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Published in: Current Breast Cancer Reports 3/2010

Open Access 01-09-2010

Mathematical Oncology: How Are the Mathematical and Physical Sciences Contributing to the War on Breast Cancer?

Authors: Arnaud H. Chauviere, Haralampos Hatzikirou, John S. Lowengrub, Hermann B. Frieboes, Alastair M. Thompson, Vittorio Cristini

Published in: Current Breast Cancer Reports | Issue 3/2010

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Abstract

Mathematical modeling has recently been added as a tool in the fight against cancer. The field of mathematical oncology has received great attention and increased enormously, but over-optimistic estimations about its ability have created unrealistic expectations. We present a critical appraisal of the current state of mathematical models of cancer. Although the field is still expanding and useful clinical applications may occur in the future, managing over-expectation requires the proposal of alternative directions for mathematical modeling. Here, we propose two main avenues for this modeling: 1) the identification of the elementary biophysical laws of cancer development, and 2) the development of a multiscale mathematical theory as the framework for models predictive of tumor growth. Finally, we suggest how these new directions could contribute to addressing the current challenges of understanding breast cancer growth and metastasis.
Literature
2.
go back to reference Harris L, Fritsche H, Mennel R, et al.: American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 2007, 25:5287–5312.CrossRefPubMed Harris L, Fritsche H, Mennel R, et al.: American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 2007, 25:5287–5312.CrossRefPubMed
3.
go back to reference Al-Hajj M, Wicha MS, Benito-Hernandez A, et al.: Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci 2003, 100:3983–3988.CrossRefPubMed Al-Hajj M, Wicha MS, Benito-Hernandez A, et al.: Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci 2003, 100:3983–3988.CrossRefPubMed
4.
go back to reference Weigelt B, Bissel MJ: Unraveling the microenvironmental influences on the normal mammary gland and breast cancer. Semin Cancer Biol 2008, 18:311–321.CrossRefPubMed Weigelt B, Bissel MJ: Unraveling the microenvironmental influences on the normal mammary gland and breast cancer. Semin Cancer Biol 2008, 18:311–321.CrossRefPubMed
6.
go back to reference Kim M-Y, Oskarsson T, Acharyya S, et al.: Tumor self-seeding by circulating cancer cells. Cell 2009, 139:1315–1326.CrossRefPubMed Kim M-Y, Oskarsson T, Acharyya S, et al.: Tumor self-seeding by circulating cancer cells. Cell 2009, 139:1315–1326.CrossRefPubMed
7.
go back to reference Thompson A, Brennan K, Cox A, et al.: Evaluation of the current knowledge limitations in breast cancer research: a gap analysis. Breast Cancer Res 2008 10:R26.CrossRefPubMed Thompson A, Brennan K, Cox A, et al.: Evaluation of the current knowledge limitations in breast cancer research: a gap analysis. Breast Cancer Res 2008 10:R26.CrossRefPubMed
8.
go back to reference Gatenby RA, Gawlinski ET: The glycolytic phenotype in carcinogenesis and tumor invasion: insights through mathematical models. Cancer Res 2003, 63:3847–3854.PubMed Gatenby RA, Gawlinski ET: The glycolytic phenotype in carcinogenesis and tumor invasion: insights through mathematical models. Cancer Res 2003, 63:3847–3854.PubMed
9.
go back to reference Konukoglu E, Clatz O, Bondiau P-Y, et al.: Extrapolating glioma invasion margin in brain magnetic resonance images: suggesting new irradiation margins. Med Image Anal 2010, 14:111–125.CrossRefPubMed Konukoglu E, Clatz O, Bondiau P-Y, et al.: Extrapolating glioma invasion margin in brain magnetic resonance images: suggesting new irradiation margins. Med Image Anal 2010, 14:111–125.CrossRefPubMed
10.
