Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?
Abstract
:Simple Summary
Abstract
1. Introduction
2. Different Concepts of Model
2.1. Model: “An Animal or Plant to Which Another Bears a Mimetic Resemblance”
2.2. Model: To Serve or Behave as the Analogue of (A Phenomenon, System, etc.); Or a Three-Dimensional Representation esp. One Showing the Component Parts in Accurate Proportion and Relative Disposition; Or to Produce (a Figure, Likeness, etc.) by Moulding, Carving, etc., esp. in Clay, Wax, or Some Other Malleable Material
2.3. Model: A Simplified or Idealised Description or Conception of a Particular System, Situation, or Process, Often in Mathematical Terms
2.4. Model: To Devise a (Usually Mathematical) Model or Simplified Description of (a Phenomenon, System, etc.)
3. Quantitative Evaluation of the Presence of Different Models in Medline
4. Conclusions
Funding
Conflicts of Interest
References
- Laplane, L.; Duluc, D.; Bikfalvi, A.; Larmonier, N.; Pradeu, T. Beyond the Tumour Microenvironment. Int. J. Cancer 2019, 145, 2611–2618. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Balkwill, F.R.; Capasso, M.; Hagemann, T. The Tumor Microenvironment at a Glance. J. Cell Sci. 2012, 125, 5591–5596. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Anderson, N.M.; Simon, M.C. The Tumor Microenvironment. Curr. Biol. 2020, 30, R921–R925. [Google Scholar] [CrossRef] [PubMed]
- Xiao, Y.; Yu, D. Tumor Microenvironment as a Therapeutic Target in Cancer. Pharmacol. Ther. 2021, 221, 107753. [Google Scholar] [CrossRef]
- Paget, S. The Distribution of Secondary Growths in Cancer of the Breast. Lancet 1889, 133, 571–573. [Google Scholar] [CrossRef] [Green Version]
- Akhtar, M.; Haider, A.; Rashid, S.; Al-Nabet, A.D.M.H. Paget’s “Seed and Soil” Theory of Cancer Metastasis: An Idea Whose Time Has Come. Adv. Anat. Pathol. 2019, 26, 69. [Google Scholar] [CrossRef]
- Li, X.; Yao, W.; Yuan, Y.; Chen, P.; Li, B.; Li, J.; Chu, R.; Song, H.; Xie, D.; Jiang, X.; et al. Targeting of Tumour-Infiltrating Macrophages via CCL2/CCR2 Signalling as a Therapeutic Strategy against Hepatocellular Carcinoma. Gut 2017, 66, 157–167. [Google Scholar] [CrossRef]
- García-Marín, R.; Reda, S.; Riobello, C.; Cabal, V.N.; Suárez-Fernández, L.; Vivanco, B.; López, F.; Llorente, J.L.; Hermsen, M.A. CD8+ Tumour-Infiltrating Lymphocytes and Tumour Microenvironment Immune Types as Biomarkers for Immunotherapy in Sinonasal Intestinal-Type Adenocarcinoma. Vaccines 2020, 8, 202. [Google Scholar] [CrossRef]
- Versluis, M.a.C.; Marchal, S.; Plat, A.; de Bock, G.H.; van Hall, T.; de Bruyn, M.; Hollema, H.; Nijman, H.W. The Prognostic Benefit of Tumour-Infiltrating Natural Killer Cells in Endometrial Cancer Is Dependent on Concurrent Overexpression of Human Leucocyte Antigen-E in the Tumour Microenvironment. Eur. J. Cancer 2017, 86, 285–295. [Google Scholar] [CrossRef]
- Ahmed, H.; Ghoshal, A.; Jones, S.; Ellis, I.; Islam, M. Head and Neck Cancer Metastasis and the Effect of the Local Soluble Factors, from the Microenvironment, on Signalling Pathways: Is It All about the Akt? Cancers 2020, 12, 2093. [Google Scholar] [CrossRef]
- Akimoto, M.; Maruyama, R.; Takamaru, H.; Ochiya, T.; Takenaga, K. Soluble IL-33 Receptor SST2 Inhibits Colorectal Cancer Malignant Growth by Modifying the Tumour Microenvironment. Nat. Commun. 2016, 7, 13589. [Google Scholar] [CrossRef]
- Kupsa, T.; Vanek, J.; Zak, P.; Jebavy, L.; Horacek, J.M. Serum Levels of Selected Cytokines and Soluble Adhesion Molecules in Acute Myeloid Leukemia: Soluble Receptor for Interleukin-2 Predicts Overall Survival. Cytokine 2020, 128, 155005. [Google Scholar] [CrossRef]
- Walker, C.; Mojares, E.; Del Río Hernández, A. Role of Extracellular Matrix in Development and Cancer Progression. Int. J. Mol. Sci. 2018, 19, 3028. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kolesnikoff, N.; Chen, C.-H.; Samuel, M.S. Interrelationships between the Extracellular Matrix and the Immune Microenvironment That Govern Epithelial Tumour Progression. Clin. Sci. 2022, 136, 361–377. [Google Scholar] [CrossRef]
- Karlsson, S.; Nyström, H. The Extracellular Matrix in Colorectal Cancer and Its Metastatic Settling—Alterations and Biological Implications. Crit. Rev. Oncol. Hematol. 2022, 175, 103712. [Google Scholar] [CrossRef] [PubMed]
- Tee, J.K.; Yip, L.X.; Tan, E.S.; Santitewagun, S.; Prasath, A.; Ke, P.C.; Ho, H.K.; Leong, D.T. Nanoparticles’ Interactions with Vasculature in Diseases. Chem. Soc. Rev. 2019, 48, 5381–5407. [Google Scholar] [CrossRef]
- Baker, J.H.E.; Kyle, A.H.; Bartels, K.L.; Methot, S.P.; Flanagan, E.J.; Balbirnie, A.; Cran, J.D.; Minchinton, A.I. Targeting the Tumour Vasculature: Exploitation of Low Oxygenation and Sensitivity to NOS Inhibition by Treatment with a Hypoxic Cytotoxin. PLoS ONE 2013, 8, e76832. [Google Scholar] [CrossRef]
- Maman, S.; Witz, I.P. A History of Exploring Cancer in Context. Nat. Rev. Cancer 2018, 18, 359–376. [Google Scholar] [CrossRef]
- Leonelli, S.; Ankeny, R.A. What Makes a Model Organism? Endeavour 2013, 37, 209–212. [Google Scholar] [CrossRef] [Green Version]
- Blount, Z.D. The Unexhausted Potential of E. coli. eLife 2015, 4, e05826. [Google Scholar] [CrossRef]
- Nielsen, J. Yeast Systems Biology: Model Organism and Cell Factory. Biotechnol. J. 2019, 14, e1800421. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Renshaw, S.A.; Trede, N.S. A Model 450 Million Years in the Making: Zebrafish and Vertebrate Immunity. Dis. Model. Mech. 2012, 5, 38–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Paschall, A.V.; Liu, K. An Orthotopic Mouse Model of Spontaneous Breast Cancer Metastasis. J. Vis. Exp. 2016, 114, e54040. [Google Scholar] [CrossRef]
