The mathematics of cancer: integrating quantitative models

Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.

[1]  Paula D. Bos,et al.  Metastasis: from dissemination to organ-specific colonization , 2009, Nature Reviews Cancer.

[2]  Goldie Jh,et al.  A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. , 1979 .

[3]  E. T. Gawlinski,et al.  A Cellular Automaton Model of Early Tumor Growth and Invasion: The Effects of Native Tissue Vascularity and Increased Anaerobic Tumor Metabolism , 2001 .

[4]  L. D. Pillis,et al.  A Validated Mathematical Model of Cell-Mediated Immune Response to Tumor Growth , 2005 .

[5]  C. Nordling A New Theory on the Cancer-inducing Mechanism , 1953, British Journal of Cancer.

[6]  J. H. Hollomon,et al.  A hypothesis for the origin of cancer foci , 1951, Cancer.

[7]  Camille Stephan-Otto Attolini,et al.  A mathematical framework to determine the temporal sequence of somatic genetic events in cancer , 2010, Proceedings of the National Academy of Sciences.

[8]  N. Socci,et al.  Optimization of Dosing for EGFR-Mutant Non–Small Cell Lung Cancer with Evolutionary Cancer Modeling , 2011, Science Translational Medicine.

[9]  A. McKenna,et al.  The Mutational Landscape of Head and Neck Squamous Cell Carcinoma , 2011, Science.

[10]  A. Zauber,et al.  Cancer: Risk factors and random chances , 2015, Nature.

[11]  Martin A. Nowak,et al.  A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity , 2015, Nature.

[12]  L. Norton,et al.  Growth curve of an experimental solid tumor following radiotherapy. , 1977, Journal of the National Cancer Institute.

[13]  P. Carmeliet,et al.  Angiogenesis in cancer and other diseases , 2000, Nature.

[14]  A. Anderson,et al.  Hybrid models of tumor growth , 2011, Wiley interdisciplinary reviews. Systems biology and medicine.

[15]  Larry Norton,et al.  Tumor Self-Seeding by Circulating Cancer Cells , 2009, Cell.

[16]  Lisette G de Pillis,et al.  A validated mathematical model of cell-mediated immune response to tumor growth. , 2007, Cancer research.

[17]  R. Weinberg,et al.  The Biology of Cancer , 2006 .

[18]  Tom Lenaerts,et al.  Dynamics of Mutant Cells in Hierarchical Organized Tissues , 2011, PLoS Comput. Biol..

[19]  Mauro Ferrari,et al.  Mathematical modeling of cancer progression and response to chemotherapy , 2006, Expert review of anticancer therapy.

[20]  L. Preziosi,et al.  Modelling and mathematical problems related to tumor evolution and its interaction with the immune system , 2000 .

[21]  L. Old Cancer immunology: the search for specificity--G. H. A. Clowes Memorial lecture. , 1981, Cancer research.

[22]  Alissa M. Weaver,et al.  Tumor Morphology and Phenotypic Evolution Driven by Selective Pressure from the Microenvironment , 2006, Cell.

[23]  Marek Kimmel,et al.  Branching processes in biology , 2002 .

[24]  E. T. Gawlinski,et al.  A cellular automaton model of early tumor growth and invasion. , 2001, Journal of theoretical biology.

[25]  M. Spitz,et al.  A smoking‐based carcinogenesis model for lung cancer risk prediction , 2011, International journal of cancer.

[26]  Arne Traulsen,et al.  Stochastic game dynamics under demographic fluctuations , 2015, Proceedings of the National Academy of Sciences.

[27]  Steven A Frank,et al.  Somatic mosaicism and cancer: inference based on a conditional Luria-Delbrück distribution. , 2003, Journal of theoretical biology.

[28]  Richard Durrett,et al.  Evolution of Resistance and Progression to Disease during Clonal Expansion of Cancer , 2009 .

[29]  Franziska Michor,et al.  The evolution of tumor metastases during clonal expansion. , 2010, Journal of theoretical biology.

