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Benzekry S, Schlicke P, Mogenet A, Greillier L, Tomasini P, Simon E. Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases. Clin Exp Metastasis 2024; 41:55-68. [PMID: 38117432 DOI: 10.1007/s10585-023-10245-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 11/07/2023] [Indexed: 12/21/2023]
Abstract
Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive computational markers from primary tumor and BM data at [Formula: see text] and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are [Formula: see text], the proliferation rate of a single tumor cell; and [Formula: see text], the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at [Formula: see text] for 20 patients. Parameters [Formula: see text] and [Formula: see text] were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p = 0.0029 and HR 1.95 (1.31-2.91) p = 0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p < 0.0001). We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.
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Affiliation(s)
- Sébastien Benzekry
- COMPutational Pharmacology and Clinical Oncology Department, Inria Sophia Antipolis - Méditerranée, Faculté de Pharmacie, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 27 Boulevard Jean Moulin, 13005, Marseille, France.
| | - Pirmin Schlicke
- Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Garching (Munich), Germany
| | - Alice Mogenet
- Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
| | - Laurent Greillier
- Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
- Aix Marseille University, CNRS, INSERM, CRCM, Marseille, France
| | - Pascale Tomasini
- Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
- Aix Marseille University, CNRS, INSERM, CRCM, Marseille, France
| | - Eléonore Simon
- Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
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Pradeu T, Daignan-Fornier B, Ewald A, Germain PL, Okasha S, Plutynski A, Benzekry S, Bertolaso M, Bissell M, Brown JS, Chin-Yee B, Chin-Yee I, Clevers H, Cognet L, Darrason M, Farge E, Feunteun J, Galon J, Giroux E, Green S, Gross F, Jaulin F, Knight R, Laconi E, Larmonier N, Maley C, Mantovani A, Moreau V, Nassoy P, Rondeau E, Santamaria D, Sawai CM, Seluanov A, Sepich-Poore GD, Sisirak V, Solary E, Yvonnet S, Laplane L. Reuniting philosophy and science to advance cancer research. Biol Rev Camb Philos Soc 2023; 98:1668-1686. [PMID: 37157910 PMCID: PMC10869205 DOI: 10.1111/brv.12971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023]
Abstract
Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.
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Affiliation(s)
- Thomas Pradeu
- CNRS UMR5164 ImmunoConcEpT, University of Bordeaux, 146 rue Leo Saignat, Bordeaux 33076, France
- CNRS UMR8590, Institut d’Histoire et Philosophie des Sciences et des Technique, University Paris I Panthéon-Sorbonne, 13 rue du Four, Paris 75006, France
| | - Bertrand Daignan-Fornier
- CNRS UMR 5095 Institut de Biochimie et Génétique Cellulaires, University of Bordeaux, 1 rue Camille St Saens, Bordeaux 33077, France
| | - Andrew Ewald
- Departments of Cell Biology and Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Pierre-Luc Germain
- Department of Health Sciences and Technology, Institute for Neurosciences, Eidgenössische Technische Hochschule (ETH) Zürich, Universitätstrasse 2, Zürich 8092, Switzerland
- Department of Molecular Life Sciences, Laboratory of Statistical Bioinformatics, Universität Zürich, Winterthurerstrasse 190, Zurich 8057, Switzerland
| | - Samir Okasha
- Department of Philosophy, University of Bristol, Cotham House, Bristol, BS6 6JL, UK
| | - Anya Plutynski
- Department of Philosophy, Washington University in St. Louis, and Associate with Division of Biology and Biomedical Sciences, St. Louis, MO 63105, USA
| | - Sébastien Benzekry
- Computational Pharmacology and Clinical Oncology (COMPO) Unit, Inria Sophia Antipolis-Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 27, bd Jean Moulin, Marseille 13005, France
| | - Marta Bertolaso
- Research Unit of Philosophy of Science and Human Development, Università Campus Bio-Medico di Roma, Via Àlvaro del Portillo, 21-00128, Rome, Italy
- Centre for Cancer Biomarkers, University of Bergen, Bergen 5007, Norway
| | - Mina Bissell
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Benjamin Chin-Yee
- Division of Hematology, Department of Medicine, Schulich School of Medicine and Dentistry, Western University, 800 Commissioners Rd E, London, ON, Canada
- Rotman Institute of Philosophy, Western University, 1151 Richmond Street North, London, ON, Canada
| | - Ian Chin-Yee
- Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, Western University, 800 Commissioners Rd E, London, ON, Canada
| | - Hans Clevers
- Pharma, Research and Early Development (pRED) of F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel 4070, Switzerland
- Oncode Institute, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center, Uppsalalaan 8, Utrecht 3584 CT, The Netherlands
| | - Laurent Cognet
- CNRS UMR 5298, Laboratoire Photonique Numérique et Nanosciences, University of Bordeaux, Rue François Mitterrand, Talence 33400, France
| | - Marie Darrason
- Department of Pneumology and Thoracic Oncology, University Hospital of Lyon, 165 Chem. du Grand Revoyet, 69310 Pierre Bénite, Lyon, France
- Lyon Institute of Philosophical Research, Lyon 3 Jean Moulin University, 1 Av. des Frères Lumière, Lyon 69007, France
| | - Emmanuel Farge
- Mechanics and Genetics of Embryonic and Tumor Development group, Institut Curie, CNRS, UMR168, Inserm, Centre Origines et conditions d’apparition de la vie (OCAV) Paris Sciences Lettres Research University, Sorbonne University, Institut Curie, 11 rue Pierre et Marie Curie, Paris 75005, France
| | - Jean Feunteun
- INSERM U981, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
| | - Jérôme Galon
- INSERM UMRS1138, Integrative Cancer Immunology, Cordelier Research Center, Sorbonne Université, Université Paris Cité, 15 rue de l’École de Médecine, Paris 75006, France
| | - Elodie Giroux
- Lyon Institute of Philosophical Research, Lyon 3 Jean Moulin University, 1 Av. des Frères Lumière, Lyon 69007, France
| | - Sara Green
- Section for History and Philosophy of Science, Department of Science Education, University of Copenhagen, Rådmandsgade 64, Copenhagen 2200, Denmark
| | - Fridolin Gross
- CNRS UMR5164 ImmunoConcEpT, University of Bordeaux, 146 rue Leo Saignat, Bordeaux 33076, France
| | - Fanny Jaulin
- INSERM U1279, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
| | - Rob Knight
- Department of Bioengineering, University of California San Diego, 3223 Voigt Dr, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Ezio Laconi
- Department of Biomedical Sciences, School of Medicine, University of Cagliari, Via Università 40, Cagliari 09124, Italy
| | - Nicolas Larmonier
- CNRS UMR5164 ImmunoConcEpT, University of Bordeaux, 146 rue Leo Saignat, Bordeaux 33076, France
| | - Carlo Maley
- Arizona Cancer Evolution Center, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
- School of Life Sciences, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
