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Sahoo P, Yang X, Abler D, Maestrini D, Adhikarla V, Frankhouser D, Cho H, Machuca V, Wang D, Barish M, Gutova M, Branciamore S, Brown CE, Rockne RC. Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data. J R Soc Interface 2020; 17:20190734. [PMID: 31937234 PMCID: PMC7014796 DOI: 10.1098/rsif.2019.0734] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 12/12/2019] [Indexed: 01/03/2023] Open
Abstract
Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.
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Affiliation(s)
- Prativa Sahoo
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Xin Yang
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Daniel Abler
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Davide Maestrini
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Vikram Adhikarla
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - David Frankhouser
- Department of Diabetes Complications and Metabolism, City of Hope National Medical Center, Duarte, CA, USA
| | - Heyrim Cho
- Department of Mathematics, University of California, Riverside, CA, USA
| | - Vanessa Machuca
- Mathematical and Computational Systems Biology, University of California, Irvine, CA, USA
| | - Dongrui Wang
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Michael Barish
- Department of Developmental and Stem Cell Biology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA
| | - Margarita Gutova
- Department of Developmental and Stem Cell Biology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA
| | - Sergio Branciamore
- Department of Diabetes Complications and Metabolism, City of Hope National Medical Center, Duarte, CA, USA
| | - Christine E. Brown
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Russell C. Rockne
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, USA
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Budia I, Alvarez-Arenas A, Woolley TE, Calvo GF, Belmonte-Beitia J. Radiation protraction schedules for low-grade gliomas: a comparison between different mathematical models. J R Soc Interface 2019; 16:20190665. [PMID: 31822220 DOI: 10.1098/rsif.2019.0665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
We optimize radiotherapy (RT) administration strategies for treating low-grade gliomas. Specifically, we consider different tumour growth laws, both with and without spatial effects. In each scenario, we find the optimal treatment in the sense of maximizing the overall survival time of a virtual low-grade glioma patient, whose tumour progresses according to the examined growth laws. We discover that an extreme protraction therapeutic strategy, which amounts to substantially extending the time interval between RT sessions, may lead to better tumour control. The clinical implications of our results are also presented.
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Affiliation(s)
- I Budia
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - A Alvarez-Arenas
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - T E Woolley
- School of Mathematics, Cardiff University, Senghennydd Road, Cardiff CF24 4AG, UK
| | - G F Calvo
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - J Belmonte-Beitia
- Department of Mathematics and MôLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
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53
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Rayfield CA, Grady F, De Leon G, Rockne R, Carrasco E, Jackson P, Vora M, Johnston SK, Hawkins-Daarud A, Clark-Swanson KR, Whitmire S, Gamez ME, Porter A, Hu L, Gonzalez-Cuyar L, Bendok B, Vora S, Swanson KR. Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival. JCO Clin Cancer Inform 2019; 2:1-14. [PMID: 30652553 DOI: 10.1200/cci.17.00080] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. PATIENTS AND METHODS From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment. RESULTS There was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%). CONCLUSION Dichotomizing the heterogeneity of GBMs into two populations-one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.
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Affiliation(s)
- Corbin A Rayfield
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Fillan Grady
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Gustavo De Leon
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Russell Rockne
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Eduardo Carrasco
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Pamela Jackson
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Mayur Vora
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Sandra K Johnston
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Andrea Hawkins-Daarud
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Kamala R Clark-Swanson
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Scott Whitmire
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Mauricio E Gamez
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Alyx Porter
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Leland Hu
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Luis Gonzalez-Cuyar
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Bernard Bendok
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Sujay Vora
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
| | - Kristin R Swanson
- Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx Porter, Leland Hu, Bernard Bendok, Sujay Vora, and Kristin R. Swanson, Mayo Clinic, Phoenix, AZ; Russell Rockne, Beckman Research Institute, City of Hope, Duarte, CA; and Sandra K. Johnston and Luis Gonzalez-Cuyar, University of Washington, Seattle, WA
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Antonopoulos M, Dionysiou D, Stamatakos G, Uzunoglu N. Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study. BMC Bioinformatics 2019; 20:442. [PMID: 31455206 PMCID: PMC6712764 DOI: 10.1186/s12859-019-2997-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/16/2019] [Indexed: 01/10/2023] Open
Abstract
Background Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. Results This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. Conclusion In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.
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Affiliation(s)
- Markos Antonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.
| | - Dimitra Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Nikolaos Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
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55
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Lorenzo G, Pérez-García VM, Mariño A, Pérez-Romasanta LA, Reali A, Gomez H. Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome. J R Soc Interface 2019; 16:20190195. [PMID: 31409240 DOI: 10.1098/rsif.2019.0195] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
External beam radiation therapy is a widespread treatment for prostate cancer. The ensuing patient follow-up is based on the evolution of the prostate-specific antigen (PSA). Serum levels of PSA decay due to the radiation-induced death of tumour cells and cancer recurrence usually manifest as a rising PSA. The current definition of biochemical relapse requires that PSA reaches nadir and starts increasing, which delays the use of further treatments. Also, these methods do not account for the post-radiation tumour dynamics that may contain early information on cancer recurrence. Here, we develop three mechanistic models of post-radiation PSA evolution. Our models render superior fits of PSA data in a patient cohort and provide a biological justification for the most common empirical formulation of PSA dynamics. We also found three model-based prognostic variables: the proliferation rate of the survival fraction, the ratio of radiation-induced cell death rate to the survival proliferation rate, and the time to PSA nadir since treatment termination. We argue that these markers may enable the early identification of biochemical relapse, which would permit physicians to subsequently adapt patient monitoring to optimize the detection and treatment of cancer recurrence.
