Geodesic
Hybrid data-based modelling in oncology: successes, challenges and hopes
Angélique Stéphanou1 ; Pascal Ballet2 ; Gibin Powathil3
1 Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38041 Grenoble, France.
2 University of Brest – LaTIM, UFR Médecine – IBRBS, 29238 Brest Cedex 3, France.
3 Department of Mathematics, Computational Foundry, College of Science, Swansea University, Swansea SA1 8EN, UK.
Mathematical modelling of natural phenomena, Tome 15 (2020), article no. 21.

Voir la notice de l'article provenant de la source EDP Sciences

In this opinion paper we make the statement that hybrid models in oncology are required as a mean for enhanced data integration. In the context of systems oncology, experimental and clinical data need to be at the heart of the models developments from conception to validation to ensure a relevant use of the models in the clinical context. The main applications pursued are to improve diagnosis and to optimize therapies.We first present the Successes achieved thanks to hybrid modelling approaches to advance knowledge, treatments or drug discovery. Then we present the Challenges that need to be addressed to allow for a better integration of the model parts and of the data into the models. And finally, the Hopes with a focus towards making personalised medicine a reality.
DOI : 10.1051/mmnp/2019026

Angélique Stéphanou 1 ; Pascal Ballet 2 ; Gibin Powathil 3

1 Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38041 Grenoble, France.
2 University of Brest – LaTIM, UFR Médecine – IBRBS, 29238 Brest Cedex 3, France.
3 Department of Mathematics, Computational Foundry, College of Science, Swansea University, Swansea SA1 8EN, UK. 
@article{MMNP_2020_15_a60,
     author = {Ang\'elique St\'ephanou and Pascal Ballet and Gibin Powathil},
     title = {Hybrid data-based modelling in oncology: successes, challenges and hopes},
     journal = {Mathematical modelling of natural phenomena},
     eid = {21},
     publisher = {mathdoc},
     volume = {15},
     year = {2020},
     doi = {10.1051/mmnp/2019026},
     language = {en},
     url = {https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2019026/}
}
TY  - JOUR
AU  - Angélique Stéphanou
AU  - Pascal Ballet
AU  - Gibin Powathil
TI  - Hybrid data-based modelling in oncology: successes, challenges and hopes
JO  - Mathematical modelling of natural phenomena
PY  - 2020
VL  - 15
PB  - mathdoc
UR  - https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2019026/
DO  - 10.1051/mmnp/2019026
LA  - en
ID  - MMNP_2020_15_a60
ER  - 
%0 Journal Article
%A Angélique Stéphanou
%A Pascal Ballet
%A Gibin Powathil
%T Hybrid data-based modelling in oncology: successes, challenges and hopes
%J Mathematical modelling of natural phenomena
%D 2020
%V 15
%I mathdoc
%U https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2019026/
%R 10.1051/mmnp/2019026
%G en
%F MMNP_2020_15_a60
Angélique Stéphanou; Pascal Ballet; Gibin Powathil. Hybrid data-based modelling in oncology: successes, challenges and hopes. Mathematical modelling of natural phenomena, Tome 15 (2020), article  no. 21. doi : 10.1051/mmnp/2019026. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2019026/

[1] Z. Agur, M. Elishmereni, Y. Kheifetz Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration Wiley Interdiscip. Rev. Syst. Biol. Med 2014 239 253

[2] J.C.L. Alfonso, K. Talkenberger, M. Seifert, B. Klink, A. Hawkins-Daarud, K.R. Swanson, H. Hatzikirou, A. Deutsch The biology and mathematical modelling of glioma invasion: a review J. Roy. Soc. Interface 2017 20170490

[3] P.M. Altrock, L.L. Liu, F. Michor The mathematics of cancer: integrating quantitative models Nat. Rev. Cancer 2015 730 745

[4] V. Andasari, R.T. Roper, M.H. Swat, M.A.J. Chaplain Integrating intracellular dynamics using compucell3d and bionetsolver: applications to multiscale modelling of cancer cell growth and invasion PLOS ONE 2012 1 17

[5] A.R.A. Anderson, P.K. Maini Mathematical oncology Bull. Math. Biol 2018 945 953