- Biodesign Center for Biocomputing, Security and Society, Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85287, USA
- Biodesign Center for Mechanisms of Evolution, Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85287, USA
- Center for Evolution and Medicine, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
| | - Alberto Mantovani
- Department of Biomedical Sciences, Humanitas University, 4 Via Rita Levi Montalcini, 20090 Pieve Emanuele, Milan, Italy
- Department of Immunology and Inflammation, Istituto Clinico Humanitas Humanitas Cancer Center (IRCCS) Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
- The William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Violaine Moreau
- INSERM UMR1312, Bordeaux Institute of Oncology (BRIC), University of Bordeaux, 146 Rue Léo Saignat, Bordeaux 33076, France
| | - Pierre Nassoy
- CNRS UMR 5298, Laboratoire Photonique Numérique et Nanosciences, University of Bordeaux, Rue François Mitterrand, Talence 33400, France
| | - Elena Rondeau
- INSERM U1111, ENS Lyon and Centre International de Recherche en Infectionlogie (CIRI), 46 Allée d’Italie, Lyon 69007, France
| | - David Santamaria
- Molecular Mechanisms of Cancer Program, Centro de Investigación del Cáncer, Consejo Superior de Investigaciones Científicas (CSIC)-University of Salamanca, Salamanca 37007, Spain
| | - Catherine M. Sawai
- INSERM UMR1312, Bordeaux Institute of Oncology (BRIC), University of Bordeaux, 146 Rue Léo Saignat, Bordeaux 33076, France
| | - Andrei Seluanov
- Department of Biology and Medicine, University of Rochester, Rochester, NY 14627, USA
| | | | - Vanja Sisirak
- CNRS UMR5164 ImmunoConcEpT, University of Bordeaux, 146 rue Leo Saignat, Bordeaux 33076, France
| | - Eric Solary
- INSERM U1287, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
- Département d’hématologie, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
- Université Paris-Saclay, Faculté de Médecine, 63 Rue Gabriel Péri, Le Kremlin-Bicêtre 94270, France
| | - Sarah Yvonnet
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen DK-2200, Denmark
| | - Lucie Laplane
- CNRS UMR8590, Institut d’Histoire et Philosophie des Sciences et des Technique, University Paris I Panthéon-Sorbonne, 13 rue du Four, Paris 75006, France
- INSERM U1287, Gustave Roussy, 114 Rue Edouard Vaillant, Villejuif 94800, France
- Center for Biology and Society, College of Liberal Arts and Sciences, Arizona State University, 1100 S McAllister Ave, Tempe, AZ 85281, USA
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Bigarré C, Bertucci F, Finetti P, Macgrogan G, Muracciole X, Benzekry S. Mechanistic modeling of metastatic relapse in early breast cancer to investigate the biological impact of prognostic biomarkers. Comput Methods Programs Biomed 2023; 231:107401. [PMID: 36804267 DOI: 10.1016/j.cmpb.2023.107401] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 01/12/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Estimating the risk of metastatic relapse is a major challenge to decide adjuvant treatment options in early-stage breast cancer (eBC). To date, distant metastasis-free survival (DMFS) analysis mainly relies on classical, agnostic, statistical models (e.g., Cox regression). Instead, we propose here to derive mechanistic models of DMFS. METHODS The present series consisted of eBC patients who did not receive adjuvant systemic therapy from three datasets, composed respectively of 692 (Bergonié Institute), 591 (Paoli-Calmettes Institute, IPC), and 163 (Public Hospital Marseille, AP-HM) patients with routine clinical annotations. The last dataset also contained expression of three non-routine biomarkers. Our mechanistic model of DMFS relies on two mathematical parameters that represent growth (α) and dissemination (μ). We identified their population distributions using mixed-effects modeling. Critically, we propose a novel variable selection procedure allowing to: (i) identify the association of biological parameters with either α, μ or both, and (ii) generate an optimal candidate model for DMFS prediction. RESULTS We found that Ki67 and Thymidine Kinase-1 were associated with α, and nodal status and Plasminogen Activator Inhibitor-1 with μ. The predictive performances of the model were excellent in calibration but moderate in discrimination, with c-indices of 0.72 (95% CI [0.48, 0.95], AP-HM), 0.63 ([0.44, 0.83], Bergonié) and 0.60 (95% CI [0.54, 0.80], IPC). CONCLUSIONS Overall, we demonstrate that our novel method combining mechanistic and advanced statistical modeling is able to unravel the biological roles of clinicopathological parameters from DMFS data.
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Affiliation(s)
- Célestin Bigarré
- COMPO, Inria Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 13385 Marseille, France.
| | - François Bertucci
- Predictive Oncology Laboratory, Marseille Cancer Research Centre (CRCM), Inserm U1068, CNRS UMR7258, Institut Paoli-Calmettes, Equipe labellisée Ligue Nationale Contre Le Cancer, Aix-Marseille University, Marseille, France; Department of Medical Oncology, CRCM, Institut Paoli-Calmettes, CNRS, Inserm, Aix-Marseille University, Marseille, France
| | - Pascal Finetti
- Predictive Oncology Laboratory, Marseille Cancer Research Centre (CRCM), Inserm U1068, CNRS UMR7258, Institut Paoli-Calmettes, Equipe labellisée Ligue Nationale Contre Le Cancer, Aix-Marseille University, Marseille, France
| | - Gaëtan Macgrogan
- Department of Biopathology, Institut Bergonié, Regional Comprehensive Cancer Centre, Bordeaux, France; Inserm U1218, Bordeaux Public Health, University of Bordeaux, Bordeaux, France
| | - Xavier Muracciole
- COMPO, Inria Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 13385 Marseille, France; Radiotherapy Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France
| | - Sébastien Benzekry
- COMPO, Inria Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 13385 Marseille, France
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Barlesi F, Greillier L, Monville F, Audigier Valette C, Martinez S, Cloarec N, Van Hulst S, Odier L, Vely F, Juquel L, Arnaud L, Bokobza S, Hamimed M, Karlsen M, Dufosse P, Pouchin A, Ghezali L, Le Ray M, Fieschi-Meric J, Benzekry S. 3MO Comprehensive biomarkers (BMS) analysis to predict efficacy of PD1/L1 immune checkpoint inhibitors (ICIs) in combination with chemotherapy: A subgroup analysis of the precision immuno-oncology for advanced non-small cell lung cancer (pioneer) trial. Immuno-Oncology and Technology 2022. [DOI: 10.1016/j.iotech.2022.100108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Bigarré C, Finetti P, Bertucci F, Muracciole X, Benzekry S. Abstract 2737: A mechanistic model for prediction of metastatic relapse in early-stage breast cancer using routine clinical features. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-2737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Estimation of the risk of metastatic relapse is a major challenge to decide treatment options for early-stage breast cancer patients. To date, metastasis free survival (MFS) analysis mainly relies on classical - agnostic - statistical models (e.g., Cox regression). Instead, we propose to derive mechanistic models to predict MFS.
