synergistically to enhance antiangiogenic effects of EGCG through activation of FOXO transcription factor. J Mol Signal. 2008;3: 7. doi: 10.1186/1750-2187-3-7 18355401
37. Arthur JSC, Ley SC, Mitogen-activated protein kinases in innate immunity. Nat Rev Immunol. Nature Publishing Group; 2013;13: 679–92. doi: 10.1038/nri3495 23954936
38. Kim S, Kim Y, Lee J, Chung J, Regulation of FOXO1 by TAK1-Nemo-like kinase pathway. J Biol Chem. 2010;285: 8122–9. doi: 10.1074/jbc.M110.101824 20061393
39. Lam EW-F, Brosens JJ, Gomes AR, Koo C-Y, Forkhead box proteins: tuning forks for transcriptional harmony. Nat Rev Cancer. Nature Publishing Group; 2013;13: 482–95. doi: 10.1038/nrc3539 23792361
40. Lehár J, Krueger AS, Avery W, Heilbut AM, Johansen LM, Price ER, et al. Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat Biotechnol. 2009;27: 659–66. doi: 10.1038/nbt.1549 19581876
41. Bozic I, Reiter JG, Allen B, Antal T, Chatterjee K, Shah P, et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife. 2013;2: e00747. doi: 10.7554/eLife.00747 23805382
42. Kopetz S, Lemos R, Powis G, The promise of patient-derived xenografts: the best laid plans of mice and men. Clin Cancer Res. 2012;18: 5160–2. doi: 10.1158/1078-0432.CCR-12-2408 22912394
43. Wilding JL, Bodmer WF, Cancer cell lines for drug discovery and development. Cancer Research. 2014;74: 2377–2384. doi: 10.1158/0008-5472.CAN-13-2971 24717177
44. Garralda E, Paz K, López-Casas PP, Jones S, Katz A, Kann LM, et al. Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment. Clin Cancer Res. 2014;20: 2476–84. doi: 10.1158/1078-0432.CCR-13-3047 24634382
45. Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C, Emerging landscape of oncogenic signatures across human cancers. Nat Genet. Nature Publishing Group; 2013;45: 1127–1133. doi: 10.1038/ng.2762 24071851
46. Bass AJ, Thorsson V, Shmulevich I, Reynolds SM, Miller M, Bernard B, et al. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. Nature Publishing Group; 2014;513: 202–209. doi: 10.1038/nature13480 25079317
47. Lee MJ, Ye AS, Gardino AK, Heijink AM, Sorger PK, MacBeath G, et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell. Elsevier Inc.; 2012;149: 780–94. doi: 10.1016/j.cell.2012.03.031 22579283
48. Pritchard JR, Bruno PM, Gilbert LA, Capron KL, Lauffenburger DA, Hemann MT, Defining principles of combination drug mechanisms of action. Proc Natl Acad Sci U S A. 2013;110: E170–9. doi: 10.1073/pnas.1210419110 23251029
49. Zielinski R, Przytycki PF, Zheng J, Zhang D, Przytycka TM, Capala J, The crosstalk between EGF, IGF, and Insulin cell signaling pathways—computational and experimental analysis. BMC Syst Biol. BioMed Central Ltd; 2009;3: 88. doi: 10.1186/1752-0509-3-88 19732446
50. Bauer AL, Jackson TL, Jiang Y, Rohlf T, Receptor cross-talk in angiogenesis: mapping environmental cues to cell phenotype using a stochastic, Boolean signaling network model. J Theor Biol. Elsevier; 2010;264: 838–46. doi: 10.1016/j.jtbi.2010.03.025 20307549
51. Singh A, Nascimento JM, Kowar S, Busch H, Boerries M, Boolean approach to signalling pathway modelling in HGF-induced keratinocyte migration. Bioinformatics. 2012;28: i495–i501. doi: 10.1093/bioinformatics/bts410 22962472