[6] A.L. Baldock, R.C. Rockne, A.D. Boone, M.L. Neal, A. Hawkins-Daarud, D.M. Corwin, C.A. Bridge, L.A. Guyman, A.D. Trister, M.M. Mrugala, J.K. Rockhill, K.R. Swanson From patient-specific mathematical neuro-oncology to precision medicine Front. Oncol 2013 62

[7] A. Ballesta, J. Clairambault Physiologically based mathematical models to optimize therapies against metastatic colorectal cancer: a mini-review Curr. Pharmaceut. Des 2014 37 48

[8] A. Ballesta, Q. Zhou, X. Zhang, H. Lv, J.M. Gallo Multiscale design of cell-type specific pharmacokinetic/ pharmacodynamic models for personalized medicine: application to temozolomide in brain tumors CPT Pharmacometr. Syst. Pharmacol 2014 e112

[9] A. Ballesta, P.F. Innominato, R. Dallmann, D.A. Rand, F.A. Levi Systems chronotherapeutics Pharmacolog. Rev 2017 161 199

[10] P. Ballet, SimCells, an advanced software for multicellular modeling: application to tumoral and blood vessel co-development, Working paper (unpublished) (2018).

[11] D. Barbolosi, J. Ciccolini, B. Lacarelle, F. Barlési, N. André Computational oncology-mathematical modelling of drug regimens for precision medicine Nat. Rev. Clin. Oncol 2016 242 254

[12] D. Basanta, A.R.A. Anderson Homeostasis back and forth: an ecoevolutionary perspective of cancer Cold Spring Harbor Perspect. Med 2017 a028332

[13] B. Bedessem, S. Ruphy Smt or toft? How the two main theories of carcinogenesis are made (artificially) incompatible Acta Biotheor 2015 257 267

[14] B. Bedessem, S. Ruphy Smt and toft integrable after all: a reply to bizzarri and cucina Acta Biotheor 2017 81 85

[15] M. Bizzarri, A. Cucina Smt and toft: why and how they are opposite and incompatible paradigms Acta Biotheor 2016 221 239

[16] M. Block Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps Exp. Opin. Drug Metab. Toxicol 2015 743 756

[17] A. Bouchnita, N. Eymard, T.K. Moyo, M.J. Koury, V. Volpert Bone marrow infiltration by multiple myeloma causes anemia by reversible disruption of erythropoiesis Am. J. Hematol 2016 371 378

[18] A. Bouchnita, F.-E. Belmati, R. Aboulaich, M.J. Koury, V. Volpert A hybrid computation model to describe the progression of multiple myeloma and its intra-clonal heterogeneity Computation 2017 16

[19] S. Brueningk, G. Powathil, P. Ziegenhein, J. Ljaz, I. Rivens, S. Nill, M. Chaplain, U. Oelfke, G. Ter Haar Combining radiation with hyperthermia: a multiscale model informed by in vitro experiments J. Roy. Soc. Interface 2018 20170681

[20] F. Caraguel, A.C. Lesart, F. Estève, B. Van Der Sanden, A. Stéphanou Towards the design of a patient-specific virtual tumour Comput. Math. Methods Med 2016 7851789

[21] L. Carrara, S.M. Lavezzi, E. Borella, G. De Nicolao, P. Magni, I. Poggesi Current mathematical models for cancer drug discovery Exp. Opin. Drug Discov 2017 785 799

[22] M.A.J. Chaplain The mathematical modelling of tumour angiogenesis and invasion Acta Biotheor 1995 387 402

[23] T. Colin, F. Cornelis, J. Jouganous, M. Martin, O. Saut Patient specific image driven evaluation of the aggressiveness of metastases to the lung Med. Image Comput. Comput. Assist Interv 2014 553 560

[24] L.M. Cook, A. Araujo, J.M. Pow-Sang, M.M. Budzevich, D. Basanta, C.C. Lynch Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer Sci. Rep 2016 29384

[25] C. Davis, H. Naci, E. Gurpinar, E. Poplavska, A. Pinto Availability of evidence of benefits on overall survival and quality of life of cancer drugs approved by European medicines agency: retrospective cohort study of drug approvals 2009–13 BMJ 2017 j4530