[30]  Robert Veltri,et al.  Critical transitions in a game theoretic model of tumour metabolism , 2014, Interface Focus.

[31]  Otto Warburn,et al.  THE METABOLISM OF TUMORS , 1931 .

[32]  Martin A. Nowak,et al.  Spatial Heterogeneity in Drug Concentrations Can Facilitate the Emergence of Resistance to Cancer Therapy , 2014, PLoS Comput. Biol..

[33]  M. Gönen,et al.  Evolutionary pathways in BRCA1-associated breast tumors. , 2012, Cancer discovery.

[34]  L. Weiss Comments on hematogenous metastatic patterns in humans as revealed by autopsy , 1992, Clinical & Experimental Metastasis.

[35]  Mauro Ferrari,et al.  Prediction of drug response in breast cancer using integrative experimental/computational modeling. , 2009, Cancer research.

[36]  H M Byrne,et al.  Growth of necrotic tumors in the presence and absence of inhibitors. , 1996, Mathematical biosciences.

[37]  Kristin R. Swanson,et al.  Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma , 2013, PloS one.

[38]  K. Schaller,et al.  'Go or grow': the key to the emergence of invasion in tumour progression? , 2012, Mathematical medicine and biology : a journal of the IMA.

[39]  N. Komarova,et al.  Stochastic modeling of drug resistance in cancer. , 2006, Journal of theoretical biology.

[40]  P. A. P. Moran,et al.  Random processes in genetics , 1958, Mathematical Proceedings of the Cambridge Philosophical Society.

[41]  G. Bonadonna,et al.  Clinical relevance of different sequencing of doxorubicin and cyclophosphamide, methotrexate, and Fluorouracil in operable breast cancer. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[42]  M. Nowak,et al.  Oncogenes, anti-oncogenes and the immune response to cancer : a mathematical model , 1992, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[43]  C. Greenman Estimation of Rearrangement Phylogeny in Cancer , 2012 .

[44]  Franziska Michor,et al.  Evolution of Resistance to Targeted Anti-Cancer Therapies during Continuous and Pulsed Administration Strategies , 2009, PLoS Comput. Biol..

[45]  Emanuel Parzen,et al.  Stochastic Processes , 1962 .

[46]  A. Schäffer,et al.  Construction of tree models for pathogenesis of nasopharyngeal carcinoma , 2004, Genes, chromosomes & cancer.

[47]  Nordling Co A New Theory on the Cancer-inducing Mechanism , 1953 .

[48]  Avner Friedman,et al.  Mathematical modeling of prostate cancer progression in response to androgen ablation therapy , 2011, Proceedings of the National Academy of Sciences.

[49]  M. Chaplain,et al.  Continuous and discrete mathematical models of tumor-induced angiogenesis , 1998, Bulletin of mathematical biology.

[50]  Benjamin J. Raphael,et al.  Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. , 2013, The New England journal of medicine.

[51]  Martin A. Nowak,et al.  Genetic Progression and the Waiting Time to Cancer , 2007, PLoS Comput. Biol..

[52]  H. Frieboes,et al.  Three-dimensional multispecies nonlinear tumor growth--I Model and numerical method. , 2008, Journal of theoretical biology.

[53]  B. Vogelstein,et al.  Variation in cancer risk among tissues can be explained by the number of stem cell divisions , 2015, Science.

[54]  Gary D Bader,et al.  International network of cancer genome projects , 2010, Nature.

[55]  E. Lander,et al.  Lessons from the Cancer Genome , 2013, Cell.

[56]  Feng Jiang,et al.  Inferring Tree Models for Oncogenesis from Comparative Genome Hybridization Data , 1999, J. Comput. Biol..

[57]  Ricky T. Tong,et al.  Effect of vascular normalization by antiangiogenic therapy on interstitial hypertension, peritumor edema, and lymphatic metastasis: insights from a mathematical model. , 2007, Cancer research.

