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Modeling the Cancer Stem Cell Hypothesis

Published online by Cambridge University Press:  28 April 2010

C. Calmelet*
Affiliation:
Department of Mathematics and Statistics, California State University Chico
A. Prokop
Affiliation:
Department of Chemical Engineering ,Vanderbilt University
J. Mensah
Affiliation:
Department of Chemistry, Tennessee State University
L. J. McCawley
Affiliation:
Department of Cancer Biology, Vanderbilt University
P. S. Crooke
Affiliation:
Department of Mathematics, Vanderbilt University
*
*Corresponding author. E-mail:ccalmelet@csuchico.edu
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Abstract

Solid tumors and hematological cancers contain small population of tumor cells that arebelieved to play a critical role in the development and progression of the disease. Thesecells, named Cancer Stem Cells (CSCs), have been found in leukemia, myeloma, breast,prostate, pancreas, colon, brain and lung cancers. It is also thought that CSCs drive themetastatic spread of cancer. The CSC compartment features a specific and phenotypicallydefined cell population characterized with self-renewal (through mutations), quiescence orslow cycling, overexpression of anti-apoptotic proteins, multidrug resistance and impaireddifferentiation. CSCs show resistance to a number of conventional therapies, and it isbelieved that this explains why it is difficult to completely eradicate the disease andwhy recurrence is an ever-present threat. A hierarchical phenomenological model isproposed based on eight compartments following the stem cell lineage at the normal andcancer cell levels. As an empirical test, the tumor grading and progression, typicallycollected in the pathologic lab, is used to correlate the outcome of this model with thetumor development stages. In addition, the model is able to quantitatively account for thetemporal development of the population of observed cell types. Two types of therapeutictreatment models are considered, with dose-density chemotherapy (a pulsatile scenario) aswell as continuous, metronomic delivery. The drug hit is considered at the stem cellprogenitor and early differentiated specialized cell levels for both normal and cancercells, while the quiescent stem cell and fully differentiated compartments are consideredfavorable outcome for cancer treatment. Circulating progenitors are neglected in thisanalysis. The model provides a number of experimentally testable predictions. The relativeimportance of the cell kill and survival is demonstrated through a deterministicparametric study. The significance of the stem cell compartment is underlined based onthis simulation study. This predictive mathematical model for cancer stem cell hypothesisis used to understand tumor responses to chemotherapeutic agents and judge theefficacy.

