Angiogenic Activity of Breast Cancer Patients’ Monocytes Reverted by Combined Use of Systems Modeling and Experimental Approaches

PLoS Computational Biology

Volumn 11, Issue 3, 2015, Pages

  Guex, Nicolas ^a   Crespo, Isaac ^a   Bron, Sylvian ^a   Ifticene Treboux, Assia ^b   Faes van’t Hull, Eveline ^a   Kharoubi, Solange ^a   Liechti, Robin ^a   Werffeli, Patricia ^b   Ibberson, Mark ^a   Majo, Francois ^c   Nicolas, Michäel ^c   Laurent, Julien ^d   Garg, Abhishek ^e   Zaman, Khalil ^b   Lehr, Hans Anton ^a   Stevenson, Brian J ^a   Rüegg, Curzio ^f   Coukos, George ^a   Delaloye, Jean François ^b   Xenarios, Ioannis ^a   Doucey, Marie Agnès ^a   more..

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b LAUSANNE UNIVERSITY HOSPITAL   (Switzerland)

^c HÔPITAL OPHTALMIQUE JULES GONIN   (Switzerland)

^d MERCK KGAA   (Germany)

^e HARVARD MEDICAL SCHOOL   (United States)

^f UNIVERSITY OF FRIBOURG   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD; DISEASES; GENE EXPRESSION; PATIENT TREATMENT;

ANGIOGENESIS; BREAST CANCER; CANCER PATIENTS; CANCER PROGRESSION; EXPERIMENTAL APPROACHES; IN-SILICO; MOUSE TUMORS; SYSTEMS MODELING APPROACH; TUMOR GROWTH; TUMOR VASCULARIZATION;

TUMORS;

ANGIOPOIETIN 1; ANGIOPOIETIN 2; ANGIOPOIETIN RECEPTOR; CD11B ANTIGEN; CD14 ANTIGEN; GELATINASE B; INTERLEUKIN 10; INTERLEUKIN 1BETA; INTERLEUKIN 2; INTERLEUKIN 6; PIGF PROTEIN; PROTEIN INHIBITOR; TIE 2 KINASE INHIBITOR; TRANSFORMING GROWTH FACTOR BETA; TRANSFORMING GROWTH FACTOR BETA RECEPTOR 1; TUMOR NECROSIS FACTOR ALPHA; UNCLASSIFIED DRUG; VASCULOTROPIN RECEPTOR 1; VASCULOTROPIN RECEPTOR 2; VASCULOTROPIN RECEPTOR 3; CYTOKINE;

ADULT; ANGIOGENESIS; ANTINEOPLASTIC ACTIVITY; ARTICLE; BREAST CANCER; BREAST CARCINOGENESIS; CANCER PATIENT; CLINICAL ARTICLE; COMPUTER MODEL; CONTROLLED STUDY; CORNEA NEOVASCULARIZATION; CYTOKINE PRODUCTION; CYTOKINE RELEASE; DYNAMICS; EXPERIMENTAL STUDY; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMAN; HUMAN CELL; HUMAN TISSUE; IMMUNE RESPONSE; IMMUNOLOGY; IN VITRO STUDY; INFORMATION SCIENCE; IRIS VASCULARIZATION; MATHEMATICAL MODEL; MONOCYTE; MONONUCLEAR CELL; MOUSE; NONHUMAN; PHENOTYPE; PREDICTION; PROTEIN TARGETING; RECURRENCE FREE SURVIVAL; TIE 2 EXPRESSING MONOCYTE; TUMOR MICROENVIRONMENT; TUMOR VASCULARIZATION; ANIMAL; BIOLOGICAL MODEL; BIOLOGY; BREAST TUMOR; CELL LINE; CHEMISTRY; CLASSIFICATION; EXPERIMENTAL NEOPLASM; FEMALE; KAPLAN MEIER METHOD; METABOLISM; MIDDLE AGED; MORTALITY; NEOVASCULARIZATION (PATHOLOGY); PATHOPHYSIOLOGY; PHYSIOLOGY; SIGNAL TRANSDUCTION; TRANSGENIC MOUSE;

ANIMALS; BREAST NEOPLASMS; CELL LINE; COMPUTATIONAL BIOLOGY; CYTOKINES; FEMALE; HUMANS; KAPLAN-MEIER ESTIMATE; MICE; MICE, TRANSGENIC; MIDDLE AGED; MODELS, BIOLOGICAL; MONOCYTES; NEOPLASMS, EXPERIMENTAL; NEOVASCULARIZATION, PATHOLOGIC; PHENOTYPE; SIGNAL TRANSDUCTION;