[58]  Krishnendu Chatterjee,et al.  Evolutionary dynamics of cancer in response to targeted combination therapy , 2013, eLife.

[59]  J. Potter,et al.  Cancer risk: Tumors excluded , 2015, Science.

[60]  N. Carter,et al.  Estimation of rearrangement phylogeny for cancer genomes. , 2012, Genome research.

[61]  L. Old Cancer immunology: the search for specificity. , 1982, National Cancer Institute monograph.

[62]  V. Kuznetsov Basic Models of Tumor-Immune System Interactions Identification, Analysis and Predictions , 1997 .

[63]  B. Vogelstein,et al.  A genetic model for colorectal tumorigenesis , 1990, Cell.

[64]  M. Archetti,et al.  Reply to Gerlee and Altrock: Diffusion and population size in game theory models of cancer , 2015, Proceedings of the National Academy of Sciences.

[65]  C. Curtis,et al.  A Big Bang model of human colorectal tumor growth , 2015, Nature Genetics.

[66]  Drew M. Pardoll,et al.  The blockade of immune checkpoints in cancer immunotherapy , 2012, Nature Reviews Cancer.

[67]  Steven A Frank,et al.  Stochastic elimination of cancer cells , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[68]  Lurias,et al.  MUTATIONS OF BACTERIA FROM VIRUS SENSITIVITY TO VIRUS RESISTANCE’-’ , 2003 .

[69]  Vittorio Cristini,et al.  Three-dimensional multispecies nonlinear tumor growth-II: Tumor invasion and angiogenesis. , 2010, Journal of theoretical biology.

[70]  A. Knudson Mutation and cancer: statistical study of retinoblastoma. , 1971, Proceedings of the National Academy of Sciences of the United States of America.

[71]  Alexander van Oudenaarden,et al.  Genetic and phenotypic diversity in breast tumor metastases. , 2014, Cancer research.

[72]  Thomas E Yankeelov,et al.  Clinically Relevant Modeling of Tumor Growth and Treatment Response , 2013, Science Translational Medicine.

[73]  O. Warburg,et al.  THE METABOLISM OF TUMORS IN THE BODY , 1927, The Journal of general physiology.

[74]  Helen M. Byrne,et al.  A Multiple Scale Model for Tumor Growth , 2005, Multiscale Model. Simul..

[75]  T. Brümmendorf,et al.  Dynamics of Resistance Development to Imatinib under Increasing Selection Pressure: A Combination of Mathematical Models and In Vitro Data , 2011, PloS one.

[76]  Elise C. Kohn,et al.  The microenvironment of the tumour–host interface , 2001, Nature.

[77]  K Hendrickson,et al.  Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach , 2010, Physics in medicine and biology.

[78]  Ami Radunskaya,et al.  A mathematical tumor model with immune resistance and drug therapy: an optimal control approach , 2001 .

[79]  S. McDougall,et al.  Multiscale modelling and nonlinear simulation of vascular tumour growth , 2009, Journal of mathematical biology.

[80]  Linda Rass,et al.  Branching Processes in Biology.Interdisciplinary Applied Mathematics, Volume 19.ByMarek Kimmeland, David E Axelrod.New York: Springer.$59.95. xviii + 230 p; ill.; index. ISBN: 0–387–95340‐X. 2002. , 2003 .

[81]  Goldie Jh,et al.  Quantitative model for multiple levels of drug resistance in clinical tumors. , 1983 .

[82]  S. Rosenberg,et al.  Cancer immunotherapy: moving beyond current vaccines , 2004, Nature Medicine.

[83]  Dominik Wodarz,et al.  Evolution of ibrutinib resistance in chronic lymphocytic leukemia (CLL) , 2014, Proceedings of the National Academy of Sciences.

[84]  L. Norton,et al.  Self-seeding in cancer. , 2012, Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer.

[85]  N. Navin,et al.  Clonal Evolution in Breast Cancer Revealed by Single Nucleus Genome Sequencing , 2014, Nature.