- Lodge, W.; Zavortink, M.; Golenkina, S.; Froldi, F.; Dark, C.; Cheung, S.; Parker, B.L.; Blazev, R.; Bakopoulos, D.; Christie, E.L.; et al. Tumor-Derived MMPs Regulate Cachexia in a Drosophila Cancer Model. Dev. Cell 2021, 56, 2664–2680.e6. [Google Scholar] [CrossRef]
- Vanhooren, V.; Libert, C. The Mouse as a Model Organism in Aging Research: Usefulness, Pitfalls and Possibilities. Ageing Res. Rev. 2013, 12, 8–21. [Google Scholar] [CrossRef] [PubMed]
- Trammell, C.E.; Goodman, A.G. Emerging Mechanisms of Insulin-Mediated Antiviral Immunity in Drosophila Melanogaster. Front. Immunol. 2019, 10, 2973. [Google Scholar] [CrossRef]
- Fields, S.; Johnston, M. Whither Model Organism Research? Science 2005, 307, 1885–1886. [Google Scholar] [CrossRef] [Green Version]
- Yao, G.; Bai, Z.; Niu, J.; Zhang, R.; Lu, Y.; Gao, T.; Wang, H. Astragalin Attenuates Depression-like Behaviors and Memory Deficits and Promotes M2 Microglia Polarization by Regulating IL-4R/JAK1/STAT6 Signaling Pathway in a Murine Model of Perimenopausal Depression. Psychopharmacology 2022, 239, 2421–2443. [Google Scholar] [CrossRef]
- Foss, C.A.; Ordonez, A.A.; Naik, R.; Das, D.; Hall, A.; Wu, Y.; Dannals, R.F.; Jain, S.K.; Pomper, M.G.; Horti, A.G. PET/CT Imaging of CSF1R in a Mouse Model of Tuberculosis. Eur. J. Nucl. Med. Mol. Imaging 2022, 49, 4088–4096. [Google Scholar] [CrossRef]
- Nong, Y.; Guo, Y.; Gumpert, A.; Li, Q.; Tomlin, A.; Zhu, X.; Bolli, R. Single Dose of Synthetic MicroRNA-199a or MicroRNA-149 Mimic Does Not Improve Cardiac Function in a Murine Model of Myocardial Infarction. Mol. Cell. Biochem. 2021, 476, 4093–4106. [Google Scholar] [CrossRef]
- Stiedl, P.; Grabner, B.; Zboray, K.; Bogner, E.; Casanova, E. Modeling Cancer Using Genetically Engineered Mice. Methods Mol. Biol. 2015, 1267, 3–18. [Google Scholar] [CrossRef]
- Entenberg, D.; Oktay, M.H.; Condeelis, J.S. Intravital Imaging to Study Cancer Progression and Metastasis. Nat. Rev. Cancer 2023, 23, 25–42. [Google Scholar] [CrossRef]
- Thamavit, W.; Bhamarapravati, N.; Sahaphong, S.; Vajrasthira, S.; Angsubhakorn, S. Effects of Dimethylnitrosamine on Induction of Cholagiocarcinoma in Opisthorchis Viverrini-Infected Syrian Golden Hamsters1. Cancer Res. 1978, 38, 4634–4639. [Google Scholar]
- Crallan, R.A.; Georgopoulos, N.T.; Southgate, J. Experimental Models of Human Bladder Carcinogenesis. Carcinogenesis 2006, 27, 374–381. [Google Scholar] [CrossRef] [Green Version]
- Hu, M.-B.; Hu, J.-M.; Jiang, L.-R.; Yang, T.; Zhu, W.-H.; Hu, Y.; Wu, X.-B.; Jiang, H.-W.; Ding, Q. Differential Expressions of Integrin-Linked Kinase, β-Parvin and Cofilin 1 in High-Fat Diet Induced Prostate Cancer Progression in a Transgenic Mouse Model. Oncol. Lett. 2018, 16, 4945–4952. [Google Scholar] [CrossRef] [Green Version]
- Asgharpour, A.; Cazanave, S.C.; Pacana, T.; Seneshaw, M.; Vincent, R.; Banini, B.A.; Kumar, D.P.; Daita, K.; Min, H.-K.; Mirshahi, F.; et al. A Diet-Induced Animal Model of Non-Alcoholic Fatty Liver Disease and Hepatocellular Cancer. J. Hepatol. 2016, 65, 579–588. [Google Scholar] [CrossRef] [Green Version]
- Chen, Y.; Liu, X.; Gao, L.; Liu, Y. Xenograft Mouse Model of Human Uveal Melanoma. Bio. Protoc. 2017, 7, e2594. [Google Scholar] [CrossRef] [PubMed]
- Fantozzi, A.; Christofori, G. Mouse Models of Breast Cancer Metastasis. Breast Cancer Res. 2006, 8, 212. [Google Scholar] [CrossRef] [PubMed]
- Yang, S.; Zhang, J.J.; Huang, X.-Y. Mouse Models for Tumor Metastasis. Methods Mol. Biol. 2012, 928, 221–228. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Patton, E.E.; Mueller, K.L.; Adams, D.J.; Anandasabapathy, N.; Aplin, A.E.; Bertolotto, C.; Bosenberg, M.; Ceol, C.J.; Burd, C.E.; Chi, P.; et al. Melanoma Models for the next Generation of Therapies. Cancer Cell 2021, 39, 610–631. [Google Scholar] [CrossRef]
- Piskovatska, V.; Strilbytska, O.; Koliada, A.; Vaiserman, A.; Lushchak, O. Health Benefits of Anti-Aging Drugs. Subcell. Biochem. 2019, 91, 339–392. [Google Scholar] [CrossRef]
- Loeuillard, E.; Fischbach, S.R.; Gores, G.J.; Rizvi, S. Animal Models of Cholangiocarcinoma. Biochim. Et Biophys. Acta (BBA)—Mol. Basis Dis. 2019, 1865, 982–992. [Google Scholar] [CrossRef] [PubMed]
- Seok, J.; Warren, H.S.; Cuenca, A.G.; Mindrinos, M.N.; Baker, H.V.; Xu, W.; Richards, D.R.; McDonald-Smith, G.P.; Gao, H.; Hennessy, L.; et al. Genomic Responses in Mouse Models Poorly Mimic Human Inflammatory Diseases. Proc. Natl. Acad. Sci. USA 2013, 110, 3507–3512. [Google Scholar] [CrossRef] [PubMed]
- Worp, v.d.H.B.; Howells, D.W.; Sena, E.S.; Porritt, M.J.; Rewell, S.; O’Collins, V.; Macleod, M.R. Can Animal Models of Disease Reliably Inform Human Studies? PLoS Med. 2010, 7, e1000245. [Google Scholar] [CrossRef] [Green Version]
- Chung, A.; Nasralla, D.; Quaglia, A. Understanding the Immunoenvironment of Primary Liver Cancer: A Histopathology Perspective. J. Hepatocell. Carcinoma 2022, 9, 1149–1169. [Google Scholar] [CrossRef]
- Lendvai, G.; Szekerczés, T.; Illyés, I.; Dóra, R.; Kontsek, E.; Gógl, A.; Kiss, A.; Werling, K.; Kovalszky, I.; Schaff, Z.; et al. Cholangiocarcinoma: Classification, Histopathology and Molecular Carcinogenesis. Pathol. Oncol. Res. 2020, 26, 3–15. [Google Scholar] [CrossRef]
- Mungenast, F.; Fernando, A.; Nica, R.; Boghiu, B.; Lungu, B.; Batra, J.; Ecker, R.C. Next-Generation Digital Histopathology of the Tumor Microenvironment. Genes 2021, 12, 538. [Google Scholar] [CrossRef] [PubMed]
- Jia, K.; Chen, Y.; Sun, Y.; Hu, Y.; Jiao, L.; Ma, J.; Yuan, J.; Qi, C.; Li, Y.; Gong, J.; et al. Multiplex Immunohistochemistry Defines the Tumor Immune Microenvironment and Immunotherapeutic Outcome in CLDN18.2-Positive Gastric Cancer. BMC Med. 2022, 20, 223. [Google Scholar] [CrossRef]