The data consisted of patients who did not receive adjuvant systemic therapy from two databases: one (data 1, N = 163) with routine clinical features and the PAI-1 and uPA biomarkers and two (data 2, N = 692) with 11 routine clinical features. The mathematical models are based on a partial differential equation describing a size-structured population of metastases. They predict MFS from the size of the tumor at diagnosis and two mathematical parameters, α and μ describing respectively the tumor growth speed and metastatic dissemination potential. Using mixed-effects modeling, the population distributions of α and μ were assumed to be lognormal and to depend on routine clinical variables, whereas the observation error was assumed lognormal on the time-to-relapse. Variable selection consisted first in a univariate Wald test for all covariates with effect either on α or μ. We then used a backward elimination procedure. Significance of the covariates in the final model was assessed by a multivariable Wald test. Concordance indexes (c-index) were computed to assess the predictive power in cross-validation procedures as well as test sets.
The model was implemented as an R package using optimized C++ code for the mechanistic part and enabling parallelization, resulting in a 4-fold reduction of the computing compared to a former python implementation. Nevertheless, over 4 hours are still needed to run a 100 samples bootstrap on a distributed computing cluster. The model selection procedure on data 1 revealed an association of PAI1 and age with μ, and of Estrogen receptor level with alpha, consistent with the established biological link between PAI-1 and tumor invasiveness. The resulting model achieved good prediction performances with a c-index of 0.72 in 5 folds cross-validation. Using only routine clinical markers, the algorithm on data 2 selected a model with the grade and Progesterone Receptors in α, Ki67 and node status in μ. These were able to adequately describe the data on calibration plots but performed poorly in prediction with a cross validated c-index of 0.60.
Mechanistic modeling was able to unravel the biological and predictive role of PAI-1 but showed limited predictive power when using only on routine clinical features.
Citation Format: Célestin Bigarré, Pascal Finetti, François Bertucci, Xavier Muracciole, Sébastien Benzekry. A mechanistic model for prediction of metastatic relapse in early-stage breast cancer using routine clinical features [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2737.
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Vaghi C, Fanciullino R, Benzekry S, Poignard C. Macro-scale models for fluid flow in tumour tissues: impact of microstructure properties. J Math Biol 2022; 84:27. [DOI: 10.1007/s00285-022-01719-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/04/2022] [Accepted: 01/20/2022] [Indexed: 11/28/2022]
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Deyme L, Benzekry S, Ciccolini J. Mechanistic models for hematological toxicities: Small is beautiful. CPT Pharmacometrics Syst Pharmacol 2021; 10:396-398. [PMID: 33638917 PMCID: PMC8129710 DOI: 10.1002/psp4.12590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/09/2020] [Accepted: 12/14/2020] [Indexed: 12/18/2022]
Affiliation(s)
- Laure Deyme
- SMARTc Unit, Centre for Research in Cancer of Marseille, Inserm U1068, Aix Marseille Univ, Marseille, France
| | - Sébastien Benzekry
- Monc Team, INRIA Bordeaux Sud Ouest Institute for Mathematics of Bordeaux, CNRS, UMR 5251, Bordeaux University, Talence, France
| | - Joseph Ciccolini
- SMARTc Unit, Centre for Research in Cancer of Marseille, Inserm U1068, Aix Marseille Univ, Marseille, France
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Benzekry S, Sentis C, Coze C, Tessonnier L, André N. Development and Validation of a Prediction Model of Overall Survival in High-Risk Neuroblastoma Using Mechanistic Modeling of Metastasis. JCO Clin Cancer Inform 2021; 5:81-90. [PMID: 33439729 DOI: 10.1200/cci.20.00092] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Prognosis of high-risk neuroblastoma (HRNB) remains poor despite multimodal therapies. Better prediction of survival could help to refine patient stratification and better tailor treatments. We established a mechanistic model of metastasis in HRNB relying on two processes: growth and dissemination relying on two patient-specific parameters: the dissemination rate μ and the minimal visible lesion size Svis. This model was calibrated using diagnosis values of primary tumor size, lactate dehydrogenase circulating levels, and the meta-iodobenzylguanidine International Society for Paediatric Oncology European (SIOPEN) score from nuclear imaging, using data from 49 metastatic patients. It was able to describe the data of total tumor mass (lactate dehydrogenase, R2 > 0.99) and number of visible metastases (SIOPEN, R2 = 0.96). A prediction model of overall survival (OS) was then developed using Cox regression. Clinical variables alone were not able to generate a model with sufficient OS prognosis ability (P = .507). The parameter μ was found to be independent of the clinical variables and positively associated with OS (P = .0739 in multivariable analysis). Critically, addition of this computational biomarker significantly improved prediction of OS with a concordance index increasing from 0.675 (95% CI, 0.663 to 0.688) to 0.733 (95% CI, 0.722 to 0.744, P < .0001), resulting in significant OS prognosis ability (P = .0422).
