Personalized cancer immunotherapy using Systems Medicine approaches

Briefings in Bioinformatics

Volumn 17, Issue 3, 2016, Pages 453-467

  Gupta, Shailendra K ^a,b   Jaitly, Tanushree ^c   Schmitz, Ulf ^a   Schuler, Gerold ^c   Wolkenhauer, Olaf ^a   Vera, Julio ^d  

^a UNIVERSITY OF ROSTOCK   (Germany)

^b CSIR INDIAN INSTITUTE OF TOXICOLOGY RESEARCH   (India)

^c UNIVERSITY HOSPITAL ERLANGEN   (Germany)

^d UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

Cancer immunotherapy; Melanoma; Multi epitope vaccine; Personalized vaccine; Systems biology

Indexed keywords

ALGORITHM; BIOLOGY; HUMAN; IMMUNOLOGY; IMMUNOTHERAPY; NEOPLASM; SYSTEM ANALYSIS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; HUMANS; IMMUNOTHERAPY; NEOPLASMS; SYSTEMS ANALYSIS;

EID: 84971634569     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbv046     Document Type: Article

References (157)

1. Becker JC, Andersen MH, Hofmeister-Müller V, et al. Survivin-specific T-cell reactivity correlates with tumor response and patient survival: a phase-II peptide vaccination trial in metastatic melanoma. Cancer Immunol Immunother 2012;61(11):2091-103.
2. Butts C, Socinski MA, Mitchell PL, et al. Tecemotide (L-BLP25) versus placebo after chemoradiotherapy for stage III nonsmall-cell lung cancer (START): a randomised, double-blind, phase 3 trial. Lancet Oncol 2014;15(1):59-68.
3. Cheever MA, Higano CS. PROVENGE (Sipuleucel-T) in prostate cancer: the first FDA-approved therapeutic cancer vaccine. Clin Cancer Res 2011;17(11):3520-6.
4. Kantoff PW, Higano CS, Shore ND, et al. Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med 2010;363(5):411-22.
5. Schwartzentruber DJ, Lawson DH, Richards JM, et al. gp100 peptide vaccine and interleukin-2 in patients with advanced melanoma. N Engl J Med 2011;364(22):2119-27.
6. Palucka K, Banchereau J, Mellman I. Designing vaccines based on biology of human dendritic cell subsets. Immunity 2010;33(4):464-78.
7. Muñoz N, Kjaer SK, Sigurdsson K, et al. Impact of human papillomavirus (HPV)-6/11/16/18 vaccine on all HPV-associated genital diseases in young women. J Natl Cancer Institute 2010;102:325-39.
8. Lehtinen M, Paavonen J, Wheeler CM, et al. Overall efficacy of HPV-16/18 AS04-adjuvanted vaccine against grade 3 or greater cervical intraepithelial neoplasia: 4-year end-ofstudy analysis of the randomised, double-blind PATRICIA trial. Lancet Oncol 2012;13(1):89-99.
9. Cohen JI. Epstein-barr virus vaccines. Clin Transl Immunol 2015;4(1):e32.
10. Hammitt LL, Hennessy TW, Fiore AE, et al. Hepatitis B immunity in children vaccinated with recombinant hepatitis B vaccine beginning at birth: a follow-up study at 15 years. Vaccine 2007;25(39):6958-64.
11. Suehiro Y, Hasegawa A, Iino T, et al. Clinical outcomes of a novel therapeutic vaccine with Tax peptide-pulsed dendritic cells for adult T cell leukaemia/lymphoma in a pilot study. Br J Haematol 2015;169:356-67.
12. Goel G, Sun W. Cancer immunotherapy in clinical practice? the past, present, and future. Chin J Cancer 2014;33:445-57.
13. Srivastava PK, Duan F. Harnessing the antigenic fingerprint of each individual cancer for immunotherapy of human cancer: genomics shows a new way and its challenges. Cancer Immunol Immunother 2013;62(5):967-74.
14. Trajanoski Z, Maccalli C, Mennonna D, et al. Somatically mutated tumor antigens in the quest for a more efficacious patient-oriented immunotherapy of cancer. Cancer Immunol Immunother 2015;64(1):99-104.
15. Charoentong P, Angelova M, Efremova M, et al. Bioinformatics for cancer immunology and immunotherapy. Cancer Immunol Immunother 2012;61(11):1885-903.