go back to reference Pathmanathan P, Gavaghan DJ, Whiteley JP, et al.: Predicting tumor location by modeling the deformation of the breast. IEEE Trans Biomed Eng 2008, 55:2471–2480.CrossRefPubMed Pathmanathan P, Gavaghan DJ, Whiteley JP, et al.: Predicting tumor location by modeling the deformation of the breast. IEEE Trans Biomed Eng 2008, 55:2471–2480.CrossRefPubMed
11.
go back to reference Tracqui P: Biophysical models of tumour growth. Rep Prog Physics 2009, 72:056701.CrossRef Tracqui P: Biophysical models of tumour growth. Rep Prog Physics 2009, 72:056701.CrossRef
12.
go back to reference Byrne H: Dissecting cancer through mathematics: from the cell to the animal model. Nat Rev Cancer 2010, 10:221–230.CrossRefPubMed Byrne H: Dissecting cancer through mathematics: from the cell to the animal model. Nat Rev Cancer 2010, 10:221–230.CrossRefPubMed
13.
go back to reference • Lowengrub JS, Frieboes HB, Jin F, et al.: Nonlinear modelling of cancer: bridging the gap between cells and tumours. NonLinearity 2010, 23:R1–R91. This article provides an overview of multiscale cancer modeling. In particular, hybrid modeling is presented, in which the tumor tissue is modeled using both discrete (cell-scale) and continuum (tumor-scale) elements, thus connecting the micrometer scale to the centimeter scale. A review of state-of-the-art of mathematical models of cancer is presented.CrossRef • Lowengrub JS, Frieboes HB, Jin F, et al.: Nonlinear modelling of cancer: bridging the gap between cells and tumours. NonLinearity 2010, 23:R1–R91. This article provides an overview of multiscale cancer modeling. In particular, hybrid modeling is presented, in which the tumor tissue is modeled using both discrete (cell-scale) and continuum (tumor-scale) elements, thus connecting the micrometer scale to the centimeter scale. A review of state-of-the-art of mathematical models of cancer is presented.CrossRef
14.
go back to reference Gatenby RA, Gawlinski ET, Gmitro AF, et al.: Acid-mediated tumor invasion: a multidisciplinary study. Cancer Res 2006, 66:5216–5223.CrossRefPubMed Gatenby RA, Gawlinski ET, Gmitro AF, et al.: Acid-mediated tumor invasion: a multidisciplinary study. Cancer Res 2006, 66:5216–5223.CrossRefPubMed
15.
go back to reference Frieboes HB, Jin F, Chuang Y-L, et al.: Three dimensional multispecies nonlinear tumor growth II: tumor invasion and angiogenesis. J Theor Biol 2010, 264:1254–1278. Frieboes HB, Jin F, Chuang Y-L, et al.: Three dimensional multispecies nonlinear tumor growth II: tumor invasion and angiogenesis. J Theor Biol 2010, 264:1254–1278.
16.
go back to reference Byrne HM, Chaplain MA: Growth of nonnecrotic tumors in the presence and absence of inhibitors. Math Biosci 1995, 130:151–181.CrossRefPubMed Byrne HM, Chaplain MA: Growth of nonnecrotic tumors in the presence and absence of inhibitors. Math Biosci 1995, 130:151–181.CrossRefPubMed
17.
go back to reference Adam JA: A simplified mathematical model of tumor growth. Math Biosci 1986, 81:229–244.CrossRef Adam JA: A simplified mathematical model of tumor growth. Math Biosci 1986, 81:229–244.CrossRef
18.
go back to reference Zheng X, Wise SM, Cristini V: Nonlinear simulation of tumor necrosis, neo-vascularization and tissue invasion via an adaptive finite-element/level-set method. Bull Math Biol 2005, 67:211–259.CrossRefPubMed Zheng X, Wise SM, Cristini V: Nonlinear simulation of tumor necrosis, neo-vascularization and tissue invasion via an adaptive finite-element/level-set method. Bull Math Biol 2005, 67:211–259.CrossRefPubMed
19.