- Ahn, J.S.; Al-Habib, A.; Vos, J.A.; Sohani, A.R.; Barboza-Quintana, O.; Flores, J.P.; Wen, S.; Rosado, F.G. Plasmablastic Lymphomas: Characterization of Tumor Microenvironment Using CD163 and PD-1 Immunohistochemistry. Ann. Clin. Lab. Sci. 2020, 50, 213–218. [Google Scholar]
- Papenfuss, H.D.; Gross, J.F.; Intaglietta, M.; Treese, F.A. A Transparent Access Chamber for the Rat Dorsal Skin Fold. Microvasc. Res. 1979, 18, 311–318. [Google Scholar] [CrossRef]
- Lunt, S.J.; Gray, C.; Reyes-Aldasoro, C.C.; Matcher, S.J.; Tozer, G.M. Application of Intravital Microscopy in Studies of Tumor Microcirculation. J. Biomed. Opt. 2010, 15, 011113. [Google Scholar] [CrossRef]
- Akerman, S.; Fisher, M.; Daniel, R.A.; Lefley, D.; Reyes-Aldasoro, C.C.; Lunt, S.J.; Harris, S.; Bjorndahl, M.; Williams, L.J.; Evans, H.; et al. Influence of Soluble or Matrix-Bound Isoforms of Vascular Endothelial Growth Factor-A on Tumor Response to Vascular-Targeted Strategies. Int. J. Cancer 2013, 133, 2563–2576. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Reyes-Aldasoro, C.C.; Wilson, I.; Prise, V.E.; Barber, P.R.; Ameer-Beg, M.; Vojnovic, B.; Cunningham, V.J.; Tozer, G.M. Estimation of Apparent Tumor Vascular Permeability from Multiphoton Fluorescence Microscopic Images of P22 Rat Sarcomas in Vivo. Microcirculation 2008, 15, 65–79. [Google Scholar] [CrossRef]
- Prasad, S.; Chandra, A.; Cavo, M.; Parasido, E.; Fricke, S.; Lee, Y.; D’Amone, E.; Gigli, G.; Albanese, C.; Rodriguez, O.; et al. Optical and Magnetic Resonance Imaging Approaches for Investigating the Tumour Microenvironment: State-of-the-Art Review and Future Trends. Nanotechnology 2020, 32, 062001. [Google Scholar] [CrossRef]
- Matsuo, M.; Matsumoto, S.; Mitchell, J.B.; Krishna, M.C.; Camphausen, K. Magnetic Resonance Imaging of the Tumor Microenvironment in Radiotherapy: Perfusion, Hypoxia, and Metabolism. Semin. Radiat. Oncol. 2014, 24, 210–217. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zinnhardt, B.; Roncaroli, F.; Foray, C.; Agushi, E.; Osrah, B.; Hugon, G.; Jacobs, A.H.; Winkeler, A. Imaging of the Glioma Microenvironment by TSPO PET. Eur. J. Nucl. Med. Mol. Imaging 2021, 49, 174–185. [Google Scholar] [CrossRef] [PubMed]
- Lilburn, D.M.L.; Groves, A.M. The Role of PET in Imaging of the Tumour Microenvironment and Response to Immunotherapy. Clin. Radiol. 2021, 76, 784.e1–784.e15. [Google Scholar] [CrossRef]
- Lambert, R.A. Comparative studies upon cancer cells and normal cells: II. the character of growth in vitro with special reference to cell division. J. Exp. Med. 1913, 17, 499–510. [Google Scholar] [CrossRef] [Green Version]
- Eichorn, P.A.; Huffman, K.V.; Oleson, J.J.; Halliday, S.L.; Williams, J.H. A Comparison of in Vivo and in Vitro Tests for Antineoplastic Activity of Eight Compounds. Ann. N. Y. Acad. Sci. 1954, 58, 1172–1182. [Google Scholar] [CrossRef]
- Tuveson, D.; Clevers, H. Cancer Modeling Meets Human Organoid Technology. Science 2019, 364, 952–955. [Google Scholar] [CrossRef]
- Baker, S.C.; Shabir, S.; Southgate, J. Biomimetic Urothelial Tissue Models for the in Vitro Evaluation of Barrier Physiology and Bladder Drug Efficacy. Mol. Pharm. 2014, 11, 1964–1970. [Google Scholar] [CrossRef]
- Pound, P.; Ritskes-Hoitinga, M. Is It Possible to Overcome Issues of External Validity in Preclinical Animal Research? Why Most Animal Models Are Bound to Fail. J. Transl. Med. 2018, 16, 304. [Google Scholar] [CrossRef] [Green Version]
- Musa, M.; Ouaret, D.; Bodmer, W.F. In Vitro Analyses of Interactions Between Colonic Myofibroblasts and Colorectal Cancer Cells for Anticancer Study. Anticancer Res. 2020, 40, 6063–6073. [Google Scholar] [CrossRef]
- Alhussan, A.; Palmerley, N.; Smazynski, J.; Karasinska, J.; Renouf, D.J.; Schaeffer, D.F.; Beckham, W.; Alexander, A.S.; Chithrani, D.B. Potential of Gold Nanoparticle in Current Radiotherapy Using a Co-Culture Model of Cancer Cells and Cancer Associated Fibroblast Cells. Cancers 2022, 14, 3586. [Google Scholar] [CrossRef] [PubMed]
- Hamilton, E.; Pearce, L.; Morgan, L.; Robinson, S.; Ware, V.; Brennan, P.; Thomas, N.S.B.; Yallop, D.; Devereux, S.; Fegan, C.; et al. Mimicking the Tumour Microenvironment: Three Different Co-Culture Systems Induce a Similar Phenotype but Distinct Proliferative Signals in Primary Chronic Lymphocytic Leukaemia Cells. Br. J. Haematol. 2012, 158, 589–599. [Google Scholar] [CrossRef]
- Xu, R.; Richards, F.M. Development of In Vitro Co-Culture Model in Anti-Cancer Drug Development Cascade. Comb. Chem. High Throughput Screen. 2017, 20, 451–457. [Google Scholar] [CrossRef] [PubMed]
- Curtis, M.; Kenny, H.A.; Ashcroft, B.; Mukherjee, A.; Johnson, A.; Zhang, Y.; Helou, Y.; Batlle, R.; Liu, X.; Gutierrez, N.; et al. Fibroblasts Mobilize Tumor Cell Glycogen to Promote Proliferation and Metastasis. Cell Metab. 2019, 29, 141–155.e9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Erdogan, B.; Ao, M.; White, L.M.; Means, A.L.; Brewer, B.M.; Yang, L.; Washington, M.K.; Shi, C.; Franco, O.E.; Weaver, A.M.; et al. Cancer-Associated Fibroblasts Promote Directional Cancer Cell Migration by Aligning Fibronectin. J. Cell Biol. 2017, 216, 3799–3816. [Google Scholar] [CrossRef] [PubMed]
- Kanthou, C.; Dachs, G.U.; Lefley, D.V.; Steele, A.J.; Coralli-Foxon, C.; Harris, S.; Greco, O.; Dos Santos, S.A.; Reyes-Aldasoro, C.C.; English, W.R.; et al. Tumour Cells Expressing Single VEGF Isoforms Display Distinct Growth, Survival and Migration Characteristics. PLoS ONE 2014, 9, e104015. [Google Scholar] [CrossRef]