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Affiliation(s)
- Guillermo Lorenzo
- Dipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, Via Ferrata 3, 27100 Pavia, Italy.,Departamento de Matemáticas, Universidade da Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Edificio Politécnico, Avenida Camilo José Cela 3, 13071 Ciudad Real, Spain
| | - Alfonso Mariño
- Servicio de Oncología Radioterápica, Centro Oncológico de Galicia, Calle Doctor Camilo Veiras 1, 15009 A Coruña, Spain
| | - Luis A Pérez-Romasanta
- Servicio de Oncología Radioterápica, Hospital Universitario de Salamanca, Paseo de San Vicente 58-182, 37007 Salamanca, Spain
| | - Alessandro Reali
- Dipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Hector Gomez
- School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA.,Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907, USA.,Purdue Center for Cancer Research, Purdue University, 201 S. University Street, West Lafayette, IN 47907, USA
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56
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Yin A, Moes DJAR, van Hasselt JGC, Swen JJ, Guchelaar HJ. A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:720-737. [PMID: 31250989 PMCID: PMC6813171 DOI: 10.1002/psp4.12450] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 05/17/2019] [Indexed: 12/19/2022]
Abstract
Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.
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Affiliation(s)
- Anyue Yin
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
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57
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Johnston SK, Whitmire P, Massey SC, Kumthekar P, Porter AB, Raghunand N, Gonzalez-Cuyar LF, Mrugala MM, Hawkins-Daarud A, Jackson PR, Hu LS, Sarkaria JN, Wang L, Gatenby RA, Egan KM, Canoll P, Swanson KR. ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: A Multidisciplinary Team-based, Multifactorial Analytical Approach. Am J Clin Oncol 2019; 42:655-661. [PMID: 31343422 PMCID: PMC7416695 DOI: 10.1097/coc.0000000000000564] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Although glioblastoma (GBM) is a fatal primary brain cancer with short median survival of 15 months, a small number of patients survive >5 years after diagnosis; they are known as extreme survivors (ES). Because of their rarity, very little is known about what differentiates these outliers from other patients with GBM. For the purpose of identifying unknown drivers of extreme survivorship in GBM, the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of GBM) was developed. This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histologic parameters. Leveraging our combined resources, the goals of the ENDURES consortium are 2-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with GBM; and (2) to leverage the ENDURES repository for new insights into tumor behavior and novel targets for prolonging survival for all patients with GBM. In this article, the authors review the available literature and discuss what is already known about ES. The authors then describe the creation of their consortium and some preliminary results.
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Affiliation(s)
- Sandra K. Johnston
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
- Department of Radiology, University of Washington, Seattle, WA
| | - Paula Whitmire
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Susan Christine Massey
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Priya Kumthekar
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | | | | | - Luis F. Gonzalez-Cuyar
- Department of Pathology, Neuropathology Division, University of Washington Medical Center, Seattle, WA
| | | | - Andrea Hawkins-Daarud
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Pamela R. Jackson
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ
| | | | - Lei Wang
- Departments of Radiology & Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Robert A. Gatenby
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL
| | | | - Peter Canoll
- Division of Neuropathology, Department of Pathology and Cell Biology, Columbia University School of Medicine, New York, NY
| | - Kristin R. Swanson
- Mathematical Neuro-Oncology Laboratory, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
- Department of Neurosurgery, Mayo Clinic, Phoenix, AZ
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ
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58
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Gaw N, Hawkins-Daarud A, Hu LS, Yoon H, Wang L, Xu Y, Jackson PR, Singleton KW, Baxter LC, Eschbacher J, Gonzales A, Nespodzany A, Smith K, Nakaji P, Mitchell JR, Wu T, Swanson KR, Li J. Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Sci Rep 2019; 9:10063. [PMID: 31296889 PMCID: PMC6624304 DOI: 10.1038/s41598-019-46296-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 06/26/2019] [Indexed: 01/30/2023] Open
Abstract
Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.
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Affiliation(s)
- Nathan Gaw
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Andrea Hawkins-Daarud
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA.
| | - Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Hyunsoo Yoon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Lujia Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Yanzhe Xu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Pamela R Jackson
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Kyle W Singleton
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Jennifer Eschbacher
- Department of Pathology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Ashlyn Gonzales
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Ashley Nespodzany
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Kris Smith
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Peter Nakaji
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - J Ross Mitchell
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, 33612, USA
| | - Teresa Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Kristin R Swanson
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Jing Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
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An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.04.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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60
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Meaney C, Stastna M, Kardar M, Kohandel M. Spatial optimization for radiation therapy of brain tumours. PLoS One 2019; 14:e0217354. [PMID: 31251755 PMCID: PMC6599149 DOI: 10.1371/journal.pone.0217354] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 04/18/2019] [Indexed: 11/18/2022] Open
Abstract
Glioblastomas are the most common primary brain tumours. They are known for their highly aggressive growth and invasion, leading to short survival times. Treatments for glioblastomas commonly involve a combination of surgical intervention, chemotherapy, and external beam radiation therapy (XRT). Previous works have not only successfully modelled the natural growth of glioblastomas in vivo, but also show potential for the prediction of response to radiation prior to treatment. This suggests that the efficacy of XRT can be optimized before treatment in order to yield longer survival times. However, while current efforts focus on optimal scheduling of radiotherapy treatment, they do not include a similarly sophisticated spatial optimization. In an effort to improve XRT, we present a method for the spatial optimization of radiation profiles. We expand upon previous results in the general problem and examine the more physically reasonable cases of 1-step and 2-step radiation profiles during the first and second XRT fractions. The results show that by including spatial optimization in XRT, while retaining a constant prescribed total dose amount, we are able to increase the total cell kill from the clinically-applied uniform case.
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Affiliation(s)
- Cameron Meaney
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
| | - Marek Stastna
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
| | - Mehran Kardar
- Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America 02139
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
- * E-mail:
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Rockne RC, Hawkins-Daarud A, Swanson KR, Sluka JP, Glazier JA, Macklin P, Hormuth DA, Jarrett AM, Lima EABF, Tinsley Oden J, Biros G, Yankeelov TE, Curtius K, Al Bakir I, Wodarz D, Komarova N, Aparicio L, Bordyuh M, Rabadan R, Finley SD, Enderling H, Caudell J, Moros EG, Anderson ARA, Gatenby RA, Kaznatcheev A, Jeavons P, Krishnan N, Pelesko J, Wadhwa RR, Yoon N, Nichol D, Marusyk A, Hinczewski M, Scott JG. The 2019 mathematical oncology roadmap. Phys Biol 2019; 16:041005. [PMID: 30991381 PMCID: PMC6655440 DOI: 10.1088/1478-3975/ab1a09] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.