[86]  G M Saidel,et al.  System dynamics of metastatic process from an implanted tumor. , 1976, Journal of theoretical biology.

[87]  E. Holland,et al.  The Probable Cell of Origin of NF1- and PDGF-Driven Glioblastomas , 2011, PloS one.

[88]  B. Vogelstein,et al.  Cancer risk: Role of environment—Response , 2015, Science.

[89]  Sanjay Kumar,et al.  Independent regulation of tumor cell migration by matrix stiffness and confinement , 2012, Proceedings of the National Academy of Sciences.

[90]  W. Pao,et al.  Effects of Pharmacokinetic Processes and Varied Dosing Schedules on the Dynamics of Acquired Resistance to Erlotinib in EGFR-Mutant Lung Cancer , 2012, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[91]  M. Archetti,et al.  Evolutionary game theory of growth factor production: implications for tumour heterogeneity and resistance to therapies , 2013, British Journal of Cancer.

[92]  M. Chaplain,et al.  A new mathematical model for avascular tumour growth , 2001, Journal of mathematical biology.

[93]  E. T. Gawlinski,et al.  A reaction-diffusion model of cancer invasion. , 1996, Cancer research.

[94]  Niko Beerenwinkel,et al.  Quantifying cancer progression with conjunctive Bayesian networks , 2009, Bioinform..

[95]  A. d’Onofrio A general framework for modeling tumor-immune system competition and immunotherapy: Mathematical analysis and biomedical inferences , 2005, 1309.3337.

[96]  J. Goldie,et al.  Quantitative model for multiple levels of drug resistance in clinical tumors. , 1983, Cancer treatment reports.

[97]  Alberto d'Onofrio,et al.  Metamodeling tumor-immune system interaction, tumor evasion and immunotherapy , 2008, Math. Comput. Model..

[98]  Mattias Höglund,et al.  Statistical behavior of complex cancer karyotypes , 2005, Genes, chromosomes & cancer.

[99]  J H Goldie,et al.  The genetic origin of drug resistance in neoplasms: implications for systemic therapy. , 1984, Cancer research.

[100]  A. Traulsen,et al.  Tyrosine kinase inhibitor therapy can cure chronic myeloid leukemia without hitting leukemic stem cells , 2009, Haematologica.

[101]  Arne Traulsen,et al.  Cancer initiation with epistatic interactions between driver and passenger mutations. , 2013, Journal of theoretical biology.

[102]  Zvia Agur,et al.  Cancer immunotherapy by interleukin-21: potential treatment strategies evaluated in a mathematical model. , 2006, Cancer research.

[103]  Carissa A. Sanchez,et al.  Temporal and Spatial Evolution of Somatic Chromosomal Alterations: A Case-Cohort Study of Barrett's Esophagus , 2013, Cancer Prevention Research.

[104]  David L. Porter,et al.  T Cells with Chimeric Antigen Receptors Have Potent Antitumor Effects and Can Establish Memory in Patients with Advanced Leukemia , 2011, Science Translational Medicine.

[105]  Thomas Lengauer,et al.  Learning multiple evolutionary pathways from cross-sectional data , 2004, J. Comput. Biol..

[106]  A. Schäffer,et al.  Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis , 2000, Genes, chromosomes & cancer.

[107]  R. Rami-Porta,et al.  Surgical treatment of lung cancer with adrenal metastasis. , 2000, Lung cancer.

[108]  M. Stratton,et al.  The cancer genome , 2009, Nature.

[109]  David Basanta,et al.  An integrated computational model of the bone microenvironment in bone-metastatic prostate cancer. , 2014, Cancer research.

[110]  Nicola Bellomo,et al.  A Survey of Models for Tumor-Immune System Dynamics , 1996 .

[111]  M. O'Callaghan,et al.  Cancer risk: Accuracy of literature , 2015, Science.

[112]  Arne Traulsen,et al.  Emergence of stable polymorphisms driven by evolutionary games between mutants , 2012, Nature Communications.

