A Multilayer Grow-or-Go Model for GBM: Effects of Invasive Cells and Anti-Angiogenesis on Growth

The recent use of anti-angiogenesis (AA) drugs for the treatment of glioblastoma multiforme (GBM) has uncovered unusual tumor responses. Here, we derive a new mathematical model that takes into account the ability of proliferative cells to become invasive under hypoxic conditions; model simulations generate the multilayer structure of GBM, namely proliferation, brain invasion, and necrosis. The model is able to replicate and justify the clinical observation of rebound growth when AA therapy is discontinued in some patients. The model is interrogated to derive fundamental insights int cancer biology and on the clinical and biological effects of AA drugs. Invasive cells promote tumor growth, which in the long run exceeds the effects of angiogenesis alone. Furthermore, AA drugs increase the fraction of invasive cells in the tumor, which explain progression by fluid-attenuated inversion recovery (FLAIR) signal and the rebound tumor growth when AA is discontinued.

[1]  K. Mikula,et al.  Error estimates of the finite volume scheme for the nonlinear tensor-driven anisotropic diffusion , 2009 .

[2]  D. Ba Santa,et al.  'Go or Grow': the key to the emergence of invasion in tumour progression? , 2010 .

[3]  Pedro Moreo,et al.  Bone ingrowth on the surface of endosseous implants. Part 1: Mathematical model. , 2009, Journal of theoretical biology.

[4]  V. Quaranta,et al.  Microenvironment driven invasion: a multiscale multimodel investigation , 2009, Journal of mathematical biology.

[5]  A. Anderson,et al.  Evolution of cell motility in an individual-based model of tumour growth. , 2009, Journal of theoretical biology.

[6]  N. Cordes,et al.  Hypoxia-Induced Tumour Cell Migration in an in vivo Chicken Model , 2000, Pathobiology.

[7]  T. Mikkelsen,et al.  Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  J. Uhm Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group , 2010 .

[9]  Hervé Delingette,et al.  Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins , 2010, Medical Image Anal..

[10]  M. Weller,et al.  Angiogenesis inhibition for glioblastoma at the edge: beyond AVAGlio and RTOG 0825. , 2013, Neuro-oncology.

[11]  T. Taxt,et al.  Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma , 2011, Proceedings of the National Academy of Sciences.

[12]  H. Fathallah-Shaykh,et al.  c-Src and Neural Wiskott-Aldrich Syndrome Protein (N-WASP) Promote Low Oxygen-Induced Accelerated Brain Invasion by Gliomas , 2013, PloS one.

[13]  M. Westphal,et al.  Cost of migration: invasion of malignant gliomas and implications for treatment. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  Hervé Delingette,et al.  Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations , 2010, IEEE Transactions on Medical Imaging.

[15]  P. Maini,et al.  A cellular automaton model for tumour growth in inhomogeneous environment. , 2003, Journal of theoretical biology.

[16]  John A Butman,et al.  Phase II trial of single-agent bevacizumab followed by bevacizumab plus irinotecan at tumor progression in recurrent glioblastoma. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  Hervé Delingette,et al.  Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation , 2005, IEEE Transactions on Medical Imaging.

[18]  R. Eymard,et al.  Finite Volume Methods , 2019, Computational Methods for Fluid Dynamics.

[19]  S. Jonathan Chapman,et al.  Mathematical Models of Avascular Tumor Growth , 2007, SIAM Rev..

[20]  M. Bredel,et al.  A phase 2 trial of single‐agent bevacizumab given in an every‐3‐week schedule for patients with recurrent high‐grade gliomas , 2010, Cancer.

[21]  Patricia J Keely,et al.  Mammary gland ECM remodeling, stiffness, and mechanosignaling in normal development and tumor progression. , 2011, Cold Spring Harbor perspectives in biology.

[22]  K. Schaller,et al.  Identification of intrinsic in vitro cellular mechanisms for glioma invasion. , 2011, Journal of theoretical biology.

[23]  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 .

[24]  D. Bresch,et al.  Computational Modeling of Solid Tumor Growth: The Avascular Stage , 2010, SIAM J. Sci. Comput..

[25]  Helen M. Byrne,et al.  Density-dependent quiescence in glioma invasion: instability in a simple reaction–diffusion model for the migration/proliferation dichotomy , 2012, Journal of biological dynamics.

[26]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[27]  Danping Peng,et al.  Weighted ENO Schemes for Hamilton-Jacobi Equations , 1999, SIAM J. Sci. Comput..