[26] H. Enderling, K.A. Rejniak Simulating cancer: computational models in oncology Front. Oncol 2013 233

[27] E.W. Esch, A. Bahinski, D. Huh Organs-on-chips at the frontiers of drug discovery Nat. Rev. Drug Discov 2015 248 260

[28] N. Eymard, V. Volpert, P. Kurbatova, V. Volpert, N. Bessonov, K. Ogungbenro, L. Aarons, P. Janiaud, P. Nony, A. Bajard, S. Chabaud, Y. Bertrand, B. Kassai, C. Cornu P. Nony and CRESim project group, Mathematical model of t-cell lymphoblastic lymphoma: disease, treatment, cure or relapse of a virtual cohort of patients Math. Med. Biol 2018 25 47

[29] J. Foo, F. Michor Evolution of acquired resistance to anti-cancer therapy J. Theor. Biol 2014 10 20

[30] J.A. Gallaher, P.M. Enriquez-Navas, K.A. Luddy, R.A. Gatenby, A.R.A. Anderson Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies Cancer Res 2018 2127 2139

[31] E. Garralda, R. Dienstmann, J. Tabernero Pharmacokinetic/pharmacodynamic modeling for drug development in oncology Am. Soc. Clin. Oncol. Annu. Meet 2017 210 215

[32] P. Gerlee, A.R.A. Anderson Evolution of cell motility in an individual-based model of tumour growth J. Theor. Biol 2009 67 83

[33] A. Ghaffarizadeh, R. Heiland, S.H. Friedman, S.M. Mumenthaler, P. Macklin PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems PLoS Comput. Biol 2018 e1005991

[34] N. Glade and A. Stéphanou, Le vivant discret et continu – Modes de représentation en biologie théorique. Editions Matériologiques, Paris (2013).

[35] F. Graner, J.A. Glazier Simulation of biological cell sorting using a two-dimensional extended potts model Phys. Rev. Lett 1992 2013 2016

[36] J.A. Grogan, A.J. Connor, B. Markelc, R.J. Muschel, P.K. Maini, H.M Byrne, J. Pitt-Francis Microvessel chaste: an open library for spatial modeling of vascularized tissue Biophys. J 2017 1767 1772

[37] S. Hamis, P. Nithiarasu, G.G. Powathil What does not kill a tumour may make it stronger: in silico insights into chemotherapeutic drug resistance J. Theor. Biol 2018 253 267

[38] L. Hutchinson, R. Kirk High drug attrition rates: where are we going wrong? Nat. Rev. Clin. Oncol. 2011 189 190

[39] P.R. Jackson, J. Juliano, A. Hawkins-Daarud, R.C. Rockne, K.R. Swanson Patient-specific mathematical neuro-oncology: using a simple proliferation and invasion tumor model to inform clinical practice Bull. Math. Biol 2015 846 856

[40] A.M. Jarrett, E.A.B.F. Lima, D.A. Hormuth, M.T. Mckenna, X. Feng, D.A. Ekrut, A.C.M. Resende, A. Brock, T.E. Yankeelov Mathematical models of tumor cell proliferation: a review of the literature Exp. Rev. Anticancer Therapy 2018 1 16

[41] Z. Ji, K. Yan, W. Li, H. Hu, X. Zhu Mathematical and computational modeling in complex biological systems BioMed Res. Int 2017 5958321

[42] A. Karolak, D.A. Markov, L.J. Mccawley, K.A. Rejniak Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues J. Roy. Soc. Interface 2018 20170703

[43] Y. Kim, G. Powathil, H. Kang, D. Trucu, H. Kim, S. Lawler, M. Chaplain Strategies of eradicating glioma cells: a multi-scale mathematical model with mir-451-ampk-mtor control PLoS ONE 2015 e0114370j

[44] I. Kola, J. Landis Can the pharmaceutical industry reduce the attrition rates? Nat. Rev. Drug Discov. 2004 711 715

[45] N. Kronik, Y. Kogan, M. Elishmereni, K. Halevi-Tobias, S. Vuk-Pavlović, Z. Agur Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models PLOS ONE 2010 1 8