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Affiliation(s)
- Sébastien Benzekry
- MONC Team, Inria Bordeaux Sud-Ouest and Institut de Mathématiques de Bordeaux, CNRS, University of Bordeaux, Bordeaux, France
| | - Coline Sentis
- Paediatric Hematology and Oncology Department, Hôpital pour enfant de La Timone, AP-HM, Marseille, France
| | - Carole Coze
- Paediatric Hematology and Oncology Department, Hôpital pour enfant de La Timone, AP-HM, Marseille, France.,Aix Marseille University, Marseille, France
| | - Laëtitia Tessonnier
- Department of Nuclear Medicine, Hôpital de La Timone, AP-HM, Marseille, France
| | - Nicolas André
- Paediatric Hematology and Oncology Department, Hôpital pour enfant de La Timone, AP-HM, Marseille, France.,SMARTc Unit, Centre de Recherche en Cancérologie de Marseille, Inserm U1068, Aix Marseille University, Marseille, France
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Ciccolini J, Barbolosi D, André N, Barlesi F, Benzekry S. Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators? JCO Precis Oncol 2020; 4:486-491. [DOI: 10.1200/po.19.00381] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Joseph Ciccolini
- SMARTc Unit, Centre de Recherche en Cancérologie de Marseille Inserm U1068, Aix Marseille Université, Marseille, France
| | - Dominique Barbolosi
- SMARTc Unit, Centre de Recherche en Cancérologie de Marseille Inserm U1068, Aix Marseille Université, Marseille, France
| | - Nicolas André
- SMARTc Unit, Centre de Recherche en Cancérologie de Marseille Inserm U1068, Aix Marseille Université, Marseille, France
- Pediatric Hematology and Oncology Department, Hôpital Pour Enfant de La Timone, Assistance Publique–Hôpitaux de Marseille, Marseille, France
| | | | - Sébastien Benzekry
- MONC Team, INRIA Bordeaux Sud-Ouest and Institut de Mathématiques de Bordeaux, CNRS UMR5251, Talence, France
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Nicolò C, Périer C, Prague M, Bellera C, MacGrogan G, Saut O, Benzekry S. Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer. JCO Clin Cancer Inform 2020; 4:259-274. [PMID: 32213092 DOI: 10.1200/cci.19.00133] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for time to distant metastatic relapse. METHODS The data we used for our model consisted of 642 patients with 21 clinicopathologic variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter α) and dissemination (parameter μ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of five covariates with the best predictive power. These were further considered to individually predict the model parameters by using a backward selection approach. Predictive performances were compared with classic Cox regression and machine learning algorithms. RESULTS The mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with α (P = .001) and EGFR expression with μ (P = .009). The model achieved a c-index of 0.65 (95% CI, 0.60 to 0.71) in cross-validation and had predictive performance similar to that of random survival forest (95% CI, 0.66 to 0.69) and Cox regression (95% CI, 0.62 to 0.67) as well as machine learning classification algorithms. CONCLUSION By providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool for routine management of patients with breast cancer.
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Affiliation(s)
- Chiara Nicolò
- Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France.,Institut de Mathématiques de Bordeaux, UMR 5251, CNRS, Bordeaux, France
| | - Cynthia Périer
- Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France.,Institut de Mathématiques de Bordeaux, UMR 5251, CNRS, Bordeaux, France
| | - Melanie Prague
- Statistics in Systems Biology and Translational Medicine Team, Inria Bordeaux Sud-Ouest, University of Bordeaux, Bordeaux, France.,INSERM U1219, Bordeaux Public Health, University of Bordeaux, Bordeaux, France
| | - Carine Bellera
- INSERM U1219, Bordeaux Public Health, University of Bordeaux, Bordeaux, France.,Department of Clinical Epidemiology and Clinical Research, Institut Bergonié, Regional Comprehensive Cancer Centre, Bordeaux, France
| | - Gaëtan MacGrogan
- Department of Biopathology, Institut Bergonié, Regional Comprehensive Cancer Centre, Bordeaux, France.,INSERM U1218, Bordeaux Public Health, University of Bordeaux, Bordeaux, France
| | - Olivier Saut
- Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France.,Institut de Mathématiques de Bordeaux, UMR 5251, CNRS, Bordeaux, France
| | - Sébastien Benzekry
- Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France.,Institut de Mathématiques de Bordeaux, UMR 5251, CNRS, Bordeaux, France
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11
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Sicard G, Rodallec A, Correard F, Vaghi C, Poignard C, Ciccolini J, Benzekry S, Sergé A, Fanciullino R. Abstract 6244: Turning poorly vascularized tumors into highly vascularized tumors with nanoparticles: Proof of concept and pharmacometric analysis. Cancer Res 2020. [DOI: 10.1158/1538-7445.am2020-6244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Combinatorial regimen are a mainstay in oncology. Taxanes and trastuzumab are both gold standards for HER2 positive breast cancer. To what extent they could reshape tumor micro-environment remains to be elucidated. The aim of this study was to compare in breast cancer mice models the antiproliferative efficacy and impact on tumor neo-angiogenesis of trastuzumab stealth immunoliposomes encapsulating docetaxel and stealth liposomal docetaxel associated with free trastuzumab. Nude mice bearing human MDA-MB-231 breast cancer tumors were treated by saline (control), docetaxel liposome (5mg/kg) associated with free trastuzumab (1.9 mg/kg) or trastuzumab immunoliposome (eq. 5 mg/kg docetaxel and 1.9 mg/kg trastuzumab) QW for 4 consecutive weeks. Tumor growth was monitored twice a week by volumetric measurement using a caliper. Blodd vessels density was evaluated in vivo by fluorescence imaging using Angiosense as a vascular probe plus ex-vivo using anti-CD31 antibodies and electronic microscopy at 2, 4 and 6 weeks. A specific algorithm was developed on MatLab for imaging analysis. No treatment-induced toxicities was observed. After 4 weeks of dosing, no difference in efficacy was seen between treatments (p=0.798) and Angiosense imaging did not show differences either (p=0.628). However electronic microscopy showed that both immunoliposomes and liposomes increased tumor vascular density as compared with control. In addition, a difference between treatments was evidenced because a marked increase in vascularization of central tumors VS. peripheral tumors was observed with trastuzumab immunoliposome as compared with standard docetaxel liposome (p=0.00023). A numerical model based on porous medium theory combined with multi-scale approaches has been derived to describe the spatial distribution of the drugs within tissues. Overall our data suggest that trastuzumab stealth immunoliposomes induce a normalization of tumor vessels with an increase in blood vessels density in central tumors. This could pave the way for sequencing combinatorial strategies so as to improve drug delivery into tumors of the associated treatments.
Citation Format: Guillaume Sicard, Anne Rodallec, Florian Correard, Cristina Vaghi, Clair Poignard, Joseph Ciccolini, Sébastien Benzekry, Arnauld Sergé, Raphaelle Fanciullino. Turning poorly vascularized tumors into highly vascularized tumors with nanoparticles: Proof of concept and pharmacometric analysis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6244.
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Affiliation(s)
| | - Anne Rodallec
- 1SMARTc CRCM Aix-Marseille University, Marseille, France
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12
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Ciccolini J, Benzekry S, Barlesi F. Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC. Br J Cancer 2020; 123:337-338. [PMID: 32541872 PMCID: PMC7403333 DOI: 10.1038/s41416-020-0918-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/02/2020] [Accepted: 05/13/2020] [Indexed: 12/12/2022] Open
Abstract
This project aims to generate dense longitudinal data in lung cancer patients undergoing anti-PD1/PDL1 therapy. Mathematical modelling with mechanistic learning algorithms will help decipher the mechanisms underlying the response or resistance to immunotherapy. A better understanding of these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors.