- Wen, S.; Hou, Y.; Fu, L.; Xi, L.; Yang, D.; Zhao, M.; Qin, Y.; Sun, K.; Teng, Y.; Liu, M. Cancer-Associated Fibroblast (CAF)-Derived IL32 Promotes Breast Cancer Cell Invasion and Metastasis via Integrin Β3-P38 MAPK Signalling. Cancer Lett. 2019, 442, 320–332. [Google Scholar] [CrossRef]
- Kikuchi, J.; Koyama, D.; Mukai, H.Y.; Furukawa, Y. Suitable Drug Combination with Bortezomib for Multiple Myeloma under Stroma-Free Conditions and in Contact with Fibronectin or Bone Marrow Stromal Cells. Int. J. Hematol. 2014, 99, 726–736. [Google Scholar] [CrossRef] [PubMed]
- Lunt, S.J.; Akerman, S.; Hill, S.A.; Fisher, M.; Wright, V.J.; Reyes-Aldasoro, C.C.; Tozer, G.M.; Kanthou, C. Vascular Effects Dominate Solid Tumor Response to Treatment with Combretastatin A-4-Phosphate. Int. J. Cancer 2011, 129, 1979–1989. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kapałczyńska, M.; Kolenda, T.; Przybyła, W.; Zajączkowska, M.; Teresiak, A.; Filas, V.; Ibbs, M.; Bliźniak, R.; Łuczewski, Ł.; Lamperska, K. 2D and 3D Cell Cultures—A Comparison of Different Types of Cancer Cell Cultures. Arch. Med. Sci. 2018, 14, 910–919. [Google Scholar] [CrossRef] [PubMed]
- Ellem, S.J.; De-Juan-Pardo, E.M.; Risbridger, G.P. In Vitro Modeling of the Prostate Cancer Microenvironment. Adv. Drug Deliv. Rev. 2014, 79–80, 214–221. [Google Scholar] [CrossRef]
- Huerta-Reyes, M.; Aguilar-Rojas, A. Three-dimensional Models to Study Breast Cancer (Review). Int. J. Oncol. 2021, 58, 331–343. [Google Scholar] [CrossRef]
- Bär, S.I.; Biersack, B.; Schobert, R. 3D Cell Cultures, as a Surrogate for Animal Models, Enhance the Diagnostic Value of Preclinical in Vitro Investigations by Adding Information on the Tumour Microenvironment: A Comparative Study of New Dual-Mode HDAC Inhibitors. Invest. New Drugs 2022, 40, 953–961. [Google Scholar] [CrossRef]
- Kunz-Schughart, L.A.; Kreutz, M.; Knuechel, R. Multicellular Spheroids: A Three-Dimensional in Vitro Culture System to Study Tumour Biology. Int. J. Exp. Pathol. 1998, 79, 1–23. [Google Scholar] [CrossRef]
- Gunti, S.; Hoke, A.T.K.; Vu, K.P.; London, N.R. Organoid and Spheroid Tumor Models: Techniques and Applications. Cancers 2021, 13, 874. [Google Scholar] [CrossRef]
- Xia, T.; Du, W.-L.; Chen, X.-Y.; Zhang, Y.-N. Organoid Models of the Tumor Microenvironment and Their Applications. J. Cell Mol. Med. 2021, 25, 5829–5841. [Google Scholar] [CrossRef]
- Rizzo, R.; Onesto, V.; Forciniti, S.; Chandra, A.; Prasad, S.; Iuele, H.; Colella, F.; Gigli, G.; Del Mercato, L.L. A PH-Sensor Scaffold for Mapping Spatiotemporal Gradients in Three-Dimensional in Vitro Tumour Models. Biosens. Bioelectron. 2022, 212, 114401. [Google Scholar] [CrossRef]
- Mazzoleni, G.; Di Lorenzo, D.; Steimberg, N. Modelling Tissues in 3D: The next Future of Pharmaco-Toxicology and Food Research? Genes Nutr. 2009, 4, 13–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Neufeld, L.; Yeini, E.; Pozzi, S.; Satchi-Fainaro, R. 3D Bioprinted Cancer Models: From Basic Biology to Drug Development. Nat. Rev. Cancer 2022, 22, 679–692. [Google Scholar] [CrossRef] [PubMed]
- Leek, R.; Grimes, D.R.; Harris, A.L.; McIntyre, A. Methods: Using Three-Dimensional Culture (Spheroids) as an In Vitro Model of Tumour Hypoxia. Adv. Exp. Med. Biol. 2016, 899, 167–196. [Google Scholar] [CrossRef] [PubMed]
- Manini, I.; Caponnetto, F.; Bartolini, A.; Ius, T.; Mariuzzi, L.; Di Loreto, C.; Beltrami, A.P.; Cesselli, D. Role of Microenvironment in Glioma Invasion: What We Learned from In Vitro Models. Int. J. Mol. Sci. 2018, 19, 147. [Google Scholar] [CrossRef] [Green Version]
- Tsai, H.-F.; Trubelja, A.; Shen, A.Q.; Bao, G. Tumour-on-a-Chip: Microfluidic Models of Tumour Morphology, Growth and Microenvironment. J. R. Soc. Interface 2017, 14, 20170137. [Google Scholar] [CrossRef] [Green Version]
- Nolan, J.; Pearce, O.M.T.; Screen, H.R.C.; Knight, M.M.; Verbruggen, S.W. Organ-on-a-Chip and Microfluidic Platforms for Oncology in the UK. Cancers 2023, 15, 635. [Google Scholar] [CrossRef]
- Ozcelikkale, A.; Moon, H.-R.; Linnes, M.; Han, B. In Vitro Microfluidic Models of Tumor Microenvironment to Screen Transport of Drugs and Nanoparticles. Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 2017, 9, e1460. [Google Scholar] [CrossRef]
- Kundu, B.; Caballero, D.; Abreu, C.M.; Reis, R.L.; Kundu, S.C. The Tumor Microenvironment: An Introduction to the Development of Microfluidic Devices. Adv. Exp. Med. Biol. 2022, 1379, 115–138. [Google Scholar] [CrossRef]
- Byrne, H.M. Dissecting Cancer through Mathematics: From the Cell to the Animal Model. Nat. Rev. Cancer 2010, 10, 221–230. [Google Scholar] [CrossRef]
- Altrock, P.M.; Liu, L.L.; Michor, F. The Mathematics of Cancer: Integrating Quantitative Models. Nat. Rev. Cancer 2015, 15, 730–745. [Google Scholar] [CrossRef]
- Malthus, T.R. An Essay on the Principle of Population: Or, a View of Its Past and Present Effects on Human Happiness; Johnson, J., Ed.; Yale University Press: London, UK, 1807. [Google Scholar]
- Armitage, P.; Doll, R. The Age Distribution of Cancer and a Multi-Stage Theory of Carcinogenesis. Br. J. Cancer 1954, 8, 1983–1989. [Google Scholar] [CrossRef] [Green Version]
- Clark, T.G.; Bradburn, M.J.; Love, S.B.; Altman, D.G. Survival Analysis Part I: Basic Concepts and First Analyses. Br. J. Cancer 2003, 89, 232–238. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fan, J.; Yu, Z. A Univariate Model of Calcium Release in the Dyadic Cleft of Cardiac Myocytes. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2009, 2009, 4499–4503. [Google Scholar] [CrossRef] [PubMed]