[113]  C. Clegg,et al.  Interleukin‐21 is a T‐helper cytokine that regulates humoral immunity and cell‐mediated anti‐tumour responses , 2004, Immunology.

[114]  Jacob G. Scott,et al.  Investigating prostate cancer tumour–stroma interactions: clinical and biological insights from an evolutionary game , 2011, British Journal of Cancer.

[115]  Mingming Jia,et al.  COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer , 2010, Nucleic Acids Res..

[116]  C. Maley,et al.  Accurate Reconstruction of the Temporal Order of Mutations in Neoplastic Progression , 2011, Cancer Prevention Research.

[117]  W. Pao,et al.  Evolutionary Modeling of Combination Treatment Strategies To Overcome Resistance to Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancer , 2011, Molecular pharmaceutics.

[118]  Jens Lagergren,et al.  New Probabilistic Network Models and Algorithms for Oncogenesis , 2006, J. Comput. Biol..

[119]  M. Archetti,et al.  Heterogeneity for IGF-II production maintained by public goods dynamics in neuroendocrine pancreatic cancer , 2015, Proceedings of the National Academy of Sciences.

[120]  N. Fusenig,et al.  Friends or foes — bipolar effects of the tumour stroma in cancer , 2004, Nature Reviews Cancer.

[121]  P. Krapivsky,et al.  Exact solution of a two-type branching process: models of tumor progression , 2011, 1105.1157.

[122]  Complexity and stability in growing cancer cell populations , 2015, Proceedings of the National Academy of Sciences.

[123]  Martin A. Nowak,et al.  Dynamics of chronic myeloid leukaemia , 2005, Nature.

[124]  Jacob G. Scott,et al.  Unifying metastasis — integrating intravasation, circulation and end-organ colonization , 2012, Nature Reviews Cancer.

[125]  Dominik Wodarz,et al.  Drug resistance in cancer: principles of emergence and prevention. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[126]  Kristin R. Swanson,et al.  The Evolution of Mathematical Modeling of Glioma Proliferation and Invasion , 2007, Journal of neuropathology and experimental neurology.

[127]  P. Armitage,et al.  A Two-stage Theory of Carcinogenesis in Relation to the Age Distribution of Human Cancer , 1957, British Journal of Cancer.

[128]  I. Fidler,et al.  The pathogenesis of cancer metastasis: the 'seed and soil' hypothesis revisited , 2003, Nature Reviews Cancer.

[129]  Johannes G. Reiter,et al.  The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers , 2012, Nature.

[130]  D. Hattis,et al.  Cancer risk: Role of environment , 2015, Science.

[131]  Albert Lai,et al.  Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model. , 2009, Cancer research.

[132]  Barbara L. Smith,et al.  Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[133]  J. Blattman,et al.  Cancer Immunotherapy: A Treatment for the Masses , 2004, Science.

[134]  A. Traulsen,et al.  Deterministic evolutionary game dynamics in finite populations. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[135]  Benjamin J. Raphael,et al.  Integrated Genomic Analyses of Ovarian Carcinoma , 2011, Nature.

[136]  Leonid A. Mirny,et al.  Tug-of-war between driver and passenger mutations in cancer and other adaptive processes , 2014, Proceedings of the National Academy of Sciences.

[137]  Alan C. Evans,et al.  BrainWeb: Online Interface to a 3D MRI Simulated Brain Database , 1997 .

[138]  David Basanta,et al.  SP-0013: A mathematical model of tumor self-seeding reveals secondary metastatic deposits as drivers of primary tumor growth , 2013 .

[139]  J H Goldie,et al.  A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. , 1979, Cancer treatment reports.

[140]  Francesco Pappalardo,et al.  SimB16: Modeling Induced Immune System Response against B16-Melanoma , 2011, PloS one.

[141]  Paul T. Spellman,et al.  Methods and challenges in timing chromosomal abnormalities within cancer samples , 2013, Bioinform..

