Free Access
Issue |
Math. Model. Nat. Phenom.
Volume 4, Number 3, 2009
Cancer modelling (Part 2)
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Page(s) | 117 - 133 | |
DOI | https://doi.org/10.1051/mmnp/20094305 | |
Published online | 05 June 2009 |
- D. Hanahan, R. Weinberg. The hallmarks of cancer. Cell, 100 (2000), No.1, 57–70. [Google Scholar]
- P. Armitage, R. Doll. The age distribution of cancer and a multi-stage theory of carcinogenesis. Br J Cancer, 8 (1954), No.1, 1–12. [Google Scholar]
- W.C. Black, H.G. Welch. Advances in diagnostic imaging and overestimations of disease prevalence and the benefits of therapy. N Engl J Med, 328 (1993), No.17, 1237–1243. [Google Scholar]
- T. Lapidot, C. Sirard, B. Murdoch, et al. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature, 367 (1994), No. 6464, 645–648. [Google Scholar]
- D. Bonnet, J.E. Dick. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med, 3 (1997), No. 7, 730–737. [Google Scholar]
- M. Al-Hajj, M.S. Wicha, A. Benito-Hernandez, S.J. Morrison, M.F. Clarke. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA, 100 (2003), No.7, 3983–3988. [Google Scholar]
- J.E. Dick. Breast cancer stem cells revealed. Proc Natl Acad Sci USA, 100 (2003), No.7, 3547–3549. [Google Scholar]
- S.K. Singh, C. Hawkins, I.D. Clarke. Identification of a cancer stem cell in human brain tumors. Cancer Res, 63 (2003), No.18, 5821–5828. [Google Scholar]
- S.K. Singh, I.D. Clarke, M. Terasaki, et al. Identification of human brain tumour initiating cells. Nature, 432 (2004), No.7015, 396–401. [Google Scholar]
- D. Fioriti, M. Mischitelli, F. Di Monaco, et al. Cancer stem cells in prostate adenocarcinoma: a target for new anticancer strategies. J. Cell Physiol., 216 (2008), No.3, 571–575. [Google Scholar]
- N.J. Maitland, T. Colling. Prostate cancer stem cells: a new target for therapy. J. Clin. Oncol., 26 (2008), No.17, 2862–2870. [Google Scholar]
- M. Todaro, M. Perez Alea, A.B. Di Stefano, et al. Colon cancer stem cells dictate tumor growth and resist cell death by production of interleukin-4. Cell stem cell, 1 (2007), No.4, 389–402. [Google Scholar]
- P. Cammareri, Y. Lombardo, M.G. Francipane, et al. Isolation and culture of colon cancer stem cells. Methods Cell Biol., 86 (2008), 311–324. [CrossRef] [PubMed] [Google Scholar]
- S.J. Morrison, J. Kimble. Asymmetric and symmetric stem-cell divisions in development and cancer. Nature, 41 (2006), No. 7097, 1068–1074. [Google Scholar]
- R. Reya, R., S.J. Morrison, M.F. Clarke, I.L. Weissman. Stem cells, cancer, and cancer stem cells. Nature, 414 (2001), No.6859, 105–111. [Google Scholar]
- D. Dingli, F. Michor. Successful therapy must eradicate cancer stem cells. Stem Cells, 24 (2006), No.12, 2603–2610. [Google Scholar]
- R.T. Prehn. The inhibition of tumor growth by tumor mass. Cancer Res, 51 (1991), No.1, 2–4. [Google Scholar]
- J. Folkman. Tumor angiogenesis: therapeutic implications. N Engl J Med, 285 (1971), No.21, 1182–1186. [Google Scholar]
- G.N. Naumov, E. Bender, D. Zurakowski, et al. A model of human tumor dormancy: an angiogenic switch from the nonangiogenic phenotype. J Natl Cancer Inst, 98 (2006), No.5, 316–325. [Google Scholar]
- M.H. Barcellos-Hoff. It takes a tissue to make a tumor: epigenetics, cancer and the microenvironment. Journal of mammary gland biology and neoplasia, 6 (2001), No.2, 213–221. [Google Scholar]