- Bradburn, M.J.; Clark, T.G.; Love, S.B.; Altman, D.G. Survival Analysis Part II: Multivariate Data Analysis—An Introduction to Concepts and Methods. Br. J. Cancer 2003, 89, 431–436. [Google Scholar] [CrossRef]
- Azuma, K.; Xiang, H.; Tagami, T.; Kasajima, R.; Kato, Y.; Karakawa, S.; Kikuchi, S.; Imaizumi, A.; Matsuo, N.; Ishii, H.; et al. Clinical Significance of Plasma-Free Amino Acids and Tryptophan Metabolites in Patients with Non-Small Cell Lung Cancer Receiving PD-1 Inhibitor: A Pilot Cohort Study for Developing a Prognostic Multivariate Model. J. Immunother. Cancer 2022, 10, e004420. [Google Scholar] [CrossRef]
- Beckman, R.A.; Kareva, I.; Adler, F.R. How Should Cancer Models Be Constructed? Cancer Control 2020, 27, 1073274820962008. [Google Scholar] [CrossRef] [PubMed]
- Anderson, A.R.A.; Quaranta, V. Integrative Mathematical Oncology. Nat. Rev. Cancer 2008, 8, 227–234. [Google Scholar] [CrossRef] [PubMed]
- Curtin, L.; Hawkins-Daarud, A.; Porter, A.B.; van der Zee, K.G.; Owen, M.R.; Swanson, K.R. A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma. Bull. Math. Biol. 2020, 82, 143. [Google Scholar] [CrossRef]
- Menon, D.R.; Fujita, M. A State of Stochastic Cancer Stemness through the CDK1-SOX2 Axis. Oncotarget 2019, 10, 2583–2585. [Google Scholar] [CrossRef] [Green Version]
- Kumar, N.; Cramer, G.M.; Dahaj, S.A.Z.; Sundaram, B.; Celli, J.P.; Kulkarni, R.V. Stochastic Modeling of Phenotypic Switching and Chemoresistance in Cancer Cell Populations. Sci. Rep. 2019, 9, 10845. [Google Scholar] [CrossRef] [Green Version]
- Gommes, C.J.; Louis, T.; Bourgot, I.; Noël, A.; Blacher, S.; Maquoi, E. Remodelling of the Fibre-Aggregate Structure of Collagen Gels by Cancer-Associated Fibroblasts: A Time-Resolved Grey-Tone Image Analysis Based on Stochastic Modelling. Front. Immunol. 2022, 13, 988502. [Google Scholar] [CrossRef] [PubMed]
- Morales, V.; Soto-Ortiz, L. Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success. Open J. Immunol. 2018, 8, 36–80. [Google Scholar] [CrossRef] [PubMed]
- Blaszczak, W.; Swietach, P. What Do Cellular Responses to Acidity Tell Us about Cancer? Cancer Metastasis Rev. 2021, 40, 1159–1176. [Google Scholar] [CrossRef] [PubMed]
- Belfatto, A.; Vidal Urbinati, A.M.; Ciardo, D.; Franchi, D.; Cattani, F.; Lazzari, R.; Jereczek-Fossa, B.A.; Orecchia, R.; Baroni, G.; Cerveri, P. Comparison between Model-Predicted Tumor Oxygenation Dynamics and Vascular-/Flow-Related Doppler Indices. Med. Phys. 2017, 44, 2011–2019. [Google Scholar] [CrossRef]
- Zhang, A.; Xu, L.X.; Sandison, G.A.; Zhang, J. A Microscale Model for Prediction of Breast Cancer Cell Damage during Cryosurgery. Cryobiology 2003, 47, 143–154. [Google Scholar] [CrossRef]
- Possenti, L.; Cicchetti, A.; Rosati, R.; Cerroni, D.; Costantino, M.L.; Rancati, T.; Zunino, P. A Mesoscale Computational Model for Microvascular Oxygen Transfer. Ann. Biomed. Eng. 2021, 49, 3356–3373. [Google Scholar] [CrossRef]
- Munck, S.; Cawthorne, C.; Escamilla-Ayala, A.; Kerstens, A.; Gabarre, S.; Wesencraft, K.; Battistella, E.; Craig, R.; Reynaud, E.G.; Swoger, J.; et al. Challenges and Advances in Optical 3D Mesoscale Imaging. J. Microsc. 2022, 286, 201–219. [Google Scholar] [CrossRef]
- Li, X.; Zhang, Y.; Wu, J.; Dai, Q. Challenges and Opportunities in Bioimage Analysis. Nat. Methods 2023, 20, 958–961. [Google Scholar] [CrossRef]
- Chen, H.; Cai, Y.; Chen, Q.; Li, Z. Multiscale Modeling of Solid Stress and Tumor Cell Invasion in Response to Dynamic Mechanical Microenvironment. Biomech. Model. Mechanobiol. 2020, 19, 577–590. [Google Scholar] [CrossRef]
- Sadhukhan, S.; Mishra, P.K.; Basu, S.K.; Mandal, J.K. A Multi-Scale Agent-Based Model for Avascular Tumour Growth. Biosystems 2021, 206, 104450. [Google Scholar] [CrossRef]
- Wang, Z.; Butner, J.D.; Kerketta, R.; Cristini, V.; Deisboeck, T.S. Simulating Cancer Growth with Multiscale Agent-Based Modeling. Semin. Cancer Biol. 2015, 30, 70–78. [Google Scholar] [CrossRef] [Green Version]
- Gerlee, P.; Kim, E.; Anderson, A.R.A. Bridging Scales in Cancer Progression: Mapping Genotype to Phenotype Using Neural Networks. Semin. Cancer Biol. 2015, 30, 30–41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wijeratne, P.A.; Vavourakis, V.; Hipwell, J.H.; Voutouri, C.; Papageorgis, P.; Stylianopoulos, T.; Evans, A.; Hawkes, D.J. Multiscale Modelling of Solid Tumour Growth: The Effect of Collagen Micromechanics. Biomech. Model. Mechanobiol. 2016, 15, 1079–1090. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kumar, P.; Li, J.; Surulescu, C. Multiscale Modeling of Glioma Pseudopalisades: Contributions from the Tumor Microenvironment. J. Math. Biol. 2021, 82, 49. [Google Scholar] [CrossRef]
- Powathil, G.G.; Swat, M.; Chaplain, M.A.J. Systems Oncology: Towards Patient-Specific Treatment Regimes Informed by Multiscale Mathematical Modelling. Semin. Cancer Biol. 2015, 30, 13–20. [Google Scholar] [CrossRef] [Green Version]
- Nikmaneshi, M.R.; Firoozabadi, B. Investigation of Cancer Response to Chemotherapy: A Hybrid Multi-Scale Mathematical and Computational Model of the Tumor Microenvironment. Biomech. Model. Mechanobiol. 2022, 21, 1233–1249. [Google Scholar] [CrossRef]
- Peng, L.; Trucu, D.; Lin, P.; Thompson, A.; Chaplain, M.A.J. A Multiscale Mathematical Model of Tumour Invasive Growth. Bull. Math. Biol. 2017, 79, 389–429. [Google Scholar] [CrossRef] [Green Version]
- Chowkwale, M.; Mahler, G.J.; Huang, P.; Murray, B.T. A Multiscale in Silico Model of Endothelial to Mesenchymal Transformation in a Tumor Microenvironment. J. Theor. Biol. 2019, 480, 229–240. [Google Scholar] [CrossRef]
- Pourhasanzade, F.; Sabzpoushan, S.H. A New Mathematical Model for Controlling Tumor Growth Based on Microenvironment Acidity and Oxygen Concentration. BioMed Res. Int. 2021, 2021, 8886050. [Google Scholar] [CrossRef]