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Affiliation(s)
- Russell C Rockne
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA 91010, United States of America. Author to whom any correspondence should be addressed
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Ferrall-Fairbanks MC, Glazar DJ, Brady RJ, Kimmel GJ, Zahid MU, Altrock PM, Enderling H. Re: Simulation analysis for tumor radiotherapy based on three-component mathematical models. J Appl Clin Med Phys 2019; 20:204-205. [PMID: 31145529 PMCID: PMC6612696 DOI: 10.1002/acm2.12608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 04/11/2019] [Accepted: 04/13/2019] [Indexed: 01/29/2023] Open
Affiliation(s)
- Meghan C Ferrall-Fairbanks
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Daniel J Glazar
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Renee J Brady
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Gregory J Kimmel
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Mohammad U Zahid
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Philipp M Altrock
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
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Jacobs J, Rockne RC, Hawkins-Daarud AJ, Jackson PR, Johnston SK, Kinahan P, Swanson KR. Improved model prediction of glioma growth utilizing tissue-specific boundary effects. Math Biosci 2019; 312:59-66. [PMID: 31009624 DOI: 10.1016/j.mbs.2019.04.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 05/04/2018] [Accepted: 04/19/2019] [Indexed: 10/27/2022]
Abstract
Kinetic parameter estimates for mathematical models of glioblastoma multiforme (GBM), derived from clinical scans, have been used to predict the occurrence of hypoxia, necrosis, response to radiation therapy, and overall survival. Modeling GBM growth in a cerebral model encounters anatomical boundaries that interfere with model calibration from clinical measurements. METHODS The effect of boundaries is examined on both spherically symmetric and anatomical models of tumor growth. This effect is incorporated into a method that updates kinetic parameters. The efficacy of this method in reproducing clinical image-derived subject data is evaluated. RESULTS Spherically symmetric simulations of tumor growth with simple boundaries behave predictably when in a linear phase of growth. Anatomic simulations of eleven out of twenty subjects demonstrated improved fit to subject data with the new method. When only subjects exhibiting linear growth are considered, eight out of nine subject demonstrate improved fit to the data. CONCLUSION Anatomical boundaries to tumor growth measurably deflect progression and affect estimates of kinetic parameters. The presented method reliably updates kinetic parameters to fit anatomic computational models to clinically derived subject data when those data are in a linear regime.
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Affiliation(s)
- Joshua Jacobs
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA.
| | - Russell C Rockne
- Division of Mathematical Oncology, City of Hope, Duarte, CA, USA
| | | | | | | | - Paul Kinahan
- Department of Radiology, University of Washington, Seattle, WA, USA
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Sunassee ED, Tan D, Ji N, Brady R, Moros EG, Caudell JJ, Yartsev S, Enderling H. Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses. Int J Radiat Biol 2019; 95:1421-1426. [PMID: 30831050 DOI: 10.1080/09553002.2019.1589013] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Purpose: Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of non-identifiability and clinically unrealistic results. Materials and methods: We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients to predict patient-specific responses to subsequent radiation doses. Results: Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R2=0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index = 0.89). Conclusion: The PSI model may be suited to forecast treatment response for individual patients and offers actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.
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Affiliation(s)
- Enakshi D Sunassee
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Dean Tan
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Nathan Ji
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Renee Brady
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Eduardo G Moros
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA.,Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Jimmy J Caudell
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
| | - Slav Yartsev
- London Health Sciences Centre, London Regional Cancer Program , London , ON , Canada
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA.,Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute , Tampa , FL , USA
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Narasimhan S, Johnson HB, Nickles TM, Miga MI, Rana N, Attia A, Weis JA. Biophysical model-based parameters to classify tumor recurrence from radiation-induced necrosis for brain metastases. Med Phys 2019; 46:2487-2496. [PMID: 30816555 DOI: 10.1002/mp.13461] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 01/30/2019] [Accepted: 02/20/2019] [Indexed: 12/16/2022] Open
Abstract
PURPOSE Stereotactic radiosurgery (SRS) is used for local control treatment of patients with intracranial metastases. As a result of SRS, some patients develop radiation-induced necrosis. Radiographically, radiation-induced necrosis can appear similar to tumor recurrence in magnetic resonance (MR) T1 -weighted contrast-enhanced imaging, T2 -weighted MR imaging, and Fluid-Attenuated Inversion Recovery (FLAIR) MR imaging. Radiographic ambiguities often necessitate invasive brain biopsies to determine lesion etiology or cause delayed subsequent therapy initiation. We use a biomechanically coupled tumor growth model to estimate patient-specific model parameters and model-derived measures to noninvasively classify etiology of enhancing lesions in this patient population. METHODS In this initial, preliminary retrospective study, we evaluated five patients with tumor recurrence and five with radiation-induced necrosis. Longitudinal patient-specific MR imaging data were used in conjunction with the model to parameterize tumor cell proliferation rate and tumor cell diffusion coefficient, and Dice correlation coefficients were used to quantify degree of correlation between model-estimated mechanical stress fields and edema visualized from MR imaging. RESULTS Results found four statistically relevant parameters which can differentiate tumor recurrence and radiation-induced necrosis. CONCLUSIONS This preliminary investigation suggests potential of this framework to noninvasively determine the etiology of enhancing lesions in patients who previously underwent SRS for intracranial metastases.