[142]  H. Othmer,et al.  A HYBRID MODEL FOR TUMOR SPHEROID GROWTH IN VITRO I: THEORETICAL DEVELOPMENT AND EARLY RESULTS , 2007 .

[143]  Yoh Iwasa,et al.  An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer , 2015, PLoS Comput. Biol..

[144]  R. Levine,et al.  A progenitor cell origin of myeloid malignancies , 2009, Proceedings of the National Academy of Sciences.

[145]  V. Quaranta,et al.  Integrative mathematical oncology , 2008, Nature Reviews Cancer.

[146]  Martin A. Nowak,et al.  Comparative lesion sequencing provides insights into tumor evolution , 2008, Proceedings of the National Academy of Sciences.

[147]  K. Pienta,et al.  Evolution of cooperation among tumor cells , 2006, Proceedings of the National Academy of Sciences.

[148]  A. Anderson,et al.  A hybrid cellular automaton model of clonal evolution in cancer: the emergence of the glycolytic phenotype. , 2008, Journal of theoretical biology.

[149]  Dominik Wodarz,et al.  Stochastic modeling of cellular colonies with quiescence: an application to drug resistance in cancer. , 2007, Theoretical population biology.

[150]  T. Antal,et al.  Fixation of Strategies for an Evolutionary Game in Finite Populations , 2005, Bulletin of mathematical biology.

[151]  klaguia International Network of Cancer Genome Projects , 2010 .

[152]  Alexander R A Anderson,et al.  Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance , 2013, Interface Focus.

[153]  Peter Kuhn,et al.  Adrenal Metastases in Lung Cancer: Clinical Implications of a Mathematical Model , 2014, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[154]  Alexander R A Anderson,et al.  Quantifying the Role of Angiogenesis in Malignant Progression of Gliomas: in Silico Modeling Integrates Imaging and Histology Nih Public Access Author Manuscript Introduction , 2011 .

[155]  M. Ferrari,et al.  What does physics have to do with cancer? , 2011, Nature Reviews Cancer.

[156]  J H Goldie,et al.  A stochastic model for the origin and treatment of tumors containing drug-resistant cells. , 1986, Bulletin of mathematical biology.

[157]  Qing Nie,et al.  Nonlinear three-dimensional simulation of solid tumor growth , 2007 .

[158]  Niko Beerenwinkel,et al.  Construction of oncogenetic tree models reveals multiple pathways of oral cancer progression , 2009, International journal of cancer.

[159]  R. Durrett Branching Process Models of Cancer , 2015 .

[160]  Peter Kuhn,et al.  A Stochastic Markov Chain Model to Describe Lung Cancer Growth and Metastasis , 2012, PloS one.

[161]  R. Ganguly,et al.  Mathematical model for the cancer stem cell hypothesis , 2006, Cell proliferation.

[162]  Z. Trajanoski,et al.  Effector memory T cells, early metastasis, and survival in colorectal cancer. , 2005, The New England journal of medicine.

[163]  David Basanta,et al.  A mathematical model of tumour self-seeding reveals secondary metastatic deposits as drivers of primary tumour growth , 2012, Journal of The Royal Society Interface.

[164]  K. Polyak,et al.  Tumorigenesis: it takes a village , 2015, Nature Reviews Cancer.

[165]  Steven J. M. Jones,et al.  Comprehensive genomic characterization of squamous cell lung cancers , 2012, Nature.

[166]  M. Kolev Mathematical modelling of the competition between tumors and immune system considering the role of the antibodies , 2003 .

[167]  A. Anderson,et al.  A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion , 2005 .

[168]  J. Murray,et al.  Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion , 2003, Journal of the Neurological Sciences.

[169]  B. Ross,et al.  Mathematical Modeling of PDGF-Driven Glioblastoma Reveals Optimized Radiation Dosing Schedules , 2014, Cell.

[170]  R. Schreiber,et al.  Cancer immunoediting: from immunosurveillance to tumor escape , 2002, Nature Immunology.