EID: 84926372371     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004050     Document Type: Article

References (60)

1. Schlatter R, Philippi N, Wangorsch G, Pick R, Sawodny O, et al. (2011) Integration of Boolean models exemplified on hepatocyte signal transduction. Briefings in bioinformatics.
2. Thomas R, Thieffry D, Kaufman M, (1995) Dynamical behaviour of biological regulatory networks—I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. Bulletin of mathematical biology 57: 247–276. 7703920
3. Huang S, Eichler G, Bar-Yam Y, Ingber DE, (2005) Cell fates as high-dimensional attractor states of a complex gene regulatory network. Physical review letters 94: 128701. 15903968
4. Damiani C, Serra R, Villani M, Kauffman SA, Colacci A, (2011) Cell-cell interaction and diversity of emergent behaviours. IET Syst Biol 5: 137–144. doi: 10.1049/iet-syb.2010.0039 21405202
5. Millat T, Wolkenhauer O, Fischer RJ, Bahl H, (2011) Modeling of cellular processes: methods, data, and requirements. Methods Mol Biol 696: 429–447. doi: 10.1007/978-1-60761-987-1_27 21063964
6. Voit EO, (2008) Modelling metabolic networks using power-laws and S-systems. Essays Biochem 45: 29–40. doi: 10.1042/BSE0450029 18793121
7. Chaves M, Tournier L, Gouze JL, (2010) Comparing Boolean and piecewise affine differential models for genetic networks. Acta Biotheor 58: 217–232. doi: 10.1007/s10441-010-9097-6 20665073
8. Giacomantonio CE, Goodhill GJ, (2010) A Boolean model of the gene regulatory network underlying Mammalian cortical area development. PLoS Comput Biol 6. doi: 10.1371/journal.pcbi.1001039 21203484
9. Garg A, Di Cara A, Xenarios I, Mendoza L, De Micheli G, (2008) Synchronous versus asynchronous modeling of gene regulatory networks. Bioinformatics 24: 1917–1925. doi: 10.1093/bioinformatics/btn336 18614585
10. Philippi N, Walter D, Schlatter R, Ferreira K, Ederer M, et al. (2009) Modeling system states in liver cells: survival, apoptosis and their modifications in response to viral infection. BMC Syst Biol 3: 97. doi: 10.1186/1752-0509-3-97 19772631
11. Samaga R, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Klamt S, (2009) The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput Biol 5: e1000438. doi: 10.1371/journal.pcbi.1000438 19662154
12. Schlatter R, Schmich K, Avalos Vizcarra I, Scheurich P, Sauter T, et al. (2009) ON/OFF and beyond—a boolean model of apoptosis. PLoS Comput Biol 5: e1000595. doi: 10.1371/journal.pcbi.1000595 20011108
13. Thakar J, Albert R, (2010) Boolean models of within-host immune interactions. Curr Opin Microbiol 13: 377–381. doi: 10.1016/j.mib.2010.04.003 20462786
14. Jack J, Wambaugh JF, Shah I, (2011) Simulating quantitative cellular responses using asynchronous threshold Boolean network ensembles. BMC Syst Biol 5: 109. doi: 10.1186/1752-0509-5-109 21745399
15. Morris MK, Saez-Rodriguez J, Clarke DC, Sorger PK, Lauffenburger DA, (2011) Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS Comput Biol 7: e1001099. doi: 10.1371/journal.pcbi.1001099 21408212
16. Saez-Rodriguez J, Alexopoulos LG, Zhang M, Morris MK, Lauffenburger DA, et al. (2011) Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Res 71: 5400–5411. doi: 10.1158/0008-5472.CAN-10-4453 21742771
17. Veliz-Cuba A, Stigler B, (2011) Boolean models can explain bistability in the lac operon. J Comput Biol 18: 783–794. doi: 10.1089/cmb.2011.0031 21563979
18. Wang H, Lu HH, Chueh TH, (2011) Constructing biological pathways by a two-step counting approach. PLoS One 6: e20074. doi: 10.1371/journal.pone.0020074 21673799
19. Wang RS, Albert R, (2011) Elementary signaling modes predict the essentiality of signal transduction network components. BMC Syst Biol 5: 44. doi: 10.1186/1752-0509-5-44 21426566
20. Wittmann DM, Krumsiek J, Saez-Rodriguez J, Lauffenburger DA, Klamt S, et al. (2009) Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Syst Biol 3: 98. doi: 10.1186/1752-0509-3-98 19785753