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Affiliation(s)
- Saramati Narasimhan
- Department of Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, TN, 37235, USA
| | - Haley B Johnson
- Department of Biomedical Engineering, Wake Forest School of Medicine, Wake Forest Baptist Medical Center, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Tanner M Nickles
- Department of Biomedical Engineering, Wake Forest School of Medicine, Wake Forest Baptist Medical Center, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, TN, 37235, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Nashville, TN, USA
| | - Nitesh Rana
- Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Albert Attia
- Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Wake Forest School of Medicine, Wake Forest Baptist Medical Center, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
- Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
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Hormuth DA, Jarrett AM, Lima EA, McKenna MT, Fuentes DT, Yankeelov TE. Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data. JCO Clin Cancer Inform 2019; 3:1-10. [PMID: 30807209 PMCID: PMC6535803 DOI: 10.1200/cci.18.00055] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2018] [Indexed: 12/19/2022] Open
Abstract
Multiparametric imaging is a critical tool in the noninvasive study and assessment of cancer. Imaging methods have evolved over the past several decades to provide quantitative measures of tumor and healthy tissue characteristics related to, for example, cell number, blood volume fraction, blood flow, hypoxia, and metabolism. Mechanistic models of tumor growth also have matured to a point where the incorporation of patient-specific measures could provide clinically relevant predictions of tumor growth and response. In this review, we identify and discuss approaches that use multiparametric imaging data, including diffusion-weighted magnetic resonance imaging, dynamic contrast-enhanced magnetic resonance imaging, diffusion tensor imaging, contrast-enhanced computed tomography, [18F]fluorodeoxyglucose positron emission tomography, and [18F]fluoromisonidazole positron emission tomography to initialize and calibrate mechanistic models of tumor growth and response. We focus the discussion on brain and breast cancers; however, we also identify three emerging areas of application in kidney, pancreatic, and lung cancers. We conclude with a discussion of the future directions for incorporating multiparametric imaging data and mechanistic modeling into clinical decision making for patients with cancer.
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67
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Zhong H, Brown S, Devpura S, Li XA, Chetty IJ. Kinetic modeling of tumor regression incorporating the concept of cancer stem-like cells for patients with locally advanced lung cancer. Theor Biol Med Model 2018; 15:23. [PMID: 30587218 PMCID: PMC6307263 DOI: 10.1186/s12976-018-0096-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 11/30/2018] [Indexed: 12/17/2022] Open
Abstract
Background Personalized medicine for patients receiving radiation therapy remains an elusive goal due, in part, to the limits in our understanding of the underlying mechanisms governing tumor response to radiation. The purpose of this study was to develop a kinetic model, in the context of locally advanced lung cancer, connecting cancer cell subpopulations with tumor volumes measured during the course of radiation treatment for understanding treatment outcome for individual patients. Methods The kinetic model consists of three cell compartments: cancer stem-like cells (CSCs), non-stem tumor cells (TCs) and dead cells (DCs). A set of ordinary differential equations were developed to describe the time evolution of each compartment, and the analytic solution of these equations was iterated to be aligned with the day-to-day tumor volume changes during the course of radiation treatment. A least squares fitting method was used to estimate the parameters of the model that include the proportion of CSCs and their radio-sensitivities. This model was applied to five patients with stage III lung cancer, and tumor volumes were measured from 33 cone-beam computed tomography (CBCT) images for each of these patients. The analytical solution of these differential equations was compared with numerically simulated results. Results For the five patients with late stage lung cancer, the derived proportions of CSCs are 0.3 on average, the average probability of the symmetry division is 0.057 and the average surviving fractions of CSCs is 0.967, respectively. The derived parameters are comparable to the results from literature and our experiments. The preliminary results suggest that the CSC self-renewal rate is relatively small, compared to the proportion of CSCs for locally advanced lung cancers. Conclusions A novel mathematical model has been developed to connect the population of cancer stem-like cells with tumor volumes measured from a sequence of CBCT images. This model may help improve our understanding of tumor response to radiation therapy, and is valuable for development of new treatment regimens for patients with locally advanced lung cancer.
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Affiliation(s)
- Hualiang Zhong
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, 53226, WI, USA.
| | - Stephen Brown
- Department of Radiation Oncology, Henry Ford Health System, Detroit, 48202, MI, USA
| | - Suneetha Devpura
- Department of Radiation Oncology, Henry Ford Health System, Detroit, 48202, MI, USA
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, 53226, WI, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health System, Detroit, 48202, MI, USA
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Suh S, Leaman EJ, Zhan Y, Behkam B. Mathematical Modeling of Bacteria-Enabled Drug Delivery System Penetration into Multicellular Tumor Spheroids. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:6162-6165. [PMID: 30441741 DOI: 10.1109/embc.2018.8513596] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Bacteria-based cancer treatment is a promising approach to address the need for targeted tumor therapies in an effort to avoid the systemic toxicity inherent in conventional chemotherapy. A number of bacterial strains have been shown to preferentially colonize tumors and impart therapeutic benefits. However, the physical underpinnings of bacteria intratumoral transport remain poorly studied. It is hypothesized that cell Iysis in hypoxic and necrotic regions of tumors creates a niche in which some bacteria thrive. To understand if preferential growth plausibly explains the experimentally observed bacterial colonization profiles, we have developed a mathematical model incorporating transport and growth dependent on tumor cell Iysate. We fit model parameters to experimental data, showing that our formulation captures experimentally observed trends. Moreover, we find that bacteria have a higher effective diffusivity than nanoparticles alone, demonstrating transport advantages to designing bacteria-based cancer therapy. This model serves as a first step towards building computational tools for designing optimized bacteria- based chemotherapeutic delivery systems.
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Elazab A, Bai H, Yang M, Hu Q, Le MH, Wang T, Lei B. A Coupled Modified Reaction Diffusion and Biomechanical Models for Cerebral Tumor Growth Modeling in Presence of Treatment. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:758-761. [PMID: 30440506 DOI: 10.1109/embc.2018.8512324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Tumor growth modeling at macroscopic level from multimodal images can help in predicting the future evolution of tumor and the treatment planning. This can be achieved using mathematical models where multi time-point images are available. In this paper, we propose a coupled modified reaction diffusion model that measures tumor invasion and infiltration with biomechanical model to consider tumor mass effect. In addition, our model considers treatment effects from radiotherapy and/or chemotherapy if any. The chemotherapy effect is included via a modified log-kill method to consider tissue heterogeneity while radiotherapy effect is considered using the linear quadratic model. We test the proposed model on both synthetic and 6 real datasets of low grade glioma cases with and without treatments. Experimental results of the proposed model on the clinical magnetic resonance images show that our model can simulate the tumor growth with good accuracy and effectively include the treatment effects.