[171]  M. Chaplain,et al.  Two-dimensional models of tumour angiogenesis and anti-angiogenesis strategies. , 1997, IMA journal of mathematics applied in medicine and biology.

[172]  R G Dale,et al.  The application of the linear-quadratic dose-effect equation to fractionated and protracted radiotherapy. , 1985, The British journal of radiology.

[173]  Bruce A. Robertson,et al.  The Cancer Diaspora: Metastasis beyond the Seed and Soil Hypothesis , 2013, Clinical Cancer Research.

[174]  Kristin R. Swanson,et al.  Response classification based on a minimal model of glioblastoma growth is prognostic for clinical outcomes and distinguishes progression from pseudoprogression. , 2013, Cancer research.

[175]  B. Frieden,et al.  Inducing catastrophe in malignant growth. , 2008, Mathematical medicine and biology : a journal of the IMA.

[176]  M. Chaplain,et al.  Paradoxical dependencies of tumor dormancy and progression on basic cell kinetics. , 2009, Cancer research.

[177]  Jacob G. Scott,et al.  A filter-flow perspective of haematogenous metastasis offers a non-genetic paradigm for personalised cancer therapy. , 2013, European journal of cancer.

[178]  Jian Li,et al.  Temporal dissection of tumorigenesis in primary cancers. , 2011, Cancer discovery.

[179]  G. Parmigiani,et al.  The Consensus Coding Sequences of Human Breast and Colorectal Cancers , 2006, Science.

[180]  C. Iacobuzio-Donahue,et al.  Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies , 2012, Cell.

[181]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.

[182]  Philip Hahnfeldt,et al.  A Multicompartment Mathematical Model of Cancer Stem Cell-Driven Tumor Growth Dynamics , 2014, Bulletin of mathematical biology.

[183]  H. Othmer,et al.  Mathematical modeling of tumor-induced angiogenesis , 2004, Journal of mathematical biology.

[184]  J. Panetta,et al.  A mathematical model of periodically pulsed chemotherapy: tumor recurrence and metastasis in a competitive environment. , 1996, Bulletin of mathematical biology.

[185]  M. L. Martins,et al.  Reaction-diffusion model for the growth of avascular tumor. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[186]  Ingo Roeder,et al.  Dynamic modeling of imatinib-treated chronic myeloid leukemia: functional insights and clinical implications , 2006, Nature Medicine.

[187]  S. McDougall,et al.  Mathematical modelling of dynamic adaptive tumour-induced angiogenesis: clinical implications and therapeutic targeting strategies. , 2006, Journal of theoretical biology.

[188]  Natalia L. Komarova,et al.  Mathematical Oncology: Using Mathematics to Enable Cancer Discoveries , 2014, Am. Math. Mon..

[189]  A. Lambert Branching Processes: Variation, Growth and Extinction of Populations , 2006 .

[190]  T. P. Dryja,et al.  Expression of recessive alleles by chromosomal mechanisms in retinoblastoma , 1983, Nature.

[191]  T. Byzova,et al.  Mechanisms of Integrin–Vascular Endothelial Growth Factor Receptor Cross-Activation in Angiogenesis , 2007, Circulation research.

[192]  M. Marra,et al.  Driver and passenger mutations in cancer. , 2015, Annual review of pathology.

[193]  Martin A Nowak,et al.  Evolutionary dynamics of escape from biomedical intervention , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[194]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[195]  D A Lauffenburger,et al.  Analysis of the roles of microvessel endothelial cell random motility and chemotaxis in angiogenesis. , 1991, Journal of theoretical biology.

[196]  Rebecca J Shipley,et al.  Multiscale Modeling of Fluid Transport in Tumors , 2008, Bulletin of mathematical biology.

[197]  F. Michor,et al.  Evolution of acquired resistance to anti-cancer therapy. , 2014, Journal of theoretical biology.

[198]  A. Denman Cellular and Molecular Immunology , 1992 .