go back to reference Cristini V, Frieboes HB, Gatenby R, et al.: Morphologic instability and cancer invasion. Clin Cancer Research 2005, 11:6772–6779.CrossRefPubMed Cristini V, Frieboes HB, Gatenby R, et al.: Morphologic instability and cancer invasion. Clin Cancer Research 2005, 11:6772–6779.CrossRefPubMed
20.
go back to reference Welter M, Bartha K, Rieger H: Emergent vascular network inhomogeneities and resulting blood flow patterns in a growing tumor. J Theor Biol 2008, 250:257–280.CrossRefPubMed Welter M, Bartha K, Rieger H: Emergent vascular network inhomogeneities and resulting blood flow patterns in a growing tumor. J Theor Biol 2008, 250:257–280.CrossRefPubMed
21.
go back to reference Sinek JP, Sanga S, Zheng X, et al.: Predicting drug pharmacokinetics and effect in vascularized tumors using computer simulation. J Math Biol 2009, 58:485–510.CrossRefPubMed Sinek JP, Sanga S, Zheng X, et al.: Predicting drug pharmacokinetics and effect in vascularized tumors using computer simulation. J Math Biol 2009, 58:485–510.CrossRefPubMed
22.
go back to reference Frieboes H, Edgerton ME, Fruehauf JP, et al.: Prediction of drug response in breast cancer using integrative experimental/computational modeling. Cancer Research 2009, 69:4484–4492.CrossRefPubMed Frieboes H, Edgerton ME, Fruehauf JP, et al.: Prediction of drug response in breast cancer using integrative experimental/computational modeling. Cancer Research 2009, 69:4484–4492.CrossRefPubMed
23.
go back to reference Swanson KR, Alvord Jr EC, Murray J: A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif 2000, 33:317–329.CrossRefPubMed Swanson KR, Alvord Jr EC, Murray J: A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif 2000, 33:317–329.CrossRefPubMed
24.
go back to reference Szeto MD, Chakraborty G, Hadley J, et al.: Quantitative metrics of net proliferation and invasion link biological aggressiveness assessed by MRI with hypoxia assessed by FMISO-PET in newly diagnosed glioblastomas. Cancer Res 2009, 69:4502–4509.CrossRefPubMed Szeto MD, Chakraborty G, Hadley J, et al.: Quantitative metrics of net proliferation and invasion link biological aggressiveness assessed by MRI with hypoxia assessed by FMISO-PET in newly diagnosed glioblastomas. Cancer Res 2009, 69:4502–4509.CrossRefPubMed
25.
go back to reference Macklin P, Edgerton ME, Cristini V: Agent-based cell modeling: application to breast cancer. In: Cristini V and Lowengrub J, Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach. New York: Springer; 2010:215–244. Macklin P, Edgerton ME, Cristini V: Agent-based cell modeling: application to breast cancer. In: Cristini V and Lowengrub J, Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach. New York: Springer; 2010:215–244.
27.
go back to reference Hatzikirou H, Basanta D, Simon M, et al.: “Go or grow”: the key to the emergence of invasion in tumor progression? Math Med Biol 2010, published online, doi:10.1093/imammb/dqq011. Hatzikirou H, Basanta D, Simon M, et al.: “Go or grow”: the key to the emergence of invasion in tumor progression? Math Med Biol 2010, published online, doi:10.​1093/​imammb/​dqq011.