[46] P. Kurbatova, S. Bernard, N. Bessonov, F. Crauste, I. Denim, C. Dumontet, S. Fischer, V. Volpert Hybrid model of erythtropoiesis and leukemia treatment with cytosine arabinoside SIAM J. Appl. Math 2011 2246 2268

[47] A.K. Laird Dynamics of tumor growth Br. J. Cancer 1964 490 502

[48] A.C. Lesart, B. Van Der Sanden, L. Hamard, F. Estève, A. Stéphanou On the importance of the submicrovascular network in a computational model of tumour growth Microvasc. Res 2012 188 204

[49] W.B. Looney, J.S. Trefil, J.C. Schaffner, C.J. Kovacs, H.A. Hopkins Solid tumor models for the assessment of different treatment modalities: I. Radiation-induced changes in growth rate characteristics of a solid tumor model Proc. Natl. Acad. Sci. U.S.A 1975 2662 2666

[50] P. Macklin, M.E. Edgerton, A.M. Thompson, V. Cristini Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression J. Theor. Biol 2012 122 140

[51] P. Macklin, H.B. Frieboes, J.L. Sparks, A. Ghaffarizadeh, S.H. Friedman, E.F. Juarez, E. Jonckheere, S.M. Mumenthaler Progress towards computational 3-d multicellular systems biology Adv. Exp. Med. Biol 2016 225 246

[52] S.C. Massey, R.C. Rockne, A. Hawkins-Daarud, J. Gallaher, A.R.A. Anderson, P. Canoll, K.R. Swanson Simulating PDGF-driven glioma growth and invasion in an anatomically accurate brain domain Bull. Math. Biol 2018 1292 1309

[53] G.R. Mirams, C.J. Arthurs, M.O. Bernabeu, R. Bordas, J. Cooper, A. Corrias, Y. Davit, S.J. Dunn, A.G. Fletcher, D.G. Harvey, M.E. Marsh, J.M. Osborne, P. Pathmanathan, J. Pitt-Francis, J. Southern, N. Zemzemi, D.J. Gavaghan Chaste: an open source c++ library for computational physiology and biology PLOS Comput. Biol 2013 1 8

[54] M. Montévil, A. Pocheville The hitchhiker’s guide to the cancer galaxy. How two critics missed their destination Orgnisms. J. Biol. Sci. 2017 37 48

[55] M.M. Palm, M.G. Dallinga, E. Van Dijk, I. Klaassen, R.O. Schlingemann, R.M.H. Merks Computational screening of tip and stalk cell behavior proposes a role for apelin signaling in sprout progression PLOS ONE 2016 1 31

[56] A.R. Perestrelo, A.C.P. Águas, A. Rainer, G. Forte Microfluidic organ/body-on-a-chip devices at the convergence of biology and microengineering Sensors (Basel, Switzerland) 2015 31142 31170

[57] R.K. Perez, R. Kang, R. Chen, J.G. Castellanos, A.R. Milewski, A.R. Perez Computational oncology J. Oncopathol. Clin. Res 2018

[58] J. Pitt-Francis, P. Pathmanathan, M.O. Bernabeu, R. Bordas, J. Cooper, A.G. Fletcher, G.R. Mirams, P. Murray, J.M. Osborne, A. Walter, S.J. Chapman, A. Garny, I.M.M. Van Leeuwen, P.K. Maini, B. Rodriguez, S.L. Waters, J.P. Whiteley, H.M. Byrne, D.J. Gavaghan Chaste: a test-driven approach to software development for biological modelling Comp. Phys. Commun 2009 2452 2471

[59] J. Poleszczuk, R. Walker, E.G. Moros, K. Latifi, J.J. Caudell, H. Enderling Predicting patient-specific radiotherapy protocols based on mathematical model choice for proliferation saturation index Bull. Math. Biol 2018 1195 1206

[60] M. Pons-Salort, B. Van Der Sanden, A. Juhem, A. Popov, A. Stéphanou A computational framework to assess the efficacy of cytotoxic molecules and vascular disrupting agents against solid tumours MMNP 2012 49 77