16. Olsen LR, Campos B, Barnkob MS, et al. Bioinformatics for cancer immunotherapy target discovery. Cancer Immunol Immunother 2014;63(12):1235-49.
17. Duan F, Duitama J, Al Seesi S, et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J Exp Med 2014;211(11):2231-48.
18. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science 2015;348(6230):69-74.
19. Osada T, Nagaoka K, Takahara M, et al. Precision Cancer Immunotherapy: Optimizing Dendritic Cell-based Strategies to Induce Tumor Antigen-specific T-cell Responses Against Individual Patient Tumors. J Immunother 2015;38(4):155-64.
20. Madden DR. The three-dimensional structure of peptide-MHC complexes. Annu Rev Immunol 1995;13(1):587-622.
21. Reilly RT, Machiels JPH, Emens LA, et al. The collaboration of both humoral and cellular HER-2/neu-targeted immune responses is required for the complete eradication of HER-2/neu-expressing tumors. Cancer Res 2001;61(3):880-3.
22. Gadaleta E, Cutts RJ, Kelly GP, et al. A global insight into a cancer transcriptional space using pancreatic data: importance, findings and flaws. Nucleic Acids Res. 2011:39:7900-7.
23. Robinson J, Halliwell JA, McWilliam H, et al. IPD-the immuno polymorphism database. Nucleic Acids Res 2012;33:D523-6.
24. Robinson J, Halliwell JA, Hayhurst JD, et al. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res 2014:gku1161.
25. Lefranc MP, Giudicelli V, Ginestoux C, et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res 2009;37(suppl 1):D1006-12.
26. Vigneron N, Stroobant V, Van den Eynde BJ, et al. Database of T cell-defined human tumor antigens: the 2013 update. Cancer Immun 2013;13:15.
27. Almeida LG, Sakabe NJ, Silva MCC, et al. CTdatabase: a knowledge-base of high-throughput and curated data on cancertestis antigens. Nucleic Acids Res 2009;37(Suppl 1):D816-19.
28. Vita R, Zarebski L, Greenbaum JA, et al. The immune epitope database 2.0. Nucleic Acids Res 2010;38(Suppl 1):D854-62.
29. Zhang J, Baran J, Cros A, et al. International Cancer Genome Consortium Data Portal-a one-stop shop for cancer genomics data. Database 2011;2011:bar026.
30. Forbes SA, Beare D, Gunasekaran P, et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res 2015;43(D1):D805-11.
31. Reinhold WC, Sunshine M, Liu H, et al. CellMiner: a webbased suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. Cancer Res 2012;72(14):3499-511.
32. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6(269):pl1.
33. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, et al. Oncomine 3.0: genes, pathways, and networks in a collection of 18, 000 cancer gene expression profiles. Neoplasia 2007;9(2):166-80.
34. Brändle D, Brasseur F, Weynants P, Boon T, et al. A mutated HLA-A2 molecule recognized by autologous cytotoxic T lymphocytes on a human renal cell carcinoma. J Exp Med 1996;183(6):2501-2508.
35. Huang J, El-Gamil M, Dudley ME, et al. T cells associated with tumor regression recognize frameshifted products of the CDKN2A tumor suppressor gene locus and a mutated HLA class I gene product. J Immunol 2004;172(10):6057-64.
36. Van Der Bruggen P, Zhang Y, Chaux P, et al. Tumor-specific shared antigenic peptides recognized by human T cells. Immunol Rev 2002;188(1):51-64.
37. Simpson AJ, Caballero OL, Jungbluth A, et al. Cancer/testis antigens, gametogenesis and cancer. Nat Rev Cancer 2005;5(8):615-25.
38. Steele J, Rao A, Marsden J, et al. Phase I/II trial of a dendritic cell vaccine transfected with DNA encoding melan A and gp100 for patients with metastatic melanoma. Gene Ther 2011;18(6):584-93.