28.
go back to reference Anderson AR, Weaver AM, Cummings PT, et al.: Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 2006, 127:905–915.CrossRefPubMed Anderson AR, Weaver AM, Cummings PT, et al.: Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 2006, 127:905–915.CrossRefPubMed
29.
go back to reference • Gatenby RA, Gillies RJ: A microenvironmental model of carcinogenesis. Nat Rev Cancer 2008, 8:56–61. This article provides a mathematical model in which the diverse cancer genotypes and phenotypes can be understood according to their roles as adaptive strategies to overcome specific microenvironmental growth constraints.CrossRefPubMed • Gatenby RA, Gillies RJ: A microenvironmental model of carcinogenesis. Nat Rev Cancer 2008, 8:56–61. This article provides a mathematical model in which the diverse cancer genotypes and phenotypes can be understood according to their roles as adaptive strategies to overcome specific microenvironmental growth constraints.CrossRefPubMed
30.
go back to reference Bru A, Albertos S, Luis Subiza J, et al.: The universal dynamics of tumor growth. Biophys J 2003, 85:2948–2961. CrossRefPubMed Bru A, Albertos S, Luis Subiza J, et al.: The universal dynamics of tumor growth. Biophys J 2003, 85:2948–2961. CrossRefPubMed
31.
go back to reference Cristini V, Lowengrub J, Nie Q: Nonlinear simulation of tumor growth. J Math Biol 2003, 46:191–224.CrossRefPubMed Cristini V, Lowengrub J, Nie Q: Nonlinear simulation of tumor growth. J Math Biol 2003, 46:191–224.CrossRefPubMed
32.
go back to reference Bearer EL, Lowengrub JS, Frieboes HB, et al.: Multiparameter computational modeling of tumor invasion. Cancer Res 2009, 69:4493–4501.CrossRefPubMed Bearer EL, Lowengrub JS, Frieboes HB, et al.: Multiparameter computational modeling of tumor invasion. Cancer Res 2009, 69:4493–4501.CrossRefPubMed
33.
go back to reference van Leeuwen IM, Edwards CM, Ilyas M, et al.: Towards a multiscale model of colorectal cancer. World J Gastroentero 2007, 13:1399–1407. van Leeuwen IM, Edwards CM, Ilyas M, et al.: Towards a multiscale model of colorectal cancer. World J Gastroentero 2007, 13:1399–1407.
34.
go back to reference Enderling H, Anderson AR, Chaplain MA, et al.: Paradoxical dependencies of tumor dormancy and progression on basic cell kinetics. Cancer Res 2009, 69:8814–8821.CrossRefPubMed Enderling H, Anderson AR, Chaplain MA, et al.: Paradoxical dependencies of tumor dormancy and progression on basic cell kinetics. Cancer Res 2009, 69:8814–8821.CrossRefPubMed
35.
go back to reference Galle J, Hoffmann M, Aust G: From single cells to tissue architecture—a bottom-up approach to modelling the spatio-temporal organisation of complex multi-cellular systems. J Math Biol 2009, 58:261–283.CrossRefPubMed Galle J, Hoffmann M, Aust G: From single cells to tissue architecture—a bottom-up approach to modelling the spatio-temporal organisation of complex multi-cellular systems. J Math Biol 2009, 58:261–283.CrossRefPubMed
36.
go back to reference Sottoriva A, Verhoeff JJC, Borovski T, et al.: Cancer stem cell tumor model reveals invasive morphology and increased phenotypical heterogeneity. Cancer Res 2010, 70:46–56.CrossRefPubMed Sottoriva A, Verhoeff JJC, Borovski T, et al.: Cancer stem cell tumor model reveals invasive morphology and increased phenotypical heterogeneity. Cancer Res 2010, 70:46–56.CrossRefPubMed
37.
go back to reference Kim Y, Stolarska MA, Othmer HG: A hybrid model for tumor spheroid growth in vitro i: theoretical development and early results. Math Mod Meth App Sci 2007, 17:1773–1798.CrossRef Kim Y, Stolarska MA, Othmer HG: A hybrid model for tumor spheroid growth in vitro i: theoretical development and early results. Math Mod Meth App Sci 2007, 17:1773–1798.CrossRef
38.