Type
Research Article
Copyright
© EDP Sciences, 2010

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References

Cho, R.W., Wang X, X., Diehn, M., Shedden, K., Ghen, GY, Sherlock, G, Gurney, A, Lewicki, J Clarke, MF. Isolation and molecular characterization of cancer stem cells in MMTV-Wnt-1 murine breast tumors . Stem Cells, 26 (2008), No. 2, 364-71.CrossRefGoogle ScholarPubMed
Demongeot, J., Kaufman, M. Thomas, R.. Positive feedback circuits and memory . C. R. Acad. Sci. III. 323 (2000), No. 1, 69-79.CrossRefGoogle ScholarPubMed
Dingli, D., Traulsen, A. Pacheco, J.. Stochastics Dynamics of Hematopoietic Tumor Stem Cells . Cell Cycle 6 (2007), No. 4, 461-466.CrossRefGoogle Scholar
Donnenberg, V.S., Landreneau, R.J. Donnenberg, A.D.. Tumorigenic stem and progenitor cells: implications for the therapeutic index of anti-cancer agents . Journal Control Release, 122 (2007), No. 3, 385-91.CrossRefGoogle ScholarPubMed
Enderling, H., Chaplain, M. A., Andersona, A.R., Vaidyab, J. S.. A mathematical model of breast cancer development, local treatment and recurrence . Journal of Theoretical Biology, 246 (2007) No. 2, 245-259. CrossRefGoogle ScholarPubMed
Fang, D., Nguyen, T. K., Leishear, K., Finko, R., Kulp, A. N., Hotz, S., Van Belle, P. A., Xu, X., Elder, D. E., Herlyn, M.. A tumorigenic subpopulation with stem cell properties in melanomas . Cancer Res. 65, (2005), No. 20, 9328-37. CrossRefGoogle ScholarPubMed
Freeman, M. Gurdon, J.B.. Regulatory principles of developmental signaling . Annu. Rev. Cell. Dev. Biol. 18 (2002), 515-39.CrossRefGoogle ScholarPubMed
Ganguly, R. Puri, I.K.. Mathematical model for the cancer stem cell hypothesis . Cell Prolif. 39 (2006), No. 1, 3-14.CrossRefGoogle ScholarPubMed
Ganguly, R. Puri, I.K.. Mathematical model for chemotherapeutic drug efficacy in arresting tumour growth based on the cancer stem cell hypothesis . Cell Prolif. 40 (2007), No. 3, 338-54.CrossRefGoogle ScholarPubMed
Hill, R.P.. Identifying cancer stem cells in solid tumors: case not proven . Cancer Res. 66 (2006), No. 4, 1891-5.CrossRefGoogle Scholar
Johnston, M.D., Edwards, C.M., Bodmer, W.F., Maini, P.K. Chapman, S.J.. Mathematical modeling of cell population dynamics in the colonic crypt and in colorectal cancer . Proc. Natl. Acad. Sci. 104 (2007), No. 10, 4008-13.CrossRefGoogle ScholarPubMed
King-Smith, E.A. Morley, A.. Computer simulation of granulopoiesis: normal and impaired granulopoiesis . Blood. 36 (1970), No. 2, 254-62.Google ScholarPubMed
Laquente, B., ViŰals, F. GermĹ, J.R.. Metronomic chemotherapy: an antiangiogenic scheduling . Clin. Transl. Oncol. 9 (2007), No. 2, 93-8.CrossRefGoogle Scholar
Leszczyniecka, M., Roberts, T., Dent, P., Grant, S., Fisher, P.B.. Differentiation therapy of human cancer: basic science and clinical applications . Pharmacol Ther. 90 (2001) No. 2-3, 105-56. CrossRefGoogle ScholarPubMed
Malanchi, I. Huelsken, J.. Cancer Stem cells: never Wnt away from the niche . Current Opinion in Oncology 21 (2008), 41-46.CrossRefGoogle Scholar
Marchal, J.A., RodrŠguez-Serrano, F., Madeddu, R., Boulaiz, H., Martinez-Amat, A., Carrillo, E., Caba, O., Prados, J.C., Velez, C., Melguizo, C., Montella, A. Aranega, A.. Differentiation: an encouraging approach to anticancer therapy . J. Anat. Embryol. 111 (2006), No. 1, 45-64.Google ScholarPubMed
Michor, F., Hughes, T.P., Iwasa, Y., Branford, S.,Shah, N. P., Sawyers, C. L., Nowack, M. A.. Dynamics of Chronic Myeloid Leukaemia. , Nature 435 (2005) 1267-1270. CrossRefGoogle ScholarPubMed
Nilsson, J.A. Cleveland, J.L.. Myc pathways provoking cell suicide and cancer . Oncogene. 22 (2003), No. 56, 9007-21.CrossRefGoogle ScholarPubMed
Pardee, A.B.. Regulatory molecular biology . Cell Cycle. 5 (2006), No. 8, 846-52.CrossRefGoogle ScholarPubMed
Pardee, A.B.. Tumor progression–targets for differential therapy . J. Cell Physiol. 209 (2006), No. 3, 589-91.CrossRefGoogle ScholarPubMed
Roeder, I., Horn, M., Glauche, I., Hochlaus, A., Mueller, M. C. Loeffler, M.. Dynamic modeling of imatinib-treated chronic myeloid leukemia: functional insights and clinical implications . Nature Medicine 12 (2006), 1181-1184.CrossRefGoogle ScholarPubMed
Schatton, T., Frank, N.Y., Frank, M.H. Identification and targeting of cancer stem cells . Bioessays 31 (2009) No. 10, 1038-49. CrossRefGoogle ScholarPubMed
Thomas, R.. Laws for the dynamics of regulatory networks . Int. J. Dev. Biol. 42 (1998), No. 3, 479-85.Google ScholarPubMed
Tindall, M.J.. Modelling the cell cycle and cell movement in multicellular tumour spheroids . Bull. Math. Biol. 69 (2007), No. 4, 1147-65.CrossRefGoogle ScholarPubMed
Lo, W. C., Chou, C. S., Gokoffski, K. K., Wan, F. Y., Lander, A.D., Calof, A. L. Nie, Q.. Feedback regulation in multistage cell lineages . Math. Biosci. Eng. 6 (2009), No. 1, 59-82.CrossRefGoogle ScholarPubMed