39. Syed ON, Mandigo CE, Killory BD, et al. Cancer-testis and melanocyte-differentiation antigen expression in malignant glioma and meningioma. J Clin Neurosci 2012;19(7):1016-21.
40. Kiessling A, Wehner R, Füssel S, et al. Tumor-associated antigens for specific immunotherapy of prostate cancer. Cancers 2012;4(1):193-217.
41. Starck SR, Shastri N. Non-conventional sources of peptides presented by MHC class I. Cell Mol Life Sci 2011;68(9):1471-9.
42. Fritsch EF, Rajasagi M, Ott PA, et al. HLA-binding properties of tumor neoepitopes in humans. Cancer Immunol Res 2014;2(6):522-9.
43. Yang X, Yu X. An introduction to epitope prediction methods and software. Rev Med Virol 2009;19(2):77-96.
44. Zhao C, Zhang H, Luan F, et al. QSAR method for prediction of protein-peptide binding affinity: application to MHC class I molecule HLA-A* 0201. J Mol Graph Model 2007;26(1):246-54.
45. Tong JC, Tan TW, Ranganathan S. Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform 2007;8(2):96-108.
46. Reitmaier R. Review of immunoinformatic approaches to insilico B-cell epitope prediction. Nat Preced 2007;1:353
47. Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol 2013;3(1):120139.
48. Lee D, Redfern O, Orengo C. Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 2007;8(12):995-1005.
49. Hopp TP, Woods KR. Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci USA 1981;78(6):3824-8.
50. Novotný J, Handschumacher M, Haber E, et al. Antigenic determinants in proteins coincide with surface regions accessible to large probes (antibody domains). Proc Natl Acad Sci USA 1986;83(2):226-30.
51. Barlow D, Edwards M, Thornton J. Continuous and discontinuous protein antigenic determinants. Nature 1986;322:747-8.
52. Jameson B, Wolf H. The antigenic index: a novel algorithm for predicting antigenic determinants. Comput Appl Biosci 1988;4(1):181-6.
53. Pellequer JL, Westhof E, Van Regenmortel MH. Correlation between the location of antigenic sites and the prediction of turns in proteins. Immunol Lett 1993;36(1):83-99.
54. Alix AJ. Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine 1999;18(3):311-14.
55. Karplus PA, Schulz GE. Prediction of chain flexibility in proteins-a tool for the selection of peptide antigens. Naturwissen 1985;72:212-13.
56. Altuvia Y, Berzofsky JA, Rosenfeld R, et al. Sequence features that correlate with MHC restriction. Mol Immunol 1994;31(1):1-19.
57. Andersen MH, Tan L, Søndergaard I, et al. Poor correspondence between predicted and experimental binding of peptides to class I MHC molecules. Tissue Antigens 2000;55(6):519-31.
58. Nussbaum AK, Kuttler C, Tenzer S, et al. Using the World Wide Web for predicting CTL epitopes. Curr Opin Immunol 2003;15(1):69-74.
59. Gowthaman U, Agrewala JN. In silico methods for predicting T-cell epitopes: Dr Jekyll or Mr Hyde? Expert Rev Proteomics 2009;6(5):527-37.
60. Ruppert J, Sidney J, Celis E, et al. Prominent role of secondary anchor residues in peptide binding to HLA-A2. 1 molecules. Cell 1993;74(5):929-37.
61. Marshall KW, Liu AF, Canales J, et al. Role of the polymorphic residues in HLA-DR molecules in allele-specific binding of peptide ligands. J Immunol 1994;152(10):4946-57.
62. Hammer J, Bono E, Gallazzi F, et al. Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning. J Exp Med 1994;180(6):2353-8.
63. Parker KC, Bednarek MA, Coligan JE. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol 1994;152(1):163-75.
64. Martin W, Sbai H, De Groot AS. Bioinformatics tools for identifying class I-restricted epitopes. Methods 2003;29(3):289-98.
65. Koszinowski HS, Rammensee HG. The making of the dominant MHC class I ligand SYFPEITHI. Eur J Immunol 1998;28:2478-86.