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Affiliation(s)
- Joseph Ciccolini
- Centre de Recherche en Cancérologie de Marseille, SMARTc, Aix-Marseille Université, Inserm U1068, CNRS UMR 7258, Institut Paoli Calmettes, 13009, Marseille, France.
- Assistance Publique Hôpitaux de Marseille, Marseille, France.
| | - Sébastien Benzekry
- MONC Team, Institut National de Recherche en Informatique Appliquée Bordeaux Sud Ouest and Institut Mathématique de Bordeaux, CNRS UMR 5251, Bordeaux, France
| | - Fabrice Barlesi
- Centre de Recherche en Cancérologie de Marseille, Aix-Marseille Université, Inserm U1068, CNRS UMR 7258, Institut Paoli Calmettes, 13009, Marseille, France
- Gustave Roussy Cancer Campus, Villejuif, France
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13
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Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, Mastri M, Poignard C, Ebos JML, Benzekry S. Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors. PLoS Comput Biol 2020; 16:e1007178. [PMID: 32097421 PMCID: PMC7059968 DOI: 10.1371/journal.pcbi.1007178] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 03/06/2020] [Accepted: 01/06/2020] [Indexed: 12/14/2022] Open
Abstract
Tumor growth curves are classically modeled by means of ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model, which could be used to reduce the dimensionality and improve predictive power. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed the simultaneous modeling of tumor dynamics and inter-animal variability. Experimental data comprised three animal models of breast and lung cancers, with 833 measurements in 94 animals. Candidate models of tumor growth included the exponential, logistic and Gompertz models. The exponential and-more notably-logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The previously reported population-level correlation between the Gompertz parameters was further confirmed in our analysis (R2 > 0.92 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a reduced Gompertz function consisting of a single individual parameter (and one population parameter). Leveraging the population approach using Bayesian inference, we estimated times of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using Bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy (prediction error) was 12.2% versus 78% and mean precision (width of the 95% prediction interval) was 15.6 days versus 210 days, for the breast cancer cell line. These results demonstrate the superior predictive power of the reduced Gompertz model, especially when combined with Bayesian estimation. They offer possible clinical perspectives for personalized prediction of the age of a tumor from limited data at diagnosis. The code and data used in our analysis are publicly available at https://github.com/cristinavaghi/plumky.
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Affiliation(s)
- Cristina Vaghi
- MONC team, Inria Bordeaux Sud-Ouest, Talence, France
- Institut de Mathématiques de Bordeaux, CNRS UMR 5251, Bordeaux University, Talence, France
| | - Anne Rodallec
- SMARTc Unit, Centre de Recherche en Cancérologie de Marseille, Inserm U1068, Aix Marseille Université, Marseille, France; Laboratoire de Pharmacocinétique et Toxicologie, La Timone University Hospital of Marseille, Marseille, France
| | - Raphaëlle Fanciullino
- SMARTc Unit, Centre de Recherche en Cancérologie de Marseille, Inserm U1068, Aix Marseille Université, Marseille, France; Laboratoire de Pharmacocinétique et Toxicologie, La Timone University Hospital of Marseille, Marseille, France
| | - Joseph Ciccolini
- SMARTc Unit, Centre de Recherche en Cancérologie de Marseille, Inserm U1068, Aix Marseille Université, Marseille, France; Laboratoire de Pharmacocinétique et Toxicologie, La Timone University Hospital of Marseille, Marseille, France
| | - Jonathan P. Mochel
- Department of Biomedical Sciences, College of Veterinary Medicine, Iowa State University, Ames, Iowa, United States of America
| | - Michalis Mastri
- Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America
| | - Clair Poignard
- MONC team, Inria Bordeaux Sud-Ouest, Talence, France
- Institut de Mathématiques de Bordeaux, CNRS UMR 5251, Bordeaux University, Talence, France
| | - John M. L. Ebos
- Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America
- Departments of Medicine and Experimental Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America
| | - Sébastien Benzekry
- MONC team, Inria Bordeaux Sud-Ouest, Talence, France
- Institut de Mathématiques de Bordeaux, CNRS UMR 5251, Bordeaux University, Talence, France
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14
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Ciccolini J, Barbolosi D, André N, Benzekry S, Barlesi F. Combinatorial immunotherapy strategies: most gods throw dice, but fate plays chess. Ann Oncol 2019; 30:1690-1691. [PMID: 31504149 DOI: 10.1093/annonc/mdz297] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- J Ciccolini
- SMARTc, Cancer Research Center of Marseille, Inserm U1068, Aix-Marseille University, Marseille.
| | - D Barbolosi
- SMARTc, Cancer Research Center of Marseille, Inserm U1068, Aix-Marseille University, Marseille
| | - N André
- SMARTc, Cancer Research Center of Marseille, Inserm U1068, Aix-Marseille University, Marseille; Pediatric Hematology and Oncology Department, La Timone University Hospital of Marseille, Marseille
| | - S Benzekry
- MONC Team, INRIA, Bordeaux Institute of Mathematics, Bordeaux
| | - F Barlesi
- INCa-labelled Early-Phases Center (CLIP2), La Timone University Hospital of Marseille, Marseille, France
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15
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Bilous M, Serdjebi C, Boyer A, Tomasini P, Pouypoudat C, Barbolosi D, Barlesi F, Chomy F, Benzekry S. Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer. Sci Rep 2019; 9:13018. [PMID: 31506498 PMCID: PMC6736889 DOI: 10.1038/s41598-019-49407-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 08/23/2019] [Indexed: 12/25/2022] Open
Abstract
Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patient-specific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC. Primary tumor growth was best described by a gompertzian model for the pre-diagnosis history, followed by a tumor growth inhibition model during treatment. Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1–5.3 years). Multiple metastatic models were further assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4–5.7 months and onset of BMs 14–19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.