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Patterns of re-irradiation for recurrent gliomas and validation of a prognostic score. Radiother Oncol 2018; 130:156-163. [PMID: 30446315 DOI: 10.1016/j.radonc.2018.10.034] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 10/17/2018] [Accepted: 10/29/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE OR OBJECTIVE Re-irradiation is a generally accepted method for salvage treatment in patients with recurrent glioma. However, no standard radiation regimen has been defined. This study aims to compare the efficacy and safety of different treatment regimens and to independently externally validate a recently published reirradiation risk score. MATERIAL AND METHODS We retrospectively analyzed a cohort of patients with recurrent malignant glioma treated with salvage conventionally fractionated (CFRT), hypofractionated (HFRT) or stereotactic radiotherapy (SRT) between 2007 and 2017 at the University Medical Centers in Utrecht and Groningen. RESULTS Of the 121 patients included, 60 patients (50%) underwent CFRT, 22 (18%) HFRT and 39 (32%) SRT. The primary tumor was grade II-III in 52 patients and grade IV in 69 patients with median Overall Survival (mOS) since first surgery of 113 [Interquartile range: 53.2-137] and 39.7 [24.6-64.9] months respectively (p < 0.01). Overall, mOS from the first day of re-irradiation was 9.7 months [6.5-14.6]. No significant difference in mOS was found between the treatment groups. In multivariate analysis, the Karnofsky performance scale ≥70% (p < 0.01), re-irradiation for first recurrence (p = 0.02), longer time interval between RT start dates (p < 0.01) and smaller planning target volume (p < 0.05) were significant favorable prognostic factors. The reirradiation risk score was validated. CONCLUSION In our series, mOS after reirradiation was sufficient to justify use of this modality. Until a reliable treatment decision tool is developed based on larger retrospective research, the decision for re-irradiation schedule should remain personalized and based on a multidisciplinary evaluation of each patient.
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Alfonso JCL, Talkenberger K, Seifert M, Klink B, Hawkins-Daarud A, Swanson KR, Hatzikirou H, Deutsch A. The biology and mathematical modelling of glioma invasion: a review. J R Soc Interface 2018; 14:rsif.2017.0490. [PMID: 29118112 DOI: 10.1098/rsif.2017.0490] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 10/17/2017] [Indexed: 12/13/2022] Open
Abstract
Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.
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Affiliation(s)
- J C L Alfonso
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.,Centre for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - K Talkenberger
- Centre for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - M Seifert
- Institute for Medical Informatics and Biometry, Technische Universität Dresden, Germany.,National Center for Tumor Diseases (NCT), Dresden, Germany
| | - B Klink
- Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Germany.,National Center for Tumor Diseases (NCT), Dresden, Germany.,German Cancer Consortium (DKTK), partner site, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - A Hawkins-Daarud
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, USA
| | - K R Swanson
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, USA
| | - H Hatzikirou
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.,Centre for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - A Deutsch
- Centre for Information Services and High Performance Computing, Technische Universität Dresden, Germany
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72
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Jacob J, Durand T, Feuvret L, Mazeron JJ, Delattre JY, Hoang-Xuan K, Psimaras D, Douzane H, Ribeiro M, Capelle L, Carpentier A, Ricard D, Maingon P. Cognitive impairment and morphological changes after radiation therapy in brain tumors: A review. Radiother Oncol 2018; 128:221-228. [PMID: 30041961 DOI: 10.1016/j.radonc.2018.05.027] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 05/25/2018] [Accepted: 05/28/2018] [Indexed: 11/18/2022]
Abstract
Life expectancy of patients treated for brain tumors has lengthened due to the therapeutic improvements. Cognitive impairment has been described following brain radiotherapy, but the mechanisms leading to this adverse event remain mostly unknown. Technical evolutions aim at enhancing the therapeutic ratio. Sparing of the healthy tissues has been improved using various approaches; however, few dose constraints have been established regarding brain structures associated with cognitive functions. The aims of this literature review are to report the main brain areas involved in cognitive adverse effects induced by radiotherapy as described in literature, to better understand brain radiosensitivity and to describe potential future improvements.
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Affiliation(s)
- Julian Jacob
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Radiation Oncology, France; Sorbonne Université, CNRS, Service de Santé des Armées, Cognition and Action Group, Paris, France.
| | - Thomas Durand
- Sorbonne Université, CNRS, Service de Santé des Armées, Cognition and Action Group, Paris, France; Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France
| | - Loïc Feuvret
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Radiation Oncology, France
| | - Jean-Jacques Mazeron
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Radiation Oncology, France
| | - Jean-Yves Delattre
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France; Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, France
| | - Khê Hoang-Xuan
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France; Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, France
| | - Dimitri Psimaras
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France; Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, France
| | - Hassen Douzane
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France
| | - Monica Ribeiro
- Sorbonne Université, CNRS, Service de Santé des Armées, Cognition and Action Group, Paris, France; Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurology, France
| | - Laurent Capelle
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurosurgery, France
| | - Alexandre Carpentier
- Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, France; Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Neurosurgery, France
| | - Damien Ricard
- Sorbonne Université, CNRS, Service de Santé des Armées, Cognition and Action Group, Paris, France; Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Department of Neurology, Clamart, France; Service de Santé des Armées, Ecole du Val-de-Grâce, Paris, France
| | - Philippe Maingon
- Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, Department of Radiation Oncology, France
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73
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Anacleto A, Dias J. Data Analysis in Radiotherapy Treatments. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2018. [DOI: 10.4018/ijehmc.2018070103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Radiotherapy is one of the main cancer treatments available today, together with chemotherapy and surgery. Radiotherapy treatments have to be planned for each patient in an individualized manner. The knowledge acquired from one single treatment can be used to improve the treatment planning and outcome of several other patients. In the last years, attention has been drawn to the added value of using data analysis for radiotherapy treatment planning, prediction of treatment outcomes, survival analysis and quality assurance. In this article, existing literature is reviewed.