21. Rafii S, Lyden D, Benezra R, Hattori K, Heissig B, (2002) Vascular and haematopoietic stem cells: novel targets for anti-angiogenesis therapy? Nat Rev Cancer 2: 826–835. 12415253
22. Carmeliet P, Jain RK, (2000) Angiogenesis in cancer and other diseases. Nature 407: 249–257. 11001068
23. Burri PH, Djonov V, (2002) Intussusceptive angiogenesis—the alternative to capillary sprouting. Mol Aspects Med 23: S1–27. 12537983
24. Coffelt SB, Lewis CE, Naldini L, Brown JM, Ferrara N, et al. (2010) Elusive identities and overlapping phenotypes of proangiogenic myeloid cells in tumors. Am J Pathol 176: 1564–1576. doi: 10.2353/ajpath.2010.090786 20167863
25. Leek RD, Lewis CE, Whitehouse R, Greenall M, Clarke J, et al. (1996) Association of macrophage infiltration with angiogenesis and prognosis in invasive breast carcinoma. Cancer Res 56: 4625–4629. 8840975
26. Leek RD, Landers RJ, Harris AL, Lewis CE, (1999) Necrosis correlates with high vascular density and focal macrophage infiltration in invasive carcinoma of the breast. Br J Cancer 79: 991–995. 10070902
27. Bolat F, Kayaselcuk F, Nursal TZ, Yagmurdur MC, Bal N, et al. (2006) Microvessel density, VEGF expression, and tumor-associated macrophages in breast tumors: correlations with prognostic parameters. J Exp Clin Cancer Res 25: 365–372. 17167977
28. Bingle L, Lewis CE, Corke KP, Reed MW, Brown NJ, (2006) Macrophages promote angiogenesis in human breast tumour spheroids in vivo. Br J Cancer 94: 101–107. 16404363
29. Ribatti D, (2009) The paracrine role of Tie-2-expressing monocytes in tumor angiogenesis. Stem Cells Dev 18: 703–706. doi: 10.1089/scd.2008.0385 19186995
30. Sickert D, Aust DE, Langer S, Baretton GB, Dieter P, (2007) Characterization of macrophage subpopulations and microvessel density in carcinomas of the gastrointestinal tract. Anticancer Res 27: 1693–1700. 17595799
31. Murdoch C, Muthana M, Coffelt SB, Lewis CE, (2008) The role of myeloid cells in the promotion of tumour angiogenesis. Nat Rev Cancer 8: 618–631. doi: 10.1038/nrc2444 18633355
32. Onita T, Ji PG, Xuan JW, Sakai H, Kanetake H, et al. (2002) Hypoxia-induced, perinecrotic expression of endothelial Per-ARNT-Sim domain protein-1/hypoxia-inducible factor-2alpha correlates with tumor progression, vascularization, and focal macrophage infiltration in bladder cancer. Clin Cancer Res 8: 471–480. 11839666
33. Takanami I, Takeuchi K, Kodaira S, (1999) Tumor-associated macrophage infiltration in pulmonary adenocarcinoma: association with angiogenesis and poor prognosis. Oncology 57: 138–142. 10461061
34. De Palma M, Venneri MA, Galli R, Sergi Sergi L, Politi LS, et al. (2005) Tie2 identifies a hematopoietic lineage of proangiogenic monocytes required for tumor vessel formation and a mesenchymal population of pericyte progenitors. Cancer Cell 8: 211–226. 16169466
35. Venneri MA, De Palma M, Ponzoni M, Pucci F, Scielzo C, et al. (2007) Identification of proangiogenic TIE2-expressing monocytes (TEMs) in human peripheral blood and cancer. Blood 109: 5276–5285. 17327411
36. Pardali E, Goumans MJ, Ten Dijke P, (2010) Signaling by members of the TGF-beta family in vascular morphogenesis and disease. Trends Cell Biol 20: 556–567. doi: 10.1016/j.tcb.2010.06.006 20656490
37. Nussenbaum F, Herman IM Tumor angiogenesis: insights and innovations. J Oncol 2010: 132641. doi: 10.1155/2010/132641 20445741
38. Ibberson M, Bron S, Guex N, Faes-van't Hull E, Ifticene-Treboux A, et al. (2013) TIE-2 and VEGFR kinase activities drive immunosuppressive function of TIE-2-expressing monocytes in human breast tumors. Clin Cancer Res 19: 3439–3449. doi: 10.1158/1078-0432.CCR-12-3181 23649001
39. Gimbrone MA, Jr.Cotran RS, Leapman SB, Folkman J, (1974) Tumor growth and neovascularization: an experimental model using the rabbit cornea. J Natl Cancer Inst 52: 413–427. 4816003
40. Laurent J, Hull EF, Touvrey C, Kuonen F, Lan Q, et al. (2011) Proangiogenic factor PlGF programs CD11b(+) myelomonocytes in breast cancer during differentiation of their hematopoietic progenitors. Cancer Res 71: 3781–3791. doi: 10.1158/0008-5472.CAN-10-3684 21507936