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Affiliation(s)
- M Bilous
- MONC team, Inria Bordeaux Sud-Ouest, Talence, France.,Institut de Mathématiques de Bordeaux, Bordeaux University, Talence, France
| | - C Serdjebi
- SMARTc Unit, Center for Research on Cancer of Marseille (CRCM), Inserm UMR 1068, CNRS UMR 7258, Aix-Marseille University U105, Marseille, France
| | - A Boyer
- SMARTc Unit, Center for Research on Cancer of Marseille (CRCM), Inserm UMR 1068, CNRS UMR 7258, Aix-Marseille University U105, Marseille, France.,Multidisciplinary Oncology and Therapeutic Innovations Department and CRCM, Inserm UMR 1068, CNRS UMR 7258, Assistance Publique Hôpitaux de Marseille, Aix Marseille University, Marseille, France
| | - P Tomasini
- Multidisciplinary Oncology and Therapeutic Innovations Department and CRCM, Inserm UMR 1068, CNRS UMR 7258, Assistance Publique Hôpitaux de Marseille, Aix Marseille University, Marseille, France
| | - C Pouypoudat
- Radiation oncology department, Haut-Lévêque Hospital, Pessac, France
| | - D Barbolosi
- SMARTc Unit, Center for Research on Cancer of Marseille (CRCM), Inserm UMR 1068, CNRS UMR 7258, Aix-Marseille University U105, Marseille, France
| | - F Barlesi
- SMARTc Unit, Center for Research on Cancer of Marseille (CRCM), Inserm UMR 1068, CNRS UMR 7258, Aix-Marseille University U105, Marseille, France.,Multidisciplinary Oncology and Therapeutic Innovations Department and CRCM, Inserm UMR 1068, CNRS UMR 7258, Assistance Publique Hôpitaux de Marseille, Aix Marseille University, Marseille, France
| | - F Chomy
- Clinical oncology department, Institut Bergonié, Bordeaux, France
| | - S Benzekry
- MONC team, Inria Bordeaux Sud-Ouest, Talence, France. .,Institut de Mathématiques de Bordeaux, Bordeaux University, Talence, France.
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16
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Rodallec A, Sicard G, Fanciullino R, Benzekry S, Lacarelle B, Milano G, Ciccolini J. Turning cold tumors into hot tumors: harnessing the potential of tumor immunity using nanoparticles. Expert Opin Drug Metab Toxicol 2018; 14:1139-1147. [PMID: 30354685 DOI: 10.1080/17425255.2018.1540588] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
INTRODUCTION Immune checkpoint inhibitors have considerably changed the landscape of oncology. However apart from world-acclaimed success stories limited to melanoma and lung cancer, many solid tumors failed to respond to immune checkpoint inhibitors due to limited immunogenicity, unfavorable tumor micro-environments (TME), lack of infiltrating T lymphocytes or increases in Tregs. Areas covered: Combinatorial strategies are foreseen as the future of immunotherapy and using cytotoxics or modulating agents is expected to boost the efficacy of immune checkpoint inhibitors. In this respect, nanoparticles displaying unique pharmacokinetic features such as tumor targeting properties, optimal payload delivery and long-lasting interferences with TME, are promising candidates for such combinations. This review covers the basis, expectancies, limits and pitfalls of future combination between nanoparticles and immune check point inhibitors. Expert opinion: Nanoparticles allow optimal delivery of variety of payloads in tumors while sparing healthy tissue, thus triggering immunogenic cell death. Depleting tumor stroma could further help immune cells and monoclonal antibodies to better circulate in the TME, plus immune-modulating properties of the charged cytotoxics. Finally, nanoparticles themselves present immunogenicity and antigenicity likely to boost immune response at the tumor level.
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Affiliation(s)
- Anne Rodallec
- a SMARTc Unit, Centre de Recherche en Cancérologie de Marseille UMR Inserm U1068 , Aix Marseille University , Marseille , France
| | - Guillaume Sicard
- a SMARTc Unit, Centre de Recherche en Cancérologie de Marseille UMR Inserm U1068 , Aix Marseille University , Marseille , France
| | - Raphaelle Fanciullino
- a SMARTc Unit, Centre de Recherche en Cancérologie de Marseille UMR Inserm U1068 , Aix Marseille University , Marseille , France
| | | | - Bruno Lacarelle
- a SMARTc Unit, Centre de Recherche en Cancérologie de Marseille UMR Inserm U1068 , Aix Marseille University , Marseille , France
| | - Gerard Milano
- c EA666 Oncopharmacology Unit , Centre Antoine Lacassagne , Nice , France
| | - Joseph Ciccolini
- a SMARTc Unit, Centre de Recherche en Cancérologie de Marseille UMR Inserm U1068 , Aix Marseille University , Marseille , France
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17
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Nicolò C, Mastri M, Tracz A, Ebos JM, Benzekry S. Abstract 4264: Mathematical modeling of differential effects of sunitinib on primary tumor and metastatic growth. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-4264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Sunitinib is a drug with anti-angiogenic activity used in the treatment of patients with metastases from renal cell carcinoma or gastrointestinal tumors. However, despite clear efficacy in reducing established tumor growth, recent preclinical studies have shown limited, or even opposing, efficacies in preventing metastatic spread.
In this work, we evaluated a mathematical metastatic model to describe primary tumor and metastatic dynamics in response to sunitinib in a clinically relevant mouse model of spontaneous metastatic breast cancer that develops after surgical removal of an orthotopically implanted primary tumor. Mice received either vehicle or sunitinib in the neoadjuvant (presurgical) setting according to different schedules. The experimental dataset comprises measurements of primary tumor kinetics, metastatic burden and pre-surgical molecular and cellular biomarkers, including vascular cell Ki67 and CD31 expression, circulating tumor cells (CTCs) and myeloid derived suppressor cells (MDSCs). Estimation of the model's parameters was performed using a mixed-effects population approach. To describe tumor growth under sunitinib treatment, a simple ordinary differential equation model of tumor growth inhibition was used.
Population fits obtained modeling the effect of treatment only on primary tumor growth described well the experimental data of all the treated groups considered. On the contrary, simulations of treatment also on metastasis could not reproduce the behavior of the data. By inserting in the model the available biomarkers as covariates, through a Wald-test, measurements of Ki67+/CD31+, CTCs and granulocytic MDSCs were found significantly correlated with the model parameter expressing the metastatic aggressiveness of the tumor.
Together, these mathematical models confirm a differential effect of sunitinib on primary (localized) tumors compared to secondary (metastatic) disease. Our results suggest that Ki67+/CD31+, CTCs and MDSCs measurements might help in predicting metastatic potential and thus aid in predicting benefit in overall survival for preoperative antiangiogenic treatments.
Citation Format: Chiara Nicolò, Michalis Mastri, Amanda Tracz, John M. Ebos, Sébastien Benzekry. Mathematical modeling of differential effects of sunitinib on primary tumor and metastatic growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4264.