[199]  H. Byrne Dissecting cancer through mathematics: from the cell to the animal model , 2010, Nature Reviews Cancer.

[200]  M. Hochberg,et al.  Peto's paradox and human cancers , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[201]  S Paget,et al.  THE DISTRIBUTION OF SECONDARY GROWTHS IN CANCER OF THE BREAST. , 1889 .

[202]  R. Jain Normalizing tumor microenvironment to treat cancer: bench to bedside to biomarkers. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[203]  P. Armitage,et al.  The age distribution of cancer and a multi-stage theory of carcinogenesis , 1954, British Journal of Cancer.

[204]  H. Ohtsuki,et al.  Accumulation of driver and passenger mutations during tumor progression , 2009, Proceedings of the National Academy of Sciences.

[205]  P. Okunieff,et al.  Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: a review. , 1989, Cancer research.

[206]  P Hahnfeldt,et al.  Migration rules: tumours are conglomerates of self-metastases , 2009, British Journal of Cancer.

[207]  F. C. Santos,et al.  The ecology of cancer from an evolutionary game theory perspective , 2014, Interface Focus.

[208]  J. C. Gore,et al.  Incorporation of diffusion-weighted magnetic resonance imaging data into a simple mathematical model of tumor growth , 2012, Physics in medicine and biology.

[209]  Y. Nakamura,et al.  Genetic alterations during colorectal-tumor development. , 1988, The New England journal of medicine.

[210]  Steven A Frank,et al.  Patterns of cell division and the risk of cancer. , 2003, Genetics.

[211]  Arne Traulsen,et al.  A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues , 2013, Journal of The Royal Society Interface.

[212]  J. Allan,et al.  Mechanisms of therapy-related carcinogenesis , 2005, Nature Reviews Cancer.

[213]  Franziska Michor,et al.  A Mathematical Methodology for Determining the Temporal Order of Pathway Alterations Arising during Gliomagenesis , 2012, PLoS Comput. Biol..

[214]  M. Washington,et al.  PIK3CA and APC mutations are synergistic in the development of intestinal cancers , 2014, Oncogene.

[215]  Giovanni Parmigiani,et al.  Half or more of the somatic mutations in cancers of self-renewing tissues originate prior to tumor initiation , 2013, Proceedings of the National Academy of Sciences.

[216]  Thomas E Yankeelov,et al.  Toward a science of tumor forecasting for clinical oncology. , 2015, Cancer research.

[217]  B. Frieden,et al.  Adaptive therapy. , 2009, Cancer research.

[218]  Peter Kuhn,et al.  Spreaders and Sponges Define Metastasis in Lung Cancer: A Markov Chain Monte Carlo Mathematical Model , 2013 .

[219]  Yoh Iwasa,et al.  The Evolution of Two Mutations During Clonal Expansion , 2007, Genetics.

[220]  Shamil R. Sunyaev,et al.  Impact of deleterious passenger mutations on cancer progression , 2012, Proceedings of the National Academy of Sciences.

[221]  J. Murray,et al.  Virtual brain tumours (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy , 2002, British Journal of Cancer.

[222]  William Pao,et al.  The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells , 2015, Cancer informatics.

[223]  Martin A Nowak,et al.  Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers , 2014, Proceedings of the National Academy of Sciences.

[224]  Nicholas Eriksson,et al.  The Temporal Order of Genetic and Pathway Alterations in Tumorigenesis , 2011, PloS one.

[225]  P. Nowell The clonal evolution of tumor cell populations. , 1976, Science.

[226]  Feng Jiang,et al.  Distance-Based Reconstruction of Tree Models for Oncogenesis , 2000, J. Comput. Biol..

[227]  F. Michor,et al.  Stochastic dynamics of cancer initiation , 2011, Physical biology.

[228]  A. Deutsch,et al.  Evolutionary game theory elucidates the role of glycolysis in glioma progression and invasion , 2008, Cell proliferation.