- R. Gatenby, R.J. Gillies. A microenvironmental model of carcinogenesis. Nat Rev Cancer, 8 (2008), No.1, 56–61. [Google Scholar]
- A. Brú, S. Albertos, J.L. Subiza et al. The universal dynamics of tumor growth. Biophys J 85 (2003), No.5, 2948–2961. [Google Scholar]
- J. Galle, M. Hoffmann, G. Aust. From single cells to tissue architecture – a bottom-up approach to modelling the spatio-temporal organization of complex multi-cellular systems. J Math Biol 58 (2009), 261–283. [Google Scholar]
- L. Norton. Conceptual and practical implications of breast tissue geometry: toward a more effective, less toxic therapy. Oncologist, 10 (2005), No. 6, 370–381. [Google Scholar]
- L. Norton, J. Massague. Is cancer a disease of self-seeding? Nat Med, 12 (2006), No.8, 875–878 [Google Scholar]
- S. Masunaga, K. Ono, M. Abe. A method for the selective measurement of the radiosensitivity of quiescent cells in solid tumors – combination of immunofluorescence staining to BrdU and micronucleus assay. Radiat Res, 125 (1991), No. 3, 243–247. [Google Scholar]
- G.W. Barendsen, C. Van Bree, N.A.P. Franken. Importance of cell proliferative state and potentially lethal damage repair on radiation effectiveness: implications for combined tumor treatments. Int. J. Oncol., 19 (2001), No. 2, 247–256. [Google Scholar]
- J.J. Kim, I.F. Tannock. Repopulation of cancer cells during therapy: an important cause of treatment failure. Nat Rev Cancer, 5 (2005), No. 7, 516–25. [Google Scholar]
- H. Enderling, A.R.A. Anderson, M.A.J. Chaplain, A.J. Munro, J.S. Vaidya. Mathematical modelling of radiotherapy strategies for early breast cancer. J. Theor. Biol., 241 (2006), No. 1, 158–171. [Google Scholar]
- H. Enderling, M.A.J. Chaplain, A.R.A. Anderson, J.S. Vaidya. A mathematical model of breast cancer development, local treatment and recurrence. J. Theor. Biol., 246 (2007), No. 2, 245–259. [Google Scholar]
- B. Ribba, T. Colin, S. Schnell. A multiscale mathematical model of cancer, and its use in analyzing irradiation therapies. Theor. Biol. Med. Model. 3 (2006), No.7. [Google Scholar]
- A. Dawson, T. Hillen. Derivation of the Tumour Control Probability (TCP) from a Cell Cycle Model. Comp. Math. Meth. Med, 7 (2006), 121–142. [CrossRef] [Google Scholar]
- T.L. Jackson, H.M. Byrne. A mathematical model to study the effects of drug resistance and vasculature on the response of solid tumors to chemotherapy. Math Biosci,164 (2000), No. 1, 17–38. [Google Scholar]
- T. Alarcon, M.R. Owen, H.M. Byrne et al. Multiscale modelling of tumour growth and therapy: the influence of vessel normalisation on chemotherapy. Comp Math Methods Med, 7(2006), 85–119. [Google Scholar]
- H.M. Byrne, T. Alarcon, M.R. Owen et al. Modelling the response of vascular tumours to chemotherapy: a multiscale approach. Math Mod Meth Appl Sci, 16 (2006), No. 1, 1219–1241. [Google Scholar]
- E.S. Norris, J.R. King, H.M. Byrne. Modelling the response of spatially structured tumours to chemotherapy: drug kinetics. Math Comp Mod, 43 (2006), No. 7-8, 820–837. [Google Scholar]
- A.R.A. Anderson, M.A.J. Chaplain, K.A. Rejniak. Single-Cell-Based Models in Biology and Medicine. Birkhauser, Basel, 2007. [Google Scholar]
- P.K. Maini, D.L.S. McElwain, D.I. Leavesley. Traveling wave model to interpret a wound-healing cell migration assay for human peritoneal mesothelial cells. Tissue Eng., 10 (2004), No.(3-4), 475–482. [Google Scholar]