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Affiliation(s)
- Chiara Nicolò
- 1Inria team MONC, Institut de Mathematiques de Bordeaux, Bordeaux, France
| | - Michalis Mastri
- 2Department of Cancer Genetics and Medicine, Roswell Park Cancer Institute, Buffalo, NY
| | - Amanda Tracz
- 2Department of Cancer Genetics and Medicine, Roswell Park Cancer Institute, Buffalo, NY
| | - John M. Ebos
- 2Department of Cancer Genetics and Medicine, Roswell Park Cancer Institute, Buffalo, NY
| | - Sébastien Benzekry
- 1Inria team MONC, Institut de Mathematiques de Bordeaux, Bordeaux, France
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18
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Imbs DC, El Cheikh R, Boyer A, Ciccolini J, Mascaux C, Lacarelle B, Barlesi F, Barbolosi D, Benzekry S. Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma. CPT Pharmacometrics Syst Pharmacol 2017; 7:42-50. [PMID: 29218795 PMCID: PMC5784740 DOI: 10.1002/psp4.12265] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 10/25/2017] [Accepted: 10/30/2017] [Indexed: 12/20/2022]
Abstract
Concomitant administration of bevacizumab and pemetrexed‐cisplatin is a common treatment for advanced nonsquamous non‐small cell lung cancer (NSCLC). Vascular normalization following bevacizumab administration may transiently enhance drug delivery, suggesting improved efficacy with sequential administration. To investigate optimal scheduling, we conducted a study in NSCLC‐bearing mice. First, experiments demonstrated improved efficacy when using sequential vs. concomitant scheduling of bevacizumab and chemotherapy. Combining this data with a mathematical model of tumor growth under therapy accounting for the normalization effect, we predicted an optimal delay of 2.8 days between bevacizumab and chemotherapy. This prediction was confirmed experimentally, with reduced tumor growth of 38% as compared to concomitant scheduling, and prolonged survival (74 vs. 70 days). Alternate sequencing of 8 days failed in achieving a similar increase in efficacy, thus emphasizing the utility of modeling support to identify optimal scheduling. The model could also be a useful tool in the clinic to personally tailor regimen sequences.
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Affiliation(s)
| | - Raouf El Cheikh
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France
| | - Arnaud Boyer
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France.,Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Joseph Ciccolini
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France
| | - Céline Mascaux
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France.,Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Bruno Lacarelle
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France
| | - Fabrice Barlesi
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France.,Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique Hôpitaux de Marseille, Marseille, France
| | | | - Sébastien Benzekry
- MONC Team, Inria Bordeaux Sud-Ouest, Talence, France.,Institut de Mathématiques de Bordeaux, UMR 5251, University of Bordeaux and CNRS, Talence, France
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Henares-Molina A, Benzekry S, Lara P, García-Rojo M, Pérez-García V, Martínez-González A. OS06.6 Optimized radiotherapy protocols delay the malignant transformation of low-grade gliomas in-silico. Neuro Oncol 2017. [DOI: 10.1093/neuonc/nox036.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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20
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Boyer A, Ciccolini J, Mascaux C, Imbs D, El Cheikh R, Barlesi F, Benzekry S. Étude de l’effet séquence bévacizumab/pémétrexed/cisplatine chez la souris porteuse de cancer du poumon non à petites cellules. Rev Mal Respir 2017. [DOI: 10.1016/j.rmr.2016.10.110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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21
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Pantziarka P, Hutchinson L, André N, Benzekry S, Bertolini F, Bhattacharjee A, Chiplunkar S, Duda DG, Gota V, Gupta S, Joshi A, Kannan S, Kerbel R, Kieran M, Palazzo A, Parikh A, Pasquier E, Patil V, Prabhash K, Shaked Y, Sholler GS, Sterba J, Waxman DJ, Banavali S. Next generation metronomic chemotherapy-report from the Fifth Biennial International Metronomic and Anti-angiogenic Therapy Meeting, 6-8 May 2016, Mumbai. Ecancermedicalscience 2016; 10:689. [PMID: 27994645 PMCID: PMC5130328 DOI: 10.3332/ecancer.2016.689] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Indexed: 12/31/2022] Open
Abstract
The 5th Biennial Metronomic and Anti-angiogenic Therapy Meeting was held on 6th – 8th May in the Indian city of Mumbai. The meeting brought together a wide range of clinicians and researchers interested in metronomic chemotherapy, anti-angiogenics, drug repurposing and combinations thereof. Clinical experiences, including many from India, were reported and discussed in three symposia covering breast cancer, head and neck cancers and paediatrics. On the pre-clinical side research into putative mechanisms of action, and the interactions between low dose metronomic chemotherapy and angiogenesis and immune responses, were discussed in a number of presentations. Drug repurposing was discussed both in terms of clinical results, particularly with respect to angiosarcoma and high-risk neuroblastoma, and in pre-clinical settings, particularly the potential for peri-operative interventions. However, it was clear that there remain a number of key areas of challenge, particularly in terms of definitions, perceptions in the wider oncological community, mechanisms of action and predictive biomarkers. While the potential for metronomics and drug repurposing in low and middle income countries remains a key theme, it is clear that there is also considerable potential for clinically relevant improvements in patient outcomes even in high income economies.
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Affiliation(s)
- Pan Pantziarka
- Anticancer Fund, Brussels, 1853 Strombeek-Bever, Belgium; The George Pantziarka TP53 Trust, London, UK
| | | | - Nicolas André
- Service d'hématologie et Oncologie Pédiatrique, Centre Hospitalo-Universitaire Timone Enfants, AP-HM, Aix-Marseille Université, INSERM, CRO2 UMR_S 911, Marseille, France; Metronomics Global Health Initiative, Marseille, France
| | - Sébastien Benzekry
- Inria team MONC and Institut de Mathématiques de Bordeaux, Talence, France
| | | | | | | | - Dan G Duda
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Vikram Gota
- ACTREC, Tata Memorial Centre, Kharghar, Navi Mumbai 410210, India
| | - Sudeep Gupta
- ACTREC, Tata Memorial Centre, Kharghar, Navi Mumbai 410210, India
| | | | - Sadhana Kannan
- ACTREC, Tata Memorial Centre, Kharghar, Navi Mumbai 410210, India
| | - Robert Kerbel
- Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Mark Kieran
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Antonella Palazzo
- Division of Medical Senology, European Institute of Oncology, Via Ripamonti 435, 20141, Milan, Italy
| | | | - Eddy Pasquier
- INSERM UMR 911, Centre de Recherche en Oncologie Biologique et Oncopharmacologie, Aix-Marseille University, Marseille, France; Metronomics Global Health Initiative, Marseille, France
| | | | | | - Yuval Shaked
- Department of Cell Biology and Cancer Science, Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | | | - Jaroslav Sterba
- Department of Pediatric Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Cernopolni 9, 613 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital and RECAMO, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - David J Waxman
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Shripad Banavali
- Tata Memorial Hospital, Mumbai, India; Metronomics Global Health Initiative, Marseille, France
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Beheshti A, Benzekry S, McDonald JT, Ma L, Peluso M, Hahnfeldt P, Hlatky L. Host age is a systemic regulator of gene expression impacting cancer progression. Cancer Res 2015; 75:1134-43. [PMID: 25732382 DOI: 10.1158/0008-5472.can-14-1053] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Aging is the major determinant of cancer incidence, which, in turn, is likely dictated in large part by processes that influence the progression of early subclinical (occult) cancers. However, there is little understanding of how aging informs changes in aggregate host signaling that favor cancer progression. In this study, we provide direct evidence that aging can serve as an organizing axis to define cancer progression-modulating processes. As a model system to explore this concept, we employed adolescent (68 days), young adult (143 days), middle-aged (551 days), and old (736 days) C57BL/6 mice as syngeneic hosts for engraftment of Lewis lung cancer to identify signaling and functional processes varying with host age. Older hosts exhibited dysregulated angiogenesis, metabolism, and apoptosis, all of which are associated with cancer progression. TGFβ1, a central player in these systemic processes, was downregulated consistently in older hosts. Our findings directly supported the conclusion of a strong host age dependence in determining the host tumor control dynamic. Furthermore, our results offer initial mechanism-based insights into how aging modulates tumor progression in ways that may be actionable for therapy or prevention.