- Tusscher, t.K.H.W.J.; Noble, D.; Noble, P.J.; Panfilov, A.V. A Model for Human Ventricular Tissue. Am. J. Physiol. Heart Circ. Physiol. 2004, 286, H1573–H1589. [Google Scholar] [CrossRef] [PubMed]
- Norton, K.-A.; Gong, C.; Jamalian, S.; Popel, A.S. Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment. Processes 2019, 7, 37. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Noble, D. Modeling the Heart—From Genes to Cells to the Whole Organ. Science 2002, 295, 1678–1682. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Walker, D.C.; Southgate, J. The Virtual Cell—A Candidate Co-Ordinator for “middle-out” Modelling of Biological Systems. Brief. Bioinform. 2009, 10, 450–461. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Walker, D.; Wood, S.; Southgate, J.; Holcombe, M.; Smallwood, R. An Integrated Agent-Mathematical Model of the Effect of Intercellular Signalling via the Epidermal Growth Factor Receptor on Cell Proliferation. J. Theor. Biol. 2006, 242, 774–789. [Google Scholar] [CrossRef]
- Rojas-Domínguez, A.; Arroyo-Duarte, R.; Rincón-Vieyra, F.; Alvarado-Mentado, M. Modeling Cancer Immunoediting in Tumor Microenvironment with System Characterization through the Ising-Model Hamiltonian. BMC Bioinform. 2022, 23, 200. [Google Scholar] [CrossRef]
- Rahbar, S.; Shafiekhani, S.; Allahverdi, A.; Jamali, A.; Kheshtchin, N.; Ajami, M.; Mirsanei, Z.; Habibi, S.; Makkiabadi, B.; Hadjati, J.; et al. Agent-Based Modeling of Tumor and Immune System Interactions in Combinational Therapy with Low-Dose 5-Fluorouracil and Dendritic Cell Vaccine in Melanoma B16F10. Iran J. Allergy Asthma Immunol. 2022, 21, 151–166. [Google Scholar] [CrossRef]
- Cesaro, G.; Milia, M.; Baruzzo, G.; Finco, G.; Morandini, F.; Lazzarini, A.; Alotto, P.; da Cunha Carvalho de Miranda, N.F.; Trajanoski, Z.; Finotello, F.; et al. MAST: A Hybrid Multi-Agent Spatio-Temporal Model of Tumor Microenvironment Informed Using a Data-Driven Approach. Bioinform. Adv. 2022, 2, vbac092. [Google Scholar] [CrossRef]
- Tylutki, Z.; Polak, S.; Wiśniowska, B. Top-down, Bottom-up and Middle-out Strategies for Drug Cardiac Safety Assessment via Modeling and Simulations. Curr. Pharmacol. Rep. 2016, 2, 171–177. [Google Scholar] [CrossRef] [Green Version]
- Tsirvouli, E.; Touré, V.; Niederdorfer, B.; Vázquez, M.; Flobak, Å.; Kuiper, M. A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines. Front. Mol. Biosci. 2020, 7, 502573. [Google Scholar] [CrossRef]
- Sugano, K. Lost in Modelling and Simulation? ADMET DMPK 2021, 9, 75–109. [Google Scholar] [CrossRef]
- Secomb, T.W.; Pries, A.R. The Microcirculation: Physiology at the Mesoscale. J. Physiol. 2011, 589, 1047–1052. [Google Scholar] [CrossRef]
- Korolev, K.S.; Xavier, J.B.; Gore, J. Turning Ecology and Evolution against Cancer. Nat. Rev. Cancer 2014, 14, 371–380. [Google Scholar] [CrossRef] [PubMed]
- Dujon, A.M.; Aktipis, A.; Alix-Panabières, C.; Amend, S.R.; Boddy, A.M.; Brown, J.S.; Capp, J.; DeGregori, J.; Ewald, P.; Gatenby, R.; et al. Identifying Key Questions in the Ecology and Evolution of Cancer. Evol. Appl. 2021, 14, 877–892. [Google Scholar] [CrossRef] [PubMed]
- Bukkuri, A.; Pienta, K.J.; Hockett, I.; Austin, R.H.; Hammarlund, E.U.; Amend, S.R.; Brown, J.S. Modeling Cancer’s Ecological and Evolutionary Dynamics. Med. Oncol. 2023, 40, 109. [Google Scholar] [CrossRef]
- Morris, B.; Curtin, L.; Hawkins-Daarud, A.; Hubbard, M.E.; Rahman, R.; Smith, S.J.; Auer, D.; Tran, N.L.; Hu, L.S.; Eschbacher, J.M.; et al. Identifying the Spatial and Temporal Dynamics of Molecularly-Distinct Glioblastoma Sub-Populations. Math. Biosci. Eng. 2020, 17, 4905–4941. [Google Scholar] [CrossRef] [PubMed]
- Luo, W. Nasopharyngeal Carcinoma Ecology Theory: Cancer as Multidimensional Spatiotemporal “Unity of Ecology and Evolution” Pathological Ecosystem. Theranostics 2023, 13, 1607–1631. [Google Scholar] [CrossRef] [PubMed]
- Daoust, S.P.; Fahrig, L.; Martin, A.E.; Thomas, F. From Forest and Agro-Ecosystems to the Microecosystems of the Human Body: What Can Landscape Ecology Tell Us about Tumor Growth, Metastasis, and Treatment Options? Evol. Appl. 2013, 6, 82–91. [Google Scholar] [CrossRef]
- Thomas, F.; Nesse, R.M.; Gatenby, R.; Gidoin, C.; Renaud, F.; Roche, B.; Ujvari, B. Evolutionary Ecology of Organs: A Missing Link in Cancer Development? Trends Cancer 2016, 2, 409–415. [Google Scholar] [CrossRef]
- Antal, T.; Krapivsky, P.L. Exact Solution of a Two-Type Branching Process: Models of Tumor Progression. J. Stat. Mech. 2011, 2011, P08018. [Google Scholar] [CrossRef]
- Bozic, I.; Antal, T.; Ohtsuki, H.; Carter, H.; Kim, D.; Chen, S.; Karchin, R.; Kinzler, K.W.; Vogelstein, B.; Nowak, M.A. Accumulation of Driver and Passenger Mutations during Tumor Progression. Proc. Natl. Acad. Sci. USA 2010, 107, 18545–18550. [Google Scholar] [CrossRef]
- Lewin, T.D.; Avignon, B.; Tovaglieri, A.; Cabon, L.; Gjorevski, N.; Hutchinson, L.G. An in Silico Model of T Cell Infiltration Dynamics Based on an Advanced in Vitro System to Enhance Preclinical Decision Making in Cancer Immunotherapy. Front. Pharmacol. 2022, 13, 837261. [Google Scholar] [CrossRef]
- Curtin, L.; Hawkins-Daarud, A.; van der Zee, K.G.; Swanson, K.R.; Owen, M.R. Speed Switch in Glioblastoma Growth Rate Due to Enhanced Hypoxia-Induced Migration. Bull. Math. Biol. 2020, 82, 43. [Google Scholar] [CrossRef] [PubMed]
- de Melo Quintela, B.; Hervas-Raluy, S.; Garcia-Aznar, J.M.; Walker, D.; Wertheim, K.Y.; Viceconti, M. A Theoretical Analysis of the Scale Separation in a Model to Predict Solid Tumour Growth. J. Theor. Biol. 2022, 547, 111173. [Google Scholar] [CrossRef] [PubMed]