66. Reche PA, Glutting JP, Zhang H, et al. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 2004;56(6):405-19.
67. Doytchinova IA, Guan P, Flower DR. EpiJen: a server for multistep T cell epitope prediction. BMC Bioinformatics 2006;7(1):131.
68. Sturniolo T, Bono E, Ding J, et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 1999;17(6):555-61.
69. Singh H, Raghava G. ProPred: prediction of HLA-DR binding sites. Bioinformatics 2001;17(12):1236-7.
70. Cochlovius B, Stassar M, Christ O, et al. In vitro and in vivo induction of a Th cell response toward peptides of the melanoma-associated glycoprotein 100 protein selected by the TEPITOPE program. J Immunol 2000;165(8):4731-41.
71. Yoon BJ. Hidden Markov models and their applications in biological sequence analysis. Curr Genom 2009;10(6):402.
72. Mamitsuka H. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins Struct Func Genet 1998;33(4):460-74.
73. Brusic V, Petrovsky N, Zhang G, et al. Prediction of promiscuous peptides that bind HLA class I molecules. Immunol Cell Biol 2002;80(3):280-5.
74. Udaka K, Mamitsuka H, Nakaseko Y, et al. Prediction of MHC class I binding peptides by a query learning algorithm based on hidden Markov models. J Biol Phys 2002;28(2):183-94.
75. Yu K, Petrovsky N, Schönbach C, et al. Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol Med 2002;8(3):137.
76. Brusic V, Rudy G, Harrison LC. Prediction of MHC binding peptides using artificial neural networks. Complex Syst Mech Adapt 1994:253-60.
77. Brusic V, Rudy G, Honeyman G, et al. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 1998;14(2):121-30.
78. Nielsen M, Lundegaard C, Worning P, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 2003;12(5):1007-17.
79. Lundegaard C, Lamberth K, Harndahl M, et al. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res 2008;36(suppl 2):W509-12.
80. Zhao Y, Pinilla C, Valmori D, et al. Application of support vector machines for T-cell epitopes prediction. Bioinformatics 2003;19(15):1978-84.
81. Bhasin M, Raghava G. SVM based method for predicting HLA-DRB1* 0401 binding peptides in an antigen sequence. Bioinformatics 2004;20(3):421-3.
82. Bozic I, Zhang GL, Brusic V. Predictive vaccinology: optimisation of predictions using support vector machine classifiers. In: M Gallagher, JP, Horgan, F Maire (eds). Intelligent Data Engineering and Automated Learning-IDEAL 2005. Berlin Heidelberg: Springer; 2005, 375-81.
83. Chen J, Liu H, Yang J, et al. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 2007;33(3):423-8.
84. EL-Manzalawy Y, Dobbs D, Honavar V. Predicting linear Bcell epitopes using string kernels. J Mol Recogn 2008;21(4):243-55.
85. Saha S, Raghava G. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res 2006;34(suppl 2):W202-W209.
86. Sweredoski MJ, Baldi P. COBEpro: a novel system for predicting continuous B-cell epitopes. Protein Eng Des Sel 2009;22(3):113-20.
87. Tong JC, Tan TW, Ranganathan S. Modeling the structure of bound peptide ligands to major histocompatibility complex. Protein Sci 2004;13(9):2523-32.
88. Fleckenstein B, Kalbacher H, Muller CP, et al. New ligands binding to the human leukocyte antigen class II molecule DRB1* 0101 based on the activity pattern of an undecapeptide library. Eur J Biochem 1996;240(1):71-7.
89. Stryhn A, Pedersen L, Romme T, et al. Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding. Eur J Immunol 1996;26(8):1911-18.
90. Stevens J, Wiesmüller KH, Barker PJ, et al. Efficient generation of major histocompatibility complex class I-peptide complexes using synthetic peptide libraries. J Biol Chem 1998;273(5):2874-84.