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Affiliation(s)
- Ana Anacleto
- Faculty of Economics, University of Coimbra, Coimbra, Portugal
| | - Joana Dias
- Inesc-Coimbra, CeBER, Faculty of Economics, University of Coimbra, Coimbra, Portugal
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74
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López Alfonso JC, Parsai S, Joshi N, Godley A, Shah C, Koyfman SA, Caudell JJ, Fuller CD, Enderling H, Scott JG. Temporally feathered intensity-modulated radiation therapy: A planning technique to reduce normal tissue toxicity. Med Phys 2018; 45:3466-3474. [PMID: 29786861 DOI: 10.1002/mp.12988] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 04/18/2018] [Accepted: 05/13/2018] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Intensity-modulated radiation therapy (IMRT) has allowed optimization of three-dimensional spatial radiation dose distributions permitting target coverage while reducing normal tissue toxicity. However, radiation-induced normal tissue toxicity is a major contributor to patients' quality of life and often a dose-limiting factor in the definitive treatment of cancer with radiation therapy. We propose the next logical step in the evolution of IMRT using canonical radiobiological principles, optimizing the temporal dimension through which radiation therapy is delivered to further reduce radiation-induced toxicity by increased time for normal tissue recovery. We term this novel treatment planning strategy "temporally feathered radiation therapy" (TFRT). METHODS Temporally feathered radiotherapy plans were generated as a composite of five simulated treatment plans each with altered constraints on particular hypothetical organs at risk (OARs) to be delivered sequentially. For each of these TFRT plans, OARs chosen for feathering receive higher doses while the remaining OARs receive lower doses than the standard fractional dose delivered in a conventional fractionated IMRT plan. Each TFRT plan is delivered a specific weekday, which in effect leads to a higher dose once weekly followed by four lower fractional doses to each temporally feathered OAR. We compared normal tissue toxicity between TFRT and conventional fractionated IMRT plans by using a dynamical mathematical model to describe radiation-induced tissue damage and repair over time. RESULTS Model-based simulations of TFRT demonstrated potential for reduced normal tissue toxicity compared to conventionally planned IMRT. The sequencing of high and low fractional doses delivered to OARs by TFRT plans suggested increased normal tissue recovery, and hence less overall radiation-induced toxicity, despite higher total doses delivered to OARs compared to conventional fractionated IMRT plans. The magnitude of toxicity reduction by TFRT planning was found to depend on the corresponding standard fractional dose of IMRT and organ-specific recovery rate of sublethal radiation-induced damage. CONCLUSIONS TFRT is a novel technique for treatment planning and optimization of therapeutic radiotherapy that considers the nonlinear aspects of normal tissue repair to optimize toxicity profiles. Model-based simulations of TFRT to carefully conceptualized clinical cases have demonstrated potential for radiation-induced toxicity reduction in a previously described dynamical model of normal tissue complication probability (NTCP).
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Affiliation(s)
- Juan Carlos López Alfonso
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Rebenring 56, Braunschweig, 38106, Germany
| | - Shireen Parsai
- Department of Radiation Oncology, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Nikhil Joshi
- Department of Radiation Oncology, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Andrew Godley
- Department of Radiation Oncology, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Chirag Shah
- Department of Radiation Oncology, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Shlomo A Koyfman
- Department of Radiation Oncology, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Jimmy J Caudell
- Department of Radiation Oncology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL, 33612, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, 1840 Old Spanish Trail, Houston, TX, 77054, USA
| | - Heiko Enderling
- Department of Radiation Oncology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL, 33612, USA.,Department of Integrated Mathematical Oncology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL, 33612, USA
| | - Jacob G Scott
- Department of Radiation Oncology, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.,Department of Translational Hematology and Oncology Research, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
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75
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McKenna MT, Weis JA, Brock A, Quaranta V, Yankeelov TE. Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol 2018; 11:732-742. [PMID: 29674173 PMCID: PMC6056758 DOI: 10.1016/j.tranon.2018.03.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 03/22/2018] [Accepted: 03/22/2018] [Indexed: 02/07/2023] Open
Abstract
Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.
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Affiliation(s)
- Matthew T McKenna
- Vanderbilt University Institute of Imaging Science, Nashville, TN; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Jared A Weis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX
| | - Vito Quaranta
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX; Department of Oncology, The University of Texas at Austin, Austin, TX; Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX.
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76
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Rutter EM, Banks HT, Flores KB. Estimating intratumoral heterogeneity from spatiotemporal data. J Math Biol 2018; 77:1999-2022. [DOI: 10.1007/s00285-018-1238-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 04/13/2018] [Indexed: 11/24/2022]
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77
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Hormuth DA, Weis JA, Barnes SL, Miga MI, Quaranta V, Yankeelov TE. Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer. Int J Radiat Oncol Biol Phys 2018; 100:1270-1279. [PMID: 29398129 PMCID: PMC5934308 DOI: 10.1016/j.ijrobp.2017.12.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/17/2017] [Accepted: 12/03/2017] [Indexed: 02/05/2023]
Abstract
PURPOSE To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy. METHODS AND MATERIALS Post-radiation therapy response is modeled using a cell death model (Md), a reduced proliferation rate model (Mp), and cell death and reduced proliferation model (Mdp). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number. RESULTS For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the Mdp and Mp models compared with the Md model. The Mdp model fit, however, had significantly lower sum squared error compared with the Mp and Md models. CONCLUSIONS The results of this study indicate that for both doses, the Mp and Mdp models result in accurate predictions of tumor growth, whereas the Md model poorly describes response to radiation therapy.
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Affiliation(s)
- David A Hormuth
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas.