[61] G.G. Powathil, M. Swat, M.A.J. Chaplain Systems oncology: towards patient-specific treatment regimes informed by multiscale mathematical modelling Semin. Cancer Biol 2015 13 20

[62] G.G. Powathil, A.J. Munro, M.A.J. Chaplain, M. Swat Bystander effects and their implications for clinical radiation therapy: insights from multiscale in silico experiments J. Theor. Biol 2016 1 14

[63] S. Prokopiou, E.G. Moros, J. Poleszczuk, J. Caudell, J.F. Torres-Roca, K. Latifi, J.K. Lee, R. Myerson, L.B. Harrison, H. Enderling A proliferation saturation index to predict radiation response and personalize radiotherapy fractionation Radiat. Oncol 2015 159

[64] C. Sonnenschein, A.M. Soto Carcinogenesis explained within the context of a theory of organisms Progr. Biophys. Molec. Biol 2016 70 76

[65] A.M. Soto, C. Sonnenschein The tissue organization field theory of cancer: a testable replacement for the somatic mutation theory BioEssays 2011 332 340

[66] A. Stéphanou, V. Volpert Hybrid modelling in biology: a classification review MMNP 2016 37 48

[67] A. Stéphanou, S.R. Mcdougall, A.R.A. Anderson, M.A.J. Chaplain Mathematical modelling of flow in 2d and 3d vascular networks: applications to antiangiogenic and chemotherapeutic drug stategies Math. Comp. Model 2005 1137 1156

[68] A. Stéphanou, A.C. Lesart, J. Deverchère, A. Juhem, A. Popov, F. Estève How tumour-induced vascular changes alter angiogenesis: Insights from a computational model J. Theor. Biol 2017 211 226

[69] A. Stéphanou, E. Fanchon, P.F. Innominato, A. Ballesta Systems biology, systems medicine, systems pharmacology: the what and the why Acta Biotheor 2018 345 365

[70] M.H. Swat, G.L. Thomas, J.M. Belmonte, A. Shirinifard, D. Hmeljak and J.A. Glazier, Multi-scale modeling of tissues using compucell3d. In Vol. 110 of Computational Methods in Cell Biology. Edited by Anand R. Asthagiri and Adam P. Arkin. Academic Press (2012) 325–366.

[71] I.M.M. Van Leeuwen, G.R. Mirams, A. Walter, A. Fletcher, P. Murray, J. Osborne, S. Varma, S.J. Young, J. Cooper, B. Doyle, J. Pitt-Francis, L. Momtahan, P. Pathmanathan, J.P. Whiteley, S.J. Chapman, D.J. Gavaghan, O.E. Jensen, J.R. King, P.K. Maini, S.L. Waters, H.M. Byrne An integrative computational model for intestinal tissue renewal Cell Proliferation 2009 617 636

[72] C.H. Wang, J.K. Rockhill, M. Mrugala, D.L. Peacock, A. Lai, K. Jusenius, J.M. Wardlaw, T. Cloughesy, A.M. Spence, R. Rockne, E.C. Alvord, K.R. Swanson Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model Cancer Res 2009 9133 9140

[73] K.K. Winner, M.P. Steinkamp, R.J. Lee, M. Swat, C.Y. Muller, M.E. Moses, Y. Jiang, B.S. Wilson Spatial modeling of drug delivery routes for treatment of disseminated ovarian cancer Cancer Res 2016 1320 1334

[74] T.E. Yankeelov Integrating imaging data into predictive biomathematical and biophysical models of cancer ISRN Biomath 2012

[75] T.E. Yankeelov, R.G. Abramson, C.C. Quarles Quantitative multimodality imaging in cancer research and therapy Nat. Rev. Clin. Oncol 2014 670 680

[76] T.E. Yankeelov, G. An, O. Saut, E.G. Luebeck, A.S. Popel, B. Ribba, P. Vicini, X. Zhou, J.A. Weis, K. Ye, G.M. Genin Multi-scale modeling in clinical oncology: opportunities and barriers to success Ann. Biomed. Eng 2016 2626 2641

Cité par Sources :