91. Sinigaglia M, Antunes DA, Rigo MM, et al. CrossTope: a curate repository of 3D structures of immunogenic peptide: MHC complexes. Database 2013;2013:bat002.
92. Atanasova M, Patronov A, Dimitrov I, et al. EpiDOCK: a molecular docking-based tool for MHC class II binding prediction. Protein Eng Des Sel. 2013;26:631-4.
93. Haste Andersen P, Nielsen M, Lund O. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci 2006;15(11):2558-67.
94. Doytchinova IA, Flower DR. QSAR and the prediction of Tcell epitopes. Curr Proteomics. 2008;5(2):73-95.
95. Tian F, Lv F, Zhou P, et al. Toward prediction of binding affinities between the MHC protein and its peptide ligands using quantitative structure-affinity relationship approach. Protein Pept Lett 2008;15(10):1033-43.
96. Wang Y, Zhou P, Lin Y, et al. Quantitative prediction of class I MHC/Epitope binding affinity using QSAR modeling derived from amino acid structural information. Comb Chem High Throughput Screen 2015;18(1):75-82.
97. Lapinsh M, Prusis P, Gutcaits A, et al. Development of proteochemometrics: a novel technology for the analysis of drug-receptor interactions. Biochim Biophys Acta 2001; 1525(1-2):180-90.
98. Dimitrov I, Garnev P, Flower DR, et al. Peptide binding to the HLA-DRB1 supertype: a proteochemometrics analysis. Eur J Med Chem 2010;45(1):236-43.
99. Bui HH, Schiewe AJ, von Grafenstein H, et al. Structural prediction of peptides binding to MHC class I molecules. Proteins 2006;63(1):43-52.
100. Schiewe AJ, Haworth IS. Structure-based prediction of MHC-peptide association: Algorithm comparison and application to cancer vaccine design. J Mol Graph Model. 2007;26(3):667-75.
101. Ponomarenko J, Bui HH, Li W, et al. ElliPro: a new structurebased tool for the prediction of antibody epitopes. BMC Bioinformatics 2008;9(1):514.
102. Laing P, Tighe P, Kwiatkowski E, et al. Selection of peptide ligands for the antimucin core antibody C595 using phage display technology: definition of candidate epitopes for a cancer vaccine. Clin Mol Pathol 1995;48(3):M136.
103. Zhu ZY, Zhong CP, Xu WF, et al. PSMA mimotope isolated from phage displayed peptide library can induce PSMA specific immune response. Cell Res 1999;9(4):271-80.
104. Riemer AB, Hantusch B, Sponer B, et al. High-molecularweight melanoma-associated antigen mimotope immunizations induce antibodies recognizing melanoma cells. Cancer Immunol Immunother 2005;54(7):677-84.
105. Luo W, Ko E, Hsu JCF, et al. Targeting melanoma cells with human high molecular weight-melanoma associated antigen-specific antibodies elicited by a peptide mimotope: functional effects. J Immunol 2006;176(10):6046-54.
106. Akhoon BA, Gupta SK, Verma V, et al. In silico designing and optimization of anti-breast cancer antibody mimetic oligopeptide targeting HER-2 in women. J Mol Graph Model 2010;28(7):664-9.
107. Li W, Ran Y, Li M, et al. Mimotope vaccination for epitopespecific induction of anti-VEGF antibodies. BMC Biotechnol 2013;13(1):77.
108. Buhrman JD, Slansky JE. Improving T cell responses to modified peptides in tumor vaccines. Immunol Res 2013;55(1-3):34-47.
109. Huang J, Gutteridge A, Honda W, et al. MIMOX: a web tool for phage display based epitope mapping. BMC Bioinformatics 2006;7(1):451.
110. Huang J, Ru B, Zhu P, et al. MimoDB 2.0: a mimotope database and beyond. Nucleic Acids Res 2012;40(D1):D271-7.
111. Sun P, Ju H, Zhang B, et al. Conformational B-cell epitope prediction method based on antigen preprocessing and mimotopes analysis. Biomed Res Int 2015;2015:257030.