41. Samaga R, Von Kamp A, Klamt S, (2010) Computing combinatorial intervention strategies and failure modes in signaling networks. Journal of computational biology: a journal of computational molecular cell biology 17: 39–53. doi: 10.1089/cmb.2009.0121 20078396
42. Klamt S, Saez-Rodriguez J, Lindquist JA, Simeoni L, Gilles ED, (2006) A methodology for the structural and functional analysis of signaling and regulatory networks. BMC bioinformatics 7: 56. 16464248
43. Garg A, Di Cara A, Xenarios I, De Micheli G, Mohanram K (2010) Efficient method for generating minimal intervention sets in gene regulatory networks. Annual Joint Conference on Systems Biology, Regulatory Genomics, and Reverse Engineering Challenges (RECOMBSAT). New York City.
44. Garg A, Mohanram K, Di Cara A, Deguerce G, Ibberson M, et al. (2012) Efficiently Computation of Minimal Perturbation Sets in a Gene Regulatory Network.
45. Ojalvo LS, King W, Cox D, Pollard JW, (2009) High-density gene expression analysis of tumor-associated macrophages from mouse mammary tumors. Am J Pathol 174: 1048–1064. doi: 10.2353/ajpath.2009.080676 19218341
46. Pettersen JS, Fuentes-Duculan J, Suarez-Farinas M, Pierson KC, Pitts-Kiefer A, et al. (2011) Tumor-associated macrophages in the cutaneous SCC microenvironment are heterogeneously activated. J Invest Dermatol 131: 1322–1330. doi: 10.103/jid.2011.9 21307877
47. Ojalvo LS, Whittaker CA, Condeelis JS, Pollard JW, (2010) Gene expression analysis of macrophages that facilitate tumor invasion supports a role for Wnt-signaling in mediating their activity in primary mammary tumors. J Immunol 184: 702–712. doi: 10.4049/jimmunol.0902360 20018620
48. Autiero M, Waltenberger J, Communi D, Kranz A, Moons L, et al. (2003) Role of PlGF in the intra- and intermolecular cross talk between the VEGF receptors Flt1 and Flk1. Nat Med 9: 936–943. 12796773
49. Jin P, Han TH, Ren J, Saunders S, Wang E, et al. (2010) Molecular signatures of maturing dendritic cells: implications for testing the quality of dendritic cell therapies. J Transl Med 8: 4. doi: 10.1186/1479-5876-8-4 20078880
50. Gyorffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, et al. (2010) An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat 123: 725–731. doi: 10.1007/s10549-009-0674-9 20020197
51. Kahlem P, Di Cara A, Durot M, Hancock J, Klipp E, et al. (2011) Strengths and Weaknesses of Selected Modeling Methods Used in Systems Biology. In: Yang N-S, editor. Systems and Computational Biology—Bioinformatics and Computational Modeling.
52. Navlakha S, Bar-Joseph Z, (2011) Algorithms in nature: the convergence of systems biology and computational thinking. Molecular systems biology 7: 546. doi: 10.1038/msb.2011.78 22068329
53. Kuonen F, Touvrey C, Laurent J, Ruegg C, (2010) Fc block treatment, dead cells exclusion, and cell aggregates discrimination concur to prevent phenotypical artifacts in the analysis of subpopulations of tumor-infiltrating CD11b(+) myelomonocytic cells. Cytometry A 77: 1082–1090. doi: 10.1002/cyto.a.20969 20824631
54. Brembilla NC, Weber J, Rimoldi D, Pradervand S, Schutz F, et al. (2008) c-Cbl expression levels regulate the functional responses of human central and effector memory CD4 T cells. Blood 112: 652–660. doi: 10.1182/blood-2008-01-134486 18505781
55. Auerbach R, Lewis R, Shinners B, Kubai L, Akhtar N, (2003) Angiogenesis assays: a critical overview. Clin Chem 49: 32–40. 12507958
56. Korff T, Augustin HG, (1999) Tensional forces in fibrillar extracellular matrices control directional capillary sprouting. J Cell Sci 112 (Pt 19): 3249–3258. 10504330
57. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80. 15461798
58. Smyth GK, (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3: Article3. 16646824
59. Benjamini Y, Hochberg Y, (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) Vol. 57: 289–300.
60. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545–15550. 16199517