52. Nelander S, Wang W, Nilsson B, She Q-B, Pratilas C, Rosen N, et al. Models from experiments: combinatorial drug perturbations of cancer cells. Mol Syst Biol. 2008;4: 216. doi: 10.1038/msb.2008.53 18766176
53. Greco WR, Bravo G, Parsons JC, The search for synergy: a critical review from a response surface perspective. Pharmacol Rev. 1995;47: 331–85. 7568331
54. Wittmann DM, Krumsiek J, Saez-Rodriguez J, Lauffenburger D a, Klamt S, Theis FJ, Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Syst Biol. 2009;3: 98. doi: 10.1186/1752-0509-3-98 19785753
55. Ooi CH, Ivanova T, Wu J, Lee M, Tan IB, Tao J, et al. Oncogenic pathway combinations predict clinical prognosis in gastric cancer. PLoS Genet. 2009;5: e1000676. doi: 10.1371/journal.pgen.1000676 19798449
56. Tan IB, Ivanova T, Lim KH, Ong CW, Deng N, Lee J-SJ, et al. Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy. Gastroenterology. 2011;141: 476–85, 485.e1–11. doi: 10.1053/j.gastro.2011.04.042 21684283
57. Wadhwa R, Song S, Lee J-S, Yao Y, Wei Q, Ajani J a, Gastric cancer-molecular and clinical dimensions. Nat Rev Clin Oncol. Nature Publishing Group; 2013;10: 643–55. doi: 10.1038/nrclinonc.2013.170 24061039
58. Orditura M, Galizia G, Sforza V, Gambardella V, Fabozzi A, Laterza MM, et al. Treatment of gastric cancer. World J Gastroenterol. 2014;20: 1635–49. doi: 10.3748/wjg.v20.i7.1635 24587643
59. See for instance clinical trials NCT01347866, NCT01363232, NCT01337765 and NCT00996892 (https://www.clinicaltrials.gov/).
60. See for instance clinical trials NCT01476137, NCT01138085, NCT01907815 and NCT01333475 (https://www.clinicaltrials.gov/).
61. Thomas R, Regulatory networks seen as asynchronous automata: A logical description. Journal of Theoretical Biology. 1991;153: 1–23. doi: 10.1016/S0022-5193(05)80350-9
62. Clevers H, Nusse R, Wnt/β-catenin signaling and disease. Cell. 2012;149: 1192–205. doi: 10.1016/j.cell.2012.05.012 22682243
63. Carayol N, Wang C-Y, IKKalpha stabilizes cytosolic beta-catenin by inhibiting both canonical and non-canonical degradation pathways. Cell Signal. 2006;18: 1941–6. doi: 10.1016/j.cellsig.2006.02.014 16616828
64. Oeckinghaus A, Hayden MS, Ghosh S, Crosstalk in NF-κB signaling pathways. Nat Immunol. 2011;12: 695–708. doi: 10.1038/ni.2065 21772278
65. Mita H, Toyota M, Aoki F, Akashi H, Maruyama R, Sasaki Y, et al. A novel method, digital genome scanning detects KRAS gene amplification in gastric cancers: involvement of overexpressed wild-type KRAS in downstream signaling and cancer cell growth. BMC Cancer. 2009;9: 198. doi: 10.1186/1471-2407-9-198 19545448
66. Asciutti S, Akiri G, Grumolato L, Vijayakumar S, Aaronson S a. Diverse mechanisms of Wnt activation and effects of pathway inhibition on proliferation of human gastric carcinoma cells. Oncogene. Nature Publishing Group; 2011;30: 956–66. doi: 10.1038/onc.2010.475 21042278
67. Naldi A, Berenguier D, Fauré A, Lopez F, Thieffry D, Chaouiya C, Logical modelling of regulatory networks with GINsim 2.3. Biosystems. 2009;97: 134–9. doi: 10.1016/j.biosystems.2009.04.008 19426782
68. Chou T-C, Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev. 2006;58: 621–81. doi: 10.1124/pr.58.3.10 16968952