- Anderson, A.R.A. A Hybrid Multiscale Model of Solid Tumour Growth and Invasion: Evolution and the Microenvironment. In Single-Cell-Based Models in Biology and Medicine; Anderson, A.R.A., Chaplain, M.A.J., Rejniak, K.A., Eds.; Mathematics and Biosciences in Interaction; Birkhäuser: Basel, Switzerland, 2007; pp. 3–28. ISBN 978-3-7643-8123-3. [Google Scholar]
- Chaplain, M.a.J.; McDougall, S.R.; Anderson, A.R.A. Mathematical Modeling of Tumor-Induced Angiogenesis. Annu. Rev. Biomed. Eng. 2006, 8, 233–257. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chaplain, M.A.; Giles, S.M.; Sleeman, B.D.; Jarvis, R.J. A Mathematical Analysis of a Model for Tumour Angiogenesis. J. Math. Biol. 1995, 33, 744–770. [Google Scholar] [CrossRef]
- Enderling, H.; Chaplain, M.A.J.; Anderson, A.R.A.; Vaidya, J.S. A Mathematical Model of Breast Cancer Development, Local Treatment and Recurrence. J. Theor. Biol. 2007, 246, 245–259. [Google Scholar] [CrossRef]
- Ramis-Conde, I.; Chaplain, M.A.J.; Anderson, A.R.A.; Drasdo, D. Multi-Scale Modelling of Cancer Cell Intravasation: The Role of Cadherins in Metastasis. Phys. Biol. 2009, 6, 016008. [Google Scholar] [CrossRef]
- Sleeman, B.D.; Nimmo, H.R. Fluid Transport in Vascularized Tumours and Metastasis. IMA J. Math. Appl. Med. Biol. 1998, 15, 53–63. [Google Scholar] [CrossRef]
- Owen, M.R.; Byrne, H.M.; Lewis, C.E. Mathematical Modelling of the Use of Macrophages as Vehicles for Drug Delivery to Hypoxic Tumour Sites. J. Theor. Biol. 2004, 226, 377–391. [Google Scholar] [CrossRef] [Green Version]
- Lewin, T.D.; Byrne, H.M.; Maini, P.K.; Caudell, J.J.; Moros, E.G.; Enderling, H. The Importance of Dead Material within a Tumour on the Dynamics in Response to Radiotherapy. Phys. Med. Biol. 2020, 65, 015007. [Google Scholar] [CrossRef]
- Italia, M.; Wertheim, K.Y.; Taschner-Mandl, S.; Walker, D.; Dercole, F. Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma. Cancers 2023, 15, 1986. [Google Scholar] [CrossRef]
- Araujo, A.; Cook, L.M.; Lynch, C.C.; Basanta, D. An Integrated Computational Model of the Bone Microenvironment in Bone-Metastatic Prostate Cancer. Cancer Res. 2014, 74, 2391–2401. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Clarke, M.A.; Fisher, J. Executable Cancer Models: Successes and Challenges. Nat. Rev. Cancer 2020, 20, 343–354. [Google Scholar] [CrossRef] [PubMed]
- Myung, J.I.; Tang, Y.; Pitt, M.A. Chapter 11 Evaluation and Comparison of Computational Models. In Methods in Enzymology; Computer Methods, Part A; Academic Press: Cambridge, MA, USA, 2009; Volume 454, pp. 287–304. [Google Scholar]
- Goldstein, B.; Faeder, J.R.; Hlavacek, W.S. Mathematical and Computational Models of Immune-Receptor Signalling. Nat. Rev. Immunol. 2004, 4, 445–456. [Google Scholar] [CrossRef]
- Ji, Z.; Yan, K.; Li, W.; Hu, H.; Zhu, X. Mathematical and Computational Modeling in Complex Biological Systems. BioMed. Res. Int. 2017, 2017, e5958321. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Konstorum, A.; Vella, A.T.; Adler, A.J.; Laubenbacher, R.C. Addressing Current Challenges in Cancer Immunotherapy with Mathematical and Computational Modelling. J. R. Soc. Interface 2017, 14, 20170150. [Google Scholar] [CrossRef] [Green Version]
- Garcia, V.; Bonhoeffer, S.; Fu, F. Cancer-Induced Immunosuppression Can Enable Effectiveness of Immunotherapy through Bistability Generation: A Mathematical and Computational Examination. J. Theor. Biol. 2020, 492, 110185. [Google Scholar] [CrossRef]
- Vega, R.; Carretero, M.; Travasso, R.D.M.; Bonilla, L.L. Notch Signaling and Taxis Mechanisms Regulate Early Stage Angiogenesis: A Mathematical and Computational Model. PLoS Comput. Biol. 2020, 16, e1006919. [Google Scholar] [CrossRef] [Green Version]
- West, J.; Robertson-Tessi, M.; Anderson, A.R.A. Agent-Based Methods Facilitate Integrative Science in Cancer. Trends Cell Biol. 2023, 33, 300–311. [Google Scholar] [CrossRef]
- Metzcar, J.; Wang, Y.; Heiland, R.; Macklin, P. A Review of Cell-Based Computational Modeling in Cancer Biology. JCO Clin. Cancer Inf. 2019, 3, 1–13. [Google Scholar] [CrossRef]
- Homeyer, A.; Nasr, P.; Engel, C.; Kechagias, S.; Lundberg, P.; Ekstedt, M.; Kost, H.; Weiss, N.; Palmer, T.; Hahn, H.K.; et al. Automated Quantification of Steatosis: Agreement with Stereological Point Counting. Diagn. Pathol. 2017, 12, 80. [Google Scholar] [CrossRef] [Green Version]
- Dawood, M.; Branson, K.; Rajpoot, N.M.; Minhas, F.U.A.A. All You Need Is Color: Image Based Spatial Gene Expression Prediction Using Neural Stain Learning. In Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Virtual Event, 13–17 September 2021; Kamp, M., Koprinska, I., Bibal, A., Bouadi, T., Frénay, B., Galárraga, L., Oramas, J., Adilova, L., Eds.; Springer International Publishing: Cham, Switzerland, 2021; pp. 437–450. [Google Scholar]
- Ortega-Ruiz, M.A.; Karabağ, C.; Garduño, V.G.; Reyes-Aldasoro, C.C. Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images. J. Imaging 2020, 6, 101. [Google Scholar] [CrossRef] [PubMed]
- Serin, F.; Erturkler, M.; Gul, M. A Novel Overlapped Nuclei Splitting Algorithm for Histopathological Images. Comput. Methods Programs Biomed. 2017, 151, 57–70. [Google Scholar] [CrossRef] [PubMed]
- Sullivan, C.A.W.; Ghosh, S.; Ocal, I.T.; Camp, R.L.; Rimm, D.L.; Chung, G.G. Microvessel Area Using Automated Image Analysis Is Reproducible and Is Associated with Prognosis in Breast Cancer. Hum. Pathol. 2009, 40, 156–165. [Google Scholar] [CrossRef]
- Patlak, C.S.; Blasberg, R.G.; Fenstermacher, J.D. Graphical Evaluation of Blood-to-Brain Transfer Constants from Multiple-Time Uptake Data. J. Cereb. Blood Flow Metab. 1983, 3, 1–7. [Google Scholar] [CrossRef]
