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121. Sanchez-Paulete, A.R.; Labiano, S.; Rodriguez-Ruiz, M.E.; Azpilikueta, A.; Etxeberria, I.; Bolaños, E.;
Lang, V.; Rodriguez, M.; Aznar, M.A.; Jure-Kunkel, M.; et al. Deciphering CD137 (4-1BB) signaling in
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122. Santiago, D.N.; Heidbuechel, J.P.W.; Kandell, W.M.; Walker, R.; Djeu, J.; Engeland, C.E.; Abate-Daga, D.;
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123. Walker, R.; Navas, P.E.; Friedman, S.H.; Galliani, S.; Karolak, A.; MacFarlane, F.; Noble, R.; Poleszczuk, J.;
Russell, S.; Rejniak, K.A.; et al. Enhancing synergy of CAR T cell therapy and oncolytic virus therapy for
pancreatic cancer. bioRxiv 2016, 055988. [CrossRef]
124. Rohrs, J.A.; Zheng, D.; Graham, N.A.; Wang, P.; Finley, S.D. Computational model of chimeric antigen
receptors explains site-specific phosphorylation kinetics. Biophys. J. 2018, 115, 1116–1129. [CrossRef]
125. Schumacher, T.N.; Schreiber, R.D. Neoantigens in cancer immunotherapy. Science 2015, 348, 69–74. [CrossRef]
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126. Sharma, P.; Allison, J.P. The future of immune checkpoint therapy. Science 2015, 348, 56–61. [CrossRef]
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127. Gong, C.; Milberg, O.; Wang, B.; Vicini, P.; Narwal, R.; Roskos, L.; Popel, A.S. A computational multiscale
agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.
J. R. Soc. Interface 2017, 14, 20170320. [CrossRef][PubMed]
128. Hillen, T.; Enderling, H.; Hahnfeldt, P. The tumor growth paradox and immune system-mediated selection
for cancer stem cells. Bull. Math. Biol. 2013, 75, 161–184. [CrossRef][PubMed]
129. Enderling, H.; Hlatky, L.; Hahnfeldt, P. Immunoediting: Evidence of the multifaceted role of the immune
system in self-metastatic tumor growth. Theor. Biol. Med. Model. 2012, 9, 31. [CrossRef][PubMed]
130. Dehne, N.; Mora, J.; Namgaladze, D.; Weigert, A.; Brüne, B. Cancer cell and macrophage cross-talk in the
tumor microenvironment. Curr. Opin. Pharmacol. 2017, 35, 12–19. [CrossRef][PubMed]
131. Wells, D.K.; Chuang, Y.; Knapp, L.M.; Brockmann, D.; Kath, W.L.; Leonard, J.N. Spatial and functional
heterogeneities shape collective behavior of tumor-immune networks. PLoS Comput. Biol. 2015, 11, e1004181.
[CrossRef]
132. Knútsdóttir, H.; Pálsson, E.; Edelstein-Keshet, L. Mathematical model of macrophage-facilitated breast cancer
cells invasion. J. Theor. Biol. 2014, 357, 184–199. [CrossRef]
133. Knutsdottir, H.; Condeelis, J.S.; Palsson, E. 3-D individual cell based computational modeling of tumor
cell-macrophage paracrine signaling mediated by EGF and CSF-1 gradients. Integr. Biol. 2016, 8, 104–119.
[CrossRef]
134. Norton, K.A.; Jin, K.; Popel, A.S. Modeling triple-negative breast cancer heterogeneity: Effects of stromal
macrophages, fibroblasts and tumor vasculature. J. Theor. Biol. 2018, 452, 56–68. [CrossRef]
135. Marusyk, A.; Almendro, V.; Polyak, K. Intra-tumour heterogeneity: A looking glass for cancer? Nat. Rev. Cancer
2012, 12, 323–334. [CrossRef][PubMed]
136. Lehrach, H. Virtual clinical trials, an essential step in increasing the effectiveness of the drug development
process. Public Health Genom. 2015, 18, 366–371. [CrossRef][PubMed]
137. Pourhasanzade, F.; Sabzpoushan, S.; Alizadeh, A.M.; Esmati, E. An agent-based model of avascular tumor
growth: Immune response tendency to prevent cancer development. Simulation 2017, 93, 641–657. [CrossRef]
138. Carmona-Fontaine, C.; Bucci, V.; Akkari, L.; Deforet, M.; Joyce, J.A.; Xavier, J.B. Emergence of spatial structure
in the tumor microenvironment due to the Warburg effect. Proc. Natl. Acad. Sci. USA 2013, 110, 19402–19407.
[CrossRef][PubMed]
139. Figueredo, G.P.; Aickelin, U.; Siebers, P.O. Systems dynamics or agent-based modelling for immune
simulation? In Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2011; Volume
6825, pp. 81–94.
140. Figueredo, G.P.; Siebers, P.-O.; Aickelin, U. Investigating mathematical models of immuno-interactions with
early-stage cancer under an agent-based modelling perspective. BMC Bioinform. 2013, 14, S6. [CrossRef]
141. Figueredo, G.P.; Joshi, T.V.; Osborne, J.M.; Byrne, H.M.; Owen, M.R. On-lattice agent-based simulation of
populations of cells within the open-source Chaste framework. Interface Focus 2013, 3, 20120081. [CrossRef]
[PubMed]
61
121. Sanchez-Paulete, A.R.; Labiano, S.; Rodriguez-Ruiz, M.E.; Azpilikueta, A.; Etxeberria, I.; Bolaños, E.;
Lang, V.; Rodriguez, M.; Aznar, M.A.; Jure-Kunkel, M.; et al. Deciphering CD137 (4-1BB) signaling in
T-cell costimulation for translation into successful cancer immunotherapy. Eur. J. Immunol. 2016, 46, 513–522.