| | - Jared A Weis
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina; Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Stephanie L Barnes
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee; Department of Neurological Surgery, Vanderbilt University, Nashville, Tennessee
| | - Vito Quaranta
- Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee
| | - Thomas E Yankeelov
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas; Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas; Department of Internal Medicine, The University of Texas at Austin, Austin, Texas
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78
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Klank RL, Rosenfeld SS, Odde DJ. A Brownian dynamics tumor progression simulator with application to glioblastoma. CONVERGENT SCIENCE PHYSICAL ONCOLOGY 2018; 4:015001. [PMID: 30627438 PMCID: PMC6322960 DOI: 10.1088/2057-1739/aa9e6e] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Tumor progression modeling offers the potential to predict tumor-spreading behavior to improve prognostic accuracy and guide therapy development. Common simulation methods include continuous reaction-diffusion (RD) approaches that capture mean spatio-temporal tumor spreading behavior and discrete agent-based (AB) approaches which capture individual cell events such as proliferation or migration. The brain cancer glioblastoma (GBM) is especially appropriate for such proliferation-migration modeling approaches because tumor cells seldom metastasize outside of the central nervous system and cells are both highly proliferative and migratory. In glioblastoma research, current RD estimates of proliferation and migration parameters are derived from computed tomography or magnetic resonance images. However, these estimates of glioblastoma cell migration rates, modeled as a diffusion coefficient, are approximately 1-2 orders of magnitude larger than single-cell measurements in animal models of this disease. To identify possible sources for this discrepancy, we evaluated the fundamental RD simulation assumptions that cells are point-like structures that can overlap. To give cells physical size (~10 μm), we used a Brownian dynamics approach that simulates individual single-cell diffusive migration, growth, and proliferation activity via a gridless, off-lattice, AB method where cells can be prohibited from overlapping each other. We found that for realistic single-cell parameter growth and migration rates, a non-overlapping model gives rise to a jammed configuration in the center of the tumor and a biased outward diffusion of cells in the tumor periphery, creating a quasi-ballistic advancing tumor front. The simulations demonstrate that a fast-progressing tumor can result from minimally diffusive cells, but at a rate that is still dependent on single-cell diffusive migration rates. Thus, modeling with the assumption of physically-grounded volume conservation can account for the apparent discrepancy between estimated and measured diffusion of GBM cells and provide a new theoretical framework that naturally links single-cell growth and migration dynamics to tumor-level progression.
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Affiliation(s)
- Rebecca L Klank
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Steven S Rosenfeld
- Burkhardt Brain Tumor Center, Department of Cancer Biology, Cleveland Clinic, Cleveland, OH, United States of America
| | - David J Odde
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
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79
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Gerasimov VA, Boldyreva VV, Datsenko PV. [Hypofractionated radiotherapy for glioblastoma: changing the radiation treatment paradigm]. ZHURNAL VOPROSY NEĬROKHIRURGII IMENI N. N. BURDENKO 2018; 81:116-124. [PMID: 29393295 DOI: 10.17116/neiro2017816116-124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Hypofractionation has the dual advantage of increased cell death with a higher dose per fraction and a reduced effect of accelerated tumor cell repopulation due to a shorter overall treatment time. However, the potential advantage may be offset by increased toxicity in the late-responding neural tissues. Recently, investigators have attempted delivering radical doses of HFRT by escalating the dose in the immediate vicinity of the enhancing tumor and postoperative surgical cavity and reported reasonable outcomes with acceptable toxicity levels. Three different studies of high-dose HFRT have reported on the paradoxical phenomenon of improved survival in patients developing radiation necrosis at the primary tumor site. The toxicity criteria of RTOG and EORTC have defined clinically or radiographically suspected radionecrosis as Grade 4 toxicity. However, most patients diagnosed with radiation necrosis in the above studies remained asymptomatic. Furthermore, the probable association with improved survival would strongly argue against adopting a blind approach for classifying radiation necrosis as Grade 4 toxicity. The data emerging from the above studies is encouraging and strongly argues for further research. However, the majority of these studies are predominantly retrospective or relatively small single-arm prospective series that add little to the overall quality of evidence. Notwithstanding the above limitations, HFRT appears to be a safe and feasible strategy for glioblastoma patients.
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Affiliation(s)
- V A Gerasimov
- Herzen Moscow Oncology Research Institute, Moscow, Russia, 125284
| | - V V Boldyreva
- Herzen Moscow Oncology Research Institute, Moscow, Russia, 125284
| | - P V Datsenko
- Herzen Moscow Oncology Research Institute, Moscow, Russia, 125284
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80
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Stocks T, Hillen T, Gong J, Burger M. A stochastic model for the normal tissue complication probability (NTCP) and applicationss. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2017; 34:469-492. [PMID: 27591250 DOI: 10.1093/imammb/dqw013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 08/06/2016] [Indexed: 11/14/2022]
Abstract
The normal tissue complication probability (NTCP) is a measure for the estimated side effects of a given radiation treatment schedule. Here we use a stochastic logistic birth-death process to define an organ-specific and patient-specific NTCP. We emphasize an asymptotic simplification which relates the NTCP to the solution of a logistic differential equation. This framework is based on simple modelling assumptions and it prepares a framework for the use of the NTCP model in clinical practice. As example, we consider side effects of prostate cancer brachytherapy such as increase in urinal frequency, urinal retention and acute rectal dysfunction.
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Affiliation(s)
- Theresa Stocks
- Department of Mathematics, Stockholm University, SE - 106 91 Stockholm, Sweden
| | - Thomas Hillen
- Centre for Mathematical Biology, Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G2G1, Canada
| | - Jiafen Gong
- The hospital for sick children research institute, SickKids, 555 University Avenue, Toronto, Ontario M5G1X8, Canada
| | - Martin Burger
- Institute for Computational and Applied Mathematics, Excellence Cluster Cells in Motion, University of Münster, Einsteinstrasse 62, D-48149 Münster
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81
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Zhang J, Cunningham JJ, Brown JS, Gatenby RA. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat Commun 2017; 8:1816. [PMID: 29180633 PMCID: PMC5703947 DOI: 10.1038/s41467-017-01968-5] [Citation(s) in RCA: 306] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 10/26/2017] [Indexed: 11/15/2022] Open
Abstract
Abiraterone treats metastatic castrate-resistant prostate cancer by inhibiting CYP17A, an enzyme for testosterone auto-production. With standard dosing, evolution of resistance with treatment failure (radiographic progression) occurs at a median of ~16.5 months. We hypothesize time to progression (TTP) could be increased by integrating evolutionary dynamics into therapy. We developed an evolutionary game theory model using Lotka–Volterra equations with three competing cancer “species”: androgen dependent, androgen producing, and androgen independent. Simulations with standard abiraterone dosing demonstrate strong selection for androgen-independent cells and rapid treatment failure. Adaptive therapy, using patient-specific tumor dynamics to inform on/off treatment cycles, suppresses proliferation of androgen-independent cells and lowers cumulative drug dose. In a pilot clinical trial, 10 of 11 patients maintained stable oscillations of tumor burdens; median TTP is at least 27 months with reduced cumulative drug use of 47% of standard dosing. The outcomes show significant improvement over published studies and a contemporaneous population. Evolution of resistance is a common cause of cancer treatment failure and tumor progression. Here, the authors present a method for integrating evolutionary principles based on adaptive therapy into abiraterone therapy for metastatic castrate-resistant prostate cancer and show the positive results of an interim analysis of a trial cohort.