112. Akhoon BA, Singh KP, Varshney M, et al. Understanding the Mechanism of Atovaquone Drug Resistance in Plasmodium falciparum Cytochrome b Mutation Y268S Using Computational Methods. PloS One 2014;9(10):e110041.
113. Bhasin M, Raghava G. A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J Biosci 2007;32(1):31-42.
114. Söllner J. Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins. J Mol Recogn 2006;19(3):209-14.
115. Wang P, Sidney J, Dow C, et al. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol 2008;4(4):e1000048.
116. Moutaftsi M, Peters B, Pasquetto V, et al. A consensus epitope prediction approach identifies the breadth of murine TCD8+-cell responses to vaccinia virus. Nat Biotechnol 2006;24(7):817-19.
117. Yasser E, Honavar V. Recent advances in B-cell epitope prediction methods. Immunome Res 2010;6(Suppl 2):S2.
118. Bui HH, Sidney J, Dinh K, et al. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics 2006;7(1):153.
119. Gupta SK, Smita S, Sarangi AN, et al. In silico CD4+ T-cell epitope prediction and HLA distribution analysis for the potential proteins of Neisseria meningitidis Serogroup B-A clue for vaccine development. Vaccine 2010;28(43):7092-7.
120. Gupta SK, Srivastava M, Akhoon BA, et al. In silico accelerated identification of structurally conserved CD8+ and CD4+ Tcell epitopes in high-risk HPV types. Infect Genet Evol 2012;12(7):1513-18.
121. Singh SB, Biswas D, Rawat J, et al. Ethnicity-tailored novel set of ESAT-6 peptides for differentiating active and latent tuberculosis. Tuberculosis 2013;93(6):618-24.
122. Zanetti M. Tapping CD4 T cells for cancer immunotherapy: the choice of personalized genomics. J Immunol 2015;194(5):2049-56.
123. Vera J, Gupta SK, Wolkenhauer O, et al. Envisioning the Application of Systems Biology in Cancer Immunology. In: N Rezaei (ed). Cancer Immunology. Berlin Heidelberg: Springer; 2015, 429-49.
124. Burkholder B, Huang RY, Burgess R, et al. Tumor-induced perturbations of cytokines and immune cell networks. Biochem Biophys Acta 2014;1845(2):182-201.
125. Kulbe H, Chakravarty P, Leinster DA, et al. A dynamic inflammatory cytokine network in the human ovarian cancer microenvironment. Cancer Res 2012;72(1):66-75.
126. Kuznetsov VA, Makalkin IA, Taylor MA, et al. Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. Bull Math Biol 1994;56(2):295-321.
127. Owen MR, Sherratt JA. Pattern formation and spatiotemporal irregularity in a model for macrophage-tumour interactions. J Theor Biol 1997;189(1):63-80.
128. d'Onofrio A. A general framework for modeling tumorimmune system competition and immunotherapy: Mathematical analysis and biomedical inferences. Phys D 2005;208(3):220-35.
129. Kim PS, Lee PP. Modeling protective anti-tumor immunity via preventative cancer vaccines using a hybrid agent-based and delay differential equation approach. PLoS Comput Biol 2012;8(10):e1002742.
130. de Pillis LG, Gu W, Radunskaya AE. Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations. J Theor Biol 2006;238(4):841-62.
131. Wilson S, Levy D. A mathematical model of the enhancement of tumor vaccine efficacy by immunotherapy. Bull Math Biol 2012;74(7):1485-500.
132. Milanesi L, Romano P, Castellani G, et al. Trends in modeling biomedical complex systems. BMC Bioinformatics 2009;10(Suppl 12):I1.
133. Celli S, Day M, Müller AJ, Molina-Paris C, et al. How many dendritic cells are required to initiate a T-cell response? Blood 2012;120(19):3945-8.
134. Nikolov S, Lai X, Liebal UW, et al. Integration of sensitivity and bifurcation analysis to detect critical processes in a model combining signalling and cell population dynamics. Int J Syst Sci 2010;41(1):81-105.