go back to reference Ramis-Conde I, Drasdo D, Anderson AR, et al.: Modeling the influence of the E-cadherin-beta-catenin pathway in cancer cell invasion: a multiscale approach. Biophys J 2008, 95:155–165.CrossRefPubMed Ramis-Conde I, Drasdo D, Anderson AR, et al.: Modeling the influence of the E-cadherin-beta-catenin pathway in cancer cell invasion: a multiscale approach. Biophys J 2008, 95:155–165.CrossRefPubMed
39.
go back to reference Norton KA, Wininger M, Bhanot G, et al.: A 2D mechanistic model of breast ductal carcinoma in situ (DCIS) morphology and progression. J Theor Biol 2010, 263:393–406.CrossRefPubMed Norton KA, Wininger M, Bhanot G, et al.: A 2D mechanistic model of breast ductal carcinoma in situ (DCIS) morphology and progression. J Theor Biol 2010, 263:393–406.CrossRefPubMed
40.
go back to reference Tektonidis M, Hatzikirou H, Chauviere A, et al.: Identification of intrinsic mechanisms for glioma invasion. PLoS Comp Biol 2010 (in press). Tektonidis M, Hatzikirou H, Chauviere A, et al.: Identification of intrinsic mechanisms for glioma invasion. PLoS Comp Biol 2010 (in press).
41.
go back to reference Preziosi L, Tosin A: Multiphase modelling of tumour growth and extracellular matrix interaction: mathematical tools and applications. J Math Biol 2009, 58:625–656.CrossRefPubMed Preziosi L, Tosin A: Multiphase modelling of tumour growth and extracellular matrix interaction: mathematical tools and applications. J Math Biol 2009, 58:625–656.CrossRefPubMed
42.
go back to reference Multiscale Cancer Modeling. Edited by Deisboeck TS, Stamatakos GS. Boca Raton, FL: Chapman & Hall/CRC; 2010. Multiscale Cancer Modeling. Edited by Deisboeck TS, Stamatakos GS. Boca Raton, FL: Chapman & Hall/CRC; 2010.
43.
go back to reference • Kevrekidis IG, Samaey G: Equation-free multiscale computation: algorithms and applications. Annu Rev Phys Chem 2009, 60:321–344. This article introduces a multiscale framework, derived in the context of physical sciences, that enables numerical simulations of mathematical models (applicable to cancer modeling) over extended spatio-temporal scales.CrossRefPubMed • Kevrekidis IG, Samaey G: Equation-free multiscale computation: algorithms and applications. Annu Rev Phys Chem 2009, 60:321–344. This article introduces a multiscale framework, derived in the context of physical sciences, that enables numerical simulations of mathematical models (applicable to cancer modeling) over extended spatio-temporal scales.CrossRefPubMed
44.
go back to reference Weinan E, Engquist B, Xiantao Li, et al.: Heterogeneous multiscale methods: a review. Commun Comput Physics 2007, 2:367–450. Weinan E, Engquist B, Xiantao Li, et al.: Heterogeneous multiscale methods: a review. Commun Comput Physics 2007, 2:367–450.
45.
go back to reference Frieboes HB, Lowengrub JS, Wise S, et al.: Computer simulation of glioma growth and morphology. Neuroimage 2007, 37:S59–S70.CrossRefPubMed Frieboes HB, Lowengrub JS, Wise S, et al.: Computer simulation of glioma growth and morphology. Neuroimage 2007, 37:S59–S70.CrossRefPubMed
Metadata
Title
Mathematical Oncology: How Are the Mathematical and Physical Sciences Contributing to the War on Breast Cancer?
Authors
Arnaud H. Chauviere
Haralampos Hatzikirou
John S. Lowengrub
Hermann B. Frieboes
Alastair M. Thompson
Vittorio Cristini
Publication date
01-09-2010
Publisher
Current Science Inc.
Published in
Current Breast Cancer Reports / Issue 3/2010
Print ISSN: 1943-4588
Electronic ISSN: 1943-4596
DOI
https://doi.org/10.1007/s12609-010-0020-6

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