- B. Ribba, K. Marron, Z. Agur, T. Alarcon, P.K. Maini. A mathematical model of Doxorubicin treatment efficacy for non-Hodgkin's lymphoma: investigation of the current protocol through theoretical modelling results. Bull Math Biol, 67 (2005), No.1, 79–99. [Google Scholar]
- M. Guerrero, X. Allen Li. Analysis of a large number of clinical studies for breast cancer radiotherapy: estimation of radiobiological parameters for treatment planning. Phys Med Biol, 48 (2003), No.20, 3307–3326. [Google Scholar]
- D.J. Brenner, L.R. Hlatky, P.J. Hahnfeldt, E.J. Hall, R.K. Sachs. A convenient extension of the linear-quadratic model to include redistribution and reoxygenation. Int J Radiat Oncol Biol Phys, 32 (1995), No. 2, 379–390. [Google Scholar]
- J.A. Stanley, W.U. Shipley, G.G. Steele. Influence of tumour size on hypoxic fraction and therapeutic sensitivity of Lewis lung tumour. Br J Cancer, 26 (1977), No.1, 105–113. [Google Scholar]
- J. Folkman, M. Hochberg. Self-regulation of growth in three dimensions. J Exp Med 138 (1973) No. 4, 745–753. [Google Scholar]
- J.M. Brown, A.J. Giaccia. The unique physiology of solid tumors: Opportunities (and problems) for cancer therapy. Cancer Res, 58 (1998), 1408 –1416. [Google Scholar]
- S. Masunaga, K. Ono, A. Takahashi, T. Ohnishi, Y. Kinashi, M. Takagaki. Radiobiological characteristics of solid tumours depending on the p53 status of the tumour cells, with emphasis on the response of intratumour quiescent cells. Eur J Cancer, 38 (2002), No. 5, 718–727. [Google Scholar]
- P. Ubezio, D. Cameron. Cell killing and resistance in pre-operative breast cancer chemotherapy. BMC Cancer, 8 (2008). [Google Scholar]
- M. Baumann, M. Krause, R. Hill. Exploring the role of cancer stem cells in radioresistance. Nat Rev Cancer, 8 (2008), No. 7, 545–554. [CrossRef] [PubMed] [Google Scholar]
- M. Baum, J.S. Vaidya. Targeted intra-operative radiotherapy–TARGIT for early breast cancer. Ann N Y Acad Sci, 1138 (2008), 132–135. [CrossRef] [PubMed] [Google Scholar]
- START Trialists' Group. The UK Standardisation of Breast Radiotherapy (START) Trial A of radiotherapy hypofractionation for treatment of early breast cancer: a randomised trial. Lancet Oncol, 9 (2008), No. 4, 331–341. [Google Scholar]
- J. Yarnold, D. Bloomeld, J. LeVay. Prospective randomized trial testing 5.7 Gy and 6.0 Gy fractions of whole breast radiotherapy in women with early breast cancer (FAST) trial. Clin Oncol, 16 (2004), S30. [Google Scholar]
- E.J. Hall. Radiobiology for the Radiologist 5th edn. Lippincott Williams & Wilkins, Philadelphia, 2000. [Google Scholar]
- S.K. Kang, J.B. Park, S.H. Cha. Multipotent, dedifferentiated cancer stem-like cells from brain gliomas. Stem Cells Dev, 15 (2006), No. 3, 423–435. [Google Scholar]
- S.A. Mani, W. Guo, M.J. Liao et al. The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell, 133 (2008), No. 4, 704–715. [Google Scholar]
- C. Guiot, P.G. Degiorgis, P.P. Delsanto et al. Does tumor growth follow a “universal law”? J Theor Biol, 225 (2003), No. 2, 147–151. [Google Scholar]
- A. Wichmann, B. Jaklevic, T.T. Su. Ionizing radiation induces caspase-dependent but Chk2- and p53-independent cell death in Drosophila melanogaster. Proc Natl Acad Sci USA, 103 (2006), No. 26, 9952–9957. [Google Scholar]
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