135. Schoeberl B, Pace EA, Fitzgerald JB, et al. Therapeutically targeting ErbB3: A key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal 2009;2(77):ra31.
136. Fitzgerald JB, Johnson BW, Baum J, et al. MM-141, an IGF-IR-and ErbB3-Directed Bispecific Antibody, Overcomes Network Adaptations That Limit Activity of IGF-IR Inhibitors. Mol Cancer Ther 2014;13(2):410-25.
137. Rojo F, Dalmases A, Corominas J, et al. Pharmacodynamics: biological activity of targeted therapies in clinical trials. Clin Transl Oncol 2007;9(10):634-44.
138. Scott AM, Wolchok JD, Old LJ. Antibody therapy of cancer. Nat Rev Cancer 2012;12(4):278-87.
139. Castiglione F, Piccoli B. Cancer immunotherapy, mathematical modeling and optimal control. J Theor Biol 2007;247(4):723-32.
140. Pennisi M, Pappalardo F, Palladini A, et al. Modeling the competition between lung metastases and the immune system using agents. BMC Bioinformatics 2010;11(Suppl 7):S13.
141. Pennisi M, Pappalardo F, Zhang P, et al. Searching of optimal vaccination schedules. IEEE Eng Med Biol Mag 2009;28(4):67-72.
142. Vera J, González-Alcónb C, Marín-Sanguino A, et al. Optimization of biochemical systems through mathematical programming: Methods and applications. Comput Oper Res 2010;37(8):1427-38.
143. Palladini A, Nicoletti G, Pappalardo F, et al. In silico modeling and in vivo efficacy of cancer-preventive vaccinations. Cancer Res 2010;70(20):7755-63.
144. Castle JC, Kreiter S, Diekmann J, et al. Exploiting the mutanome for tumor vaccination. Cancer Res 2012;72(5):1081-91.
145. Ulloa-Montoya F, Louahed J, Dizier B, et al. Predictive gene signature in MAGE-A3 antigen-specific cancer immunotherapy. J Clin Oncol 2013;31(19):2388-95.
146. Kronik N, Kogan Y, Elishmereni M, et al. Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. PLoS One 2010;5(12):e15482.
147. Kronik N, Kogan Y, Schlegel PG, et al. Improving T-cell immunotherapy for melanoma through a mathematically motivated strategy: efficacy in numbers? J Immunother 2012;35(2):116-24.
148. Kogan Y, Halevi-Tobias K, Elishmereni M, et al. Reconsidering the paradigm of cancer immunotherapy by computationally aided real-time personalization. Cancer Res 2012;72(9):2218-27.
149. Radunskaya A, de Pillis L, Gallegos A. A model of dendritic cell therapy for melanoma. Front Oncol 2013;3:56.
150. Chan C, Stark J, George AJ. Feedback control of T-cell receptor activation. Proc R Soc Lond B 2004;271(1542):931-40.
151. Beyer T, Busse M, Hristov K, et al. Integrating signals from the T-cell receptor and the interleukin-2 receptor. PLoS Comput Biol 2011;7(8):e1002121.
152. François P, Voisinne G, Siggia ED, et al. Phenotypic model for early T-cell activation displaying sensitivity, specificity, and antagonism. Proc Natl Acad Sci USA 2013;110(10):E888-E897.
153. Sciammas R, Li Y, Warmflash A, et al. An incoherent regulatory network architecture that orchestrates B cell diversification in response to antigen signaling. Mol Syst Biol 2011;7(1).
154. Ochiai K, Maienschein-Cline M, Simonetti G, et al. Transcriptional regulation of germinal center B and plasma cell fates by dynamical control of IRF4. Immunity 2013;38(5):918-29.
155. Watzl C, Sternberg-Simon M, Urlaub D, et al. Understanding natural killer cell regulation by mathematical approaches. Front Immunol 2012;3:359.
156. Vera J, Schmitz U, Lai X, et al. Kinetic Modeling-Based Detection of Genetic Signatures That Provide Chemoresistance via the E2F1-p73/DNp73-miR-205 Network. Cancer Res 2013;73(12):3511-24.
157. Clancy T, Pedicini M, Castiglione F, et al. Immunological network signatures of cancer progression and survival. BMC Med Genomics 2011;4(1):28.