[28]  K. Hoang-Xuan,et al.  Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. , 2014, The New England journal of medicine.

[29]  Webster K. Cavenee,et al.  Erratum: The 2007 WHO classification of tumours of the central nervous system (Acta Neuropathol (2007) vol. 114 (97-109)) , 2007 .

[30]  David A Mankoff,et al.  Volumetric analysis of 18F-FDG PET in glioblastoma multiforme: prognostic information and possible role in definition of target volumes in radiation dose escalation. , 2002, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[31]  Philip Gerlee,et al.  The Impact of Phenotypic Switching on Glioblastoma Growth and Invasion , 2012, PLoS Comput. Biol..

[32]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[33]  Angelo Iollo,et al.  An inverse problem for the recovery of the vascularization of a tumor , 2014 .

[34]  J. Tonn,et al.  Mechanisms of glioma cell invasion. , 2003, Acta neurochirurgica. Supplement.

[35]  Olga Stasová,et al.  Convergence Analysis of Finite Volume Scheme for Nonlinear Tensor Anisotropic Diffusion in Image Processing , 2007, SIAM J. Numer. Anal..

[36]  T. Cloughesy,et al.  Bevacizumab for newly diagnosed glioblastoma. , 2014, The New England journal of medicine.

[37]  Michael Berens,et al.  A mathematical model of glioblastoma tumor spheroid invasion in a three-dimensional in vitro experiment. , 2007, Biophysical journal.

[38]  Ignacio Ramis-Conde,et al.  Modeling the influence of the E-cadherin-beta-catenin pathway in cancer cell invasion: a multiscale approach. , 2008, Biophysical journal.

[39]  Thomas S Deisboeck,et al.  Emerging patterns in tumor systems: simulating the dynamics of multicellular clusters with an agent-based spatial agglomeration model. , 2002, Journal of theoretical biology.

[40]  P. Carmeliet,et al.  Antiangiogenic therapy, hypoxia, and metastasis: risky liaisons, or not? , 2011, Nature Reviews Clinical Oncology.

[41]  A. Giese Glioma invasion--pattern of dissemination by mechanisms of invasion and surgical intervention, pattern of gene expression and its regulatory control by tumorsuppressor p53 and proto-oncogene ETS-1. , 2003, Acta neurochirurgica. Supplement.

[42]  D. Drasdo,et al.  Individual-based approaches to birth and death in avascu1ar tumors , 2003 .

[43]  Philippe Angot,et al.  A penalization method to take into account obstacles in incompressible viscous flows , 1999, Numerische Mathematik.

[44]  H. Frieboes,et al.  Computer simulation of glioma growth and morphology , 2007, NeuroImage.

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

[46]  M. Neeman,et al.  Hypoxic stress and cancer: imaging the axis of evil in tumor metastasis , 2011, NMR in biomedicine.

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

[48]  Kristin R. Swanson,et al.  Quantifying glioma cell growth and invasion in vitro , 2008, Math. Comput. Model..

[49]  Kristin R. Swanson,et al.  Virtual resection of gliomas: Effect of extent of resection on recurrence , 2003 .

[50]  L. Preziosi,et al.  ON THE CLOSURE OF MASS BALANCE MODELS FOR TUMOR GROWTH , 2002 .

[51]  Didier Bresch,et al.  A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy. , 2009, Journal of theoretical biology.

[52]  M. Westphal,et al.  Invasion as limitation to anti-angiogenic glioma therapy. , 2003, Acta neurochirurgica. Supplement.

[53]  M. Gilbert,et al.  Bevacizumab for newly diagnosed glioblastoma. , 2014, The New England journal of medicine.

[54]  Vittorio Cristini,et al.  Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach , 2010 .

[55]  Hsin-Chieh Yeh,et al.  Effect of the 2011 vs 2003 duty hour regulation-compliant models on sleep duration, trainee education, and continuity of patient care among internal medicine house staff: a randomized trial. , 2013, JAMA internal medicine.

[56]  Didier Bresch,et al.  A viscoelastic model for avascular tumor growth , 2009 .

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

[58]  K. Aldape,et al.  A randomized trial of bevacizumab for newly diagnosed glioblastoma. , 2014, The New England journal of medicine.

[59]  T. Mikkelsen,et al.  Efficacy, safety and patterns of response and recurrence in patients with recurrent high-grade gliomas treated with bevacizumab plus irinotecan , 2009, Journal of Neuro-Oncology.