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Affiliation(s)
- Afshin Beheshti
- Center of Cancer Systems Biology, Tufts University School of Medicine, Boston, Massachusetts
| | - Sébastien Benzekry
- Center of Cancer Systems Biology, Tufts University School of Medicine, Boston, Massachusetts. INRIA Bordeaux Sud-Ouest MC2, Talence, France
| | - J Tyson McDonald
- Center of Cancer Systems Biology, Tufts University School of Medicine, Boston, Massachusetts
| | - Lili Ma
- Center of Cancer Systems Biology, Tufts University School of Medicine, Boston, Massachusetts
| | - Michael Peluso
- Center of Cancer Systems Biology, Tufts University School of Medicine, Boston, Massachusetts
| | - Philip Hahnfeldt
- Center of Cancer Systems Biology, Tufts University School of Medicine, Boston, Massachusetts
| | - Lynn Hlatky
- Center of Cancer Systems Biology, Tufts University School of Medicine, Boston, Massachusetts.
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Benzekry S, Lamont C, Beheshti A, Tracz A, Ebos JML, Hlatky L, Hahnfeldt P. Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol 2014; 10:e1003800. [PMID: 25167199 PMCID: PMC4148196 DOI: 10.1371/journal.pcbi.1003800] [Citation(s) in RCA: 252] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Accepted: 07/08/2014] [Indexed: 01/03/2023] Open
Abstract
Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic. Tumor growth curves display relatively simple time curves that can be quantified using mathematical models. Herein we exploited two experimental animal systems to assess the descriptive and predictive power of nine classical tumor growth models. Several goodness-of-fit metrics and a dedicated error model were employed to rank the models for their relative descriptive power. We found that the model with the highest descriptive power was not necessarily the most predictive one. The breast growth curves had a linear profile that allowed good predictability. Conversely, not one of the models was able to accurately predict the lung growth curves when using only a few data points. To overcome this issue, we considered a method that uses the parameter population distribution, informed from a priori knowledge, to estimate the individual parameter vector of an independent growth curve. This method was found to considerably improve the prediction success rates. These findings may benefit preclinical cancer research by identifying models most descriptive of fundamental growth characteristics. Clinical perspective is also offered on what can be expected from mathematical modeling in terms of future growth prediction.
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Affiliation(s)
- Sébastien Benzekry
- Inria Bordeaux Sud-Ouest, Institut de Mathématiques de Bordeaux, Bordeaux, France
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
- * E-mail:
| | - Clare Lamont
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Afshin Beheshti
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Amanda Tracz
- Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York, United States of America
| | - John M. L. Ebos
- Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York, United States of America
| | - Lynn Hlatky
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Philip Hahnfeldt
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
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Abstract
Autopsy studies of adults dying of non-cancer causes have shown that virtually all of us possess occult, cancerous lesions. This suggests that, for most individuals, cancer will become dormant and not progress, while only in some will it become symptomatic disease. Meanwhile, it was recently shown in animal models that a tumor can produce both stimulators and inhibitors of its own blood supply. To explain the autopsy findings in light of the preclinical research data, we propose a mathematical model of cancer development at the organism scale describing a growing population of metastases, which, together with the primary tumor, can exert a progressively greater level of systemic angiogenesis-inhibitory influence that eventually overcomes local angiogenesis stimulation to suppress the growth of all lesions. As a departure from modeling efforts to date, we look not just at signaling from and effects on the primary tumor, but integrate over this increasingly negative global signaling from all sources to track the development of total tumor burden. This in silico study of the dynamics of the tumor/metastasis system identifies ranges of parameter values where mutual angio-inhibitory interactions within a population of tumor lesions could yield global dormancy, i.e., an organism-level homeostatic steady state in total tumor burden. Given that mortality arises most often from metastatic disease rather than growth of the primary per se, this finding may have important therapeutic implications.
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Affiliation(s)
- Sébastien Benzekry
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
- Inria team MC2, Institut de Mathématiques de Bordeaux, Bordeaux, France
| | - Alberto Gandolfi
- Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Roma, Italy
| | - Philip Hahnfeldt
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
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25
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Benzekry S, Hahnfeldt P. Maximum tolerated dose versus metronomic scheduling in the treatment of metastatic cancers. J Theor Biol 2013; 335:235-44. [PMID: 23850479 DOI: 10.1016/j.jtbi.2013.06.036] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2013] [Revised: 05/15/2013] [Accepted: 06/27/2013] [Indexed: 10/26/2022]
Abstract
Although optimal control theory has been used for the theoretical study of anti-cancerous drugs scheduling optimization, with the aim of reducing the primary tumor volume, the effect on metastases is often ignored. Here, we use a previously published model for metastatic development to define an optimal control problem at the scale of the entire organism of the patient. In silico study of the impact of different scheduling strategies for anti-angiogenic and cytotoxic agents (either in monotherapy or in combination) is performed to compare a low-dose, continuous, metronomic administration scheme with a more classical maximum tolerated dose schedule. Simulation results reveal differences between primary tumor reduction and control of metastases but overall suggest use of the metronomic protocol.
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Affiliation(s)
- Sébastien Benzekry
- Center of Cancer Systems Biology, Steward Research & Specialty Projects Corp., St Elizabeth's Medical Center, Tufts University School of Medicine, Boston 02135, USA.
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