- Reyes-Aldasoro, C.C.; Akerman, S.; Tozer, G.M. Measuring the Velocity of Fluorescently Labelled Red Blood Cells with a Keyhole Tracking Algorithm. J. Microsc. 2008, 229, 162–173. [Google Scholar] [CrossRef] [Green Version]
- Yuan, Y. Spatial Heterogeneity in the Tumor Microenvironment. Cold Spring Harb. Perspect. Med. 2016, 6, a026583. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mcculloch, W.S.; Pitts, W. The Statistical Organization of Nervous Activity. Biometrics 1948, 4, 91–99. [Google Scholar] [CrossRef]
- beim Graben, P.; Wright, J. From McCulloch-Pitts Neurons toward Biology. Bull. Math. Biol. 2011, 73, 261–265. [Google Scholar] [CrossRef] [Green Version]
- Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 2015, arXiv:1409.1556. [Google Scholar]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 2017, 60, 84–90. [Google Scholar] [CrossRef] [Green Version]
- Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv 2015, arXiv:1505.04597. [Google Scholar]
- Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going Deeper with Convolutions. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7–12 June 2015; pp. 1–9. [Google Scholar]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef] [PubMed]
- Kriegeskorte, N.; Golan, T. Neural Network Models and Deep Learning. Curr. Biol. 2019, 29, R231–R236. [Google Scholar] [CrossRef]
- Kuntz, S.; Krieghoff-Henning, E.; Kather, J.N.; Jutzi, T.; Höhn, J.; Kiehl, L.; Hekler, A.; Alwers, E.; von Kalle, C.; Fröhling, S.; et al. Gastrointestinal Cancer Classification and Prognostication from Histology Using Deep Learning: Systematic Review. Eur. J. Cancer 2021, 155, 200–215. [Google Scholar] [CrossRef]
- Davri, A.; Birbas, E.; Kanavos, T.; Ntritsos, G.; Giannakeas, N.; Tzallas, A.T.; Batistatou, A. Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review. Diagnostics 2022, 12, 837. [Google Scholar] [CrossRef]
- Tran, K.A.; Kondrashova, O.; Bradley, A.; Williams, E.D.; Pearson, J.V.; Waddell, N. Deep Learning in Cancer Diagnosis, Prognosis and Treatment Selection. Genome Med. 2021, 13, 152. [Google Scholar] [CrossRef]
- Bhinder, B.; Gilvary, C.; Madhukar, N.S.; Elemento, O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021, 11, 900–915. [Google Scholar] [CrossRef] [PubMed]
- Wu, J.; Lin, D. A Review of Artificial Intelligence in Precise Assessment of Programmed Cell Death-Ligand 1 and Tumor-Infiltrating Lymphocytes in Non-Small Cell Lung Cancer. Adv. Anat. Pathol. 2021, 28, 439–445. [Google Scholar] [CrossRef]
- Thakur, N.; Yoon, H.; Chong, Y. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers 2020, 12, 1884. [Google Scholar] [CrossRef]
- Bejnordi, B.E.; Mullooly, M.; Pfeiffer, R.M.; Fan, S.; Vacek, P.M.; Weaver, D.L.; Herschorn, S.; Brinton, L.A.; van Ginneken, B.; Karssemeijer, N.; et al. Using Deep Convolutional Neural Networks to Identify and Classify Tumor-Associated Stroma in Diagnostic Breast Biopsies. Mod. Pathol. 2018, 31, 1502–1512. [Google Scholar] [CrossRef] [PubMed]
- Pantanowitz, L.; Quiroga-Garza, G.M.; Bien, L.; Heled, R.; Laifenfeld, D.; Linhart, C.; Sandbank, J.; Shach, A.A.; Shalev, V.; Vecsler, M.; et al. An Artificial Intelligence Algorithm for Prostate Cancer Diagnosis in Whole Slide Images of Core Needle Biopsies: A Blinded Clinical Validation and Deployment Study. Lancet Digit. Health 2020, 2, e407–e416. [Google Scholar] [CrossRef] [PubMed]
- Kather, J.N.; Krisam, J.; Charoentong, P.; Luedde, T.; Herpel, E.; Weis, C.-A.; Gaiser, T.; Marx, A.; Valous, N.A.; Ferber, D.; et al. Predicting Survival from Colorectal Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study. PLoS Med. 2019, 16, e1002730. [Google Scholar] [CrossRef] [PubMed]
- Shaban, M.; Raza, S.E.A.; Hassan, M.; Jamshed, A.; Mushtaq, S.; Loya, A.; Batis, N.; Brooks, J.; Nankivell, P.; Sharma, N.; et al. A Digital Score of Tumour-Associated Stroma Infiltrating Lymphocytes Predicts Survival in Head and Neck Squamous Cell Carcinoma. J. Pathol. 2022, 256, 174–185. [Google Scholar] [CrossRef] [PubMed]
- Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K.Q. Densely Connected Convolutional Networks. arXiv 2018, arXiv:1608.06993. [Google Scholar]
- Reyes-Aldasoro, C.C. The Proportion of Cancer-Related Entries in PubMed Has Increased Considerably; Is Cancer Truly “The Emperor of All Maladies”? PLoS ONE 2017, 12, e0173671. [Google Scholar] [CrossRef] [Green Version]
- Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef]
Definition | Keywords |
---|---|
Model organism | Animal model. Mouse model. Mice model. Rat model. Zebrafish model. Xenograft model. In vivo model. |
In vitro model | In vitro model. Tumor on a chip. Microfluidic model. 3D Bioprinting. 3D model. Organoid model. Spheroid model. Organ on a chip. |
Mathematical model | Mechanistic Model. Scoring model. Prediction model. Risk model. Integrative model. Mathematical model. Prognostic model. |
Computational model | In silico model. Computational model. Deep Learning model. Machine Learning model. Convolutional Neural Network. Agent-based model. |
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Reyes-Aldasoro, C.C. Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”? Cancers 2023, 15, 3796. https://doi.org/10.3390/cancers15153796
Reyes-Aldasoro CC. Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”? Cancers. 2023; 15(15):3796. https://doi.org/10.3390/cancers15153796
Chicago/Turabian StyleReyes-Aldasoro, Constantino Carlos. 2023. "Modelling the Tumour Microenvironment, but What Exactly Do We Mean by “Model”?" Cancers 15, no. 15: 3796. https://doi.org/10.3390/cancers15153796