[CrossRef][PubMed]
122. Santiago, D.N.; Heidbuechel, J.P.W.; Kandell, W.M.; Walker, R.; Djeu, J.; Engeland, C.E.; Abate-Daga, D.;
Enderling, H. Fighting cancer with mathematics and viruses. Viruses 2017, 9, 239. [CrossRef]
123. Walker, R.; Navas, P.E.; Friedman, S.H.; Galliani, S.; Karolak, A.; MacFarlane, F.; Noble, R.; Poleszczuk, J.;
Russell, S.; Rejniak, K.A.; et al. Enhancing synergy of CAR T cell therapy and oncolytic virus therapy for
pancreatic cancer. bioRxiv 2016, 055988. [CrossRef]
124. Rohrs, J.A.; Zheng, D.; Graham, N.A.; Wang, P.; Finley, S.D. Computational model of chimeric antigen
receptors explains site-specific phosphorylation kinetics. Biophys. J. 2018, 115, 1116–1129. [CrossRef]
125. Schumacher, T.N.; Schreiber, R.D. Neoantigens in cancer immunotherapy. Science 2015, 348, 69–74. [CrossRef]
[PubMed]
126. Sharma, P.; Allison, J.P. The future of immune checkpoint therapy. Science 2015, 348, 56–61. [CrossRef]
[PubMed]
127. Gong, C.; Milberg, O.; Wang, B.; Vicini, P.; Narwal, R.; Roskos, L.; Popel, A.S. A computational multiscale
agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.
J. R. Soc. Interface 2017, 14, 20170320. [CrossRef][PubMed]
128. Hillen, T.; Enderling, H.; Hahnfeldt, P. The tumor growth paradox and immune system-mediated selection
for cancer stem cells. Bull. Math. Biol. 2013, 75, 161–184. [CrossRef][PubMed]
129. Enderling, H.; Hlatky, L.; Hahnfeldt, P. Immunoediting: Evidence of the multifaceted role of the immune
system in self-metastatic tumor growth. Theor. Biol. Med. Model. 2012, 9, 31. [CrossRef][PubMed]
130. Dehne, N.; Mora, J.; Namgaladze, D.; Weigert, A.; Brüne, B. Cancer cell and macrophage cross-talk in the
tumor microenvironment. Curr. Opin. Pharmacol. 2017, 35, 12–19. [CrossRef][PubMed]
131. Wells, D.K.; Chuang, Y.; Knapp, L.M.; Brockmann, D.; Kath, W.L.; Leonard, J.N. Spatial and functional
heterogeneities shape collective behavior of tumor-immune networks. PLoS Comput. Biol. 2015, 11, e1004181.
[CrossRef]
132. Knútsdóttir, H.; Pálsson, E.; Edelstein-Keshet, L. Mathematical model of macrophage-facilitated breast cancer
cells invasion. J. Theor. Biol. 2014, 357, 184–199. [CrossRef]
133. Knutsdottir, H.; Condeelis, J.S.; Palsson, E. 3-D individual cell based computational modeling of tumor
cell-macrophage paracrine signaling mediated by EGF and CSF-1 gradients. Integr. Biol. 2016, 8, 104–119.
[CrossRef]
134. Norton, K.A.; Jin, K.; Popel, A.S. Modeling triple-negative breast cancer heterogeneity: Effects of stromal
macrophages, fibroblasts and tumor vasculature. J. Theor. Biol. 2018, 452, 56–68. [CrossRef]
135. Marusyk, A.; Almendro, V.; Polyak, K. Intra-tumour heterogeneity: A looking glass for cancer? Nat. Rev. Cancer
2012, 12, 323–334. [CrossRef][PubMed]
136. Lehrach, H. Virtual clinical trials, an essential step in increasing the effectiveness of the drug development
process. Public Health Genom. 2015, 18, 366–371. [CrossRef][PubMed]
137. Pourhasanzade, F.; Sabzpoushan, S.; Alizadeh, A.M.; Esmati, E. An agent-based model of avascular tumor
growth: Immune response tendency to prevent cancer development. Simulation 2017, 93, 641–657. [CrossRef]
138. Carmona-Fontaine, C.; Bucci, V.; Akkari, L.; Deforet, M.; Joyce, J.A.; Xavier, J.B. Emergence of spatial structure
in the tumor microenvironment due to the Warburg effect. Proc. Natl. Acad. Sci. USA 2013, 110, 19402–19407.
[CrossRef][PubMed]
139. Figueredo, G.P.; Aickelin, U.; Siebers, P.O. Systems dynamics or agent-based modelling for immune
simulation? In Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2011; Volume
6825, pp. 81–94.
140. Figueredo, G.P.; Siebers, P.-O.; Aickelin, U. Investigating mathematical models of immuno-interactions with
early-stage cancer under an agent-based modelling perspective. BMC Bioinform. 2013, 14, S6. [CrossRef]
141. Figueredo, G.P.; Joshi, T.V.; Osborne, J.M.; Byrne, H.M.; Owen, M.R. On-lattice agent-based simulation of
populations of cells within the open-source Chaste framework. Interface Focus 2013, 3, 20120081. [CrossRef]
[PubMed]
61