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Affiliation(s)
- Jingsong Zhang
- Department of Genitourinary Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, 33612, USA
| | - Jessica J Cunningham
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, 33612, USA
| | - Joel S Brown
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, 33612, USA.,Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Robert A Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL, 33612, USA. .,Department of Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center & Research Institute, Tampa, FL, 33612, USA.
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82
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Neufeld Z, von Witt W, Lakatos D, Wang J, Hegedus B, Czirok A. The role of Allee effect in modelling post resection recurrence of glioblastoma. PLoS Comput Biol 2017; 13:e1005818. [PMID: 29149169 PMCID: PMC5711030 DOI: 10.1371/journal.pcbi.1005818] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 12/01/2017] [Accepted: 10/09/2017] [Indexed: 11/24/2022] Open
Abstract
Resection of the bulk of a tumour often cannot eliminate all cancer cells, due to their infiltration into the surrounding healthy tissue. This may lead to recurrence of the tumour at a later time. We use a reaction-diffusion equation based model of tumour growth to investigate how the invasion front is delayed by resection, and how this depends on the density and behaviour of the remaining cancer cells. We show that the delay time is highly sensitive to qualitative details of the proliferation dynamics of the cancer cell population. The typically assumed logistic type proliferation leads to unrealistic results, predicting immediate recurrence. We find that in glioblastoma cell cultures the cell proliferation rate is an increasing function of the density at small cell densities. Our analysis suggests that cooperative behaviour of cancer cells, analogous to the Allee effect in ecology, can play a critical role in determining the time until tumour recurrence. Mathematical models of propagating fronts have been used to represent a wide variety of biological phenomena from action potentials in neural cells to invasive species in ecology and epidemic spreading. Here we show that when such models are used to predict the effects of external perturbations the results can be very sensitive to certain details of the local dynamics. For example, the post resection recurrence of tumour growth depends strongly on the density dependence of the proliferation of cancer cells. This suggests that targeting the cooperative behaviour of cancer cells could be an efficient strategy for delaying the recurrence of diffuse aggressive brain tumours.
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Affiliation(s)
- Zoltan Neufeld
- School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
- * E-mail:
| | - William von Witt
- School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Dora Lakatos
- Department of Biological Physics, Eotvos University, Budapest, Hungary
| | - Jiaming Wang
- School of Gifted Young, University of Science and Technology of China, Hefei, China
| | - Balazs Hegedus
- Department of Thoracic Surgery, Ruhrlandklinik, University Duisburg-Essen, Essen, Germany
- MTA-SE Molecular Oncology Research Group, Hungarian Academy of Sciences - Semmelweis University, Budapest, Hungary
| | - Andras Czirok
- Department of Biological Physics, Eotvos University, Budapest, Hungary
- Department of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, Kansas, United States of America
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83
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Christ B, Dahmen U, Herrmann KH, König M, Reichenbach JR, Ricken T, Schleicher J, Ole Schwen L, Vlaic S, Waschinsky N. Computational Modeling in Liver Surgery. Front Physiol 2017; 8:906. [PMID: 29249974 PMCID: PMC5715340 DOI: 10.3389/fphys.2017.00906] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 10/25/2017] [Indexed: 12/13/2022] Open
Abstract
The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.
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Affiliation(s)
- Bruno Christ
- Molecular Hepatology Lab, Clinics of Visceral, Transplantation, Thoracic and Vascular Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Matthias König
- Department of Biology, Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Tim Ricken
- Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany
| | - Jana Schleicher
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany.,Department of Bioinformatics, Friedrich Schiller University Jena, Jena, Germany
| | | | - Sebastian Vlaic
- Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
| | - Navina Waschinsky
- Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany
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84
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Protopapa M, Zygogianni A, Stamatakos GS, Antypas C, Armpilia C, Uzunoglu NK, Kouloulias V. Clinical implications of in silico mathematical modeling for glioblastoma: a critical review. J Neurooncol 2017; 136:1-11. [PMID: 29081039 DOI: 10.1007/s11060-017-2650-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 10/22/2017] [Indexed: 01/22/2023]
Abstract
Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology. This paper reviews mathematical models on glioblastoma from the clinical doctor's point of view, with focus on 3D modeling approaches of radiation response of in vivo glioblastomas based on contemporary imaging techniques. As these models aim to provide a clinically useful tool in the era of personalized medicine, the integration of the latest advances in molecular and imaging science and in clinical practice by the in silico models is crucial for their clinical relevance. Our aim is to indicate areas of GBM research that have not yet been addressed by in silico models and to point out evidence that has come up from in silico experiments, which may be worth considering in the clinic. This review examines how close these models have come in predicting the outcome of treatment protocols and in shaping the future of radiotherapy treatments.
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Affiliation(s)
- Maria Protopapa
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Anna Zygogianni
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios S Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Christos Antypas
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Christina Armpilia
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos K Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Vassilis Kouloulias
- Radiation Oncology Unit, 2nd Department of Radiology, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece. .,Radiotherapy Unit, 2nd Department of Radiology, ATTIKON University Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece.
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85
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Elazab A. Low grade glioma growth modeling considering chemotherapy and radiotherapy effects from magnetic resonance images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3077-3080. [PMID: 29060548 DOI: 10.1109/embc.2017.8037507] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Studying tumor growth using mathematical models from magnetic resonance (MR) images is an important application that is believed to play a major role in cancer treatment by predicting tumor evolution, quantifying the response to therapy, and treatment planning. Reaction diffusion is the most popular model because of its simplicity and consistency with the biological growth process. However, most of the current growth models focus on presurgical images and likely without treatment. In this paper, we propose a new reaction diffusion model to consider the chemotherapy and radiotherapy effects on the tumor growth modelling for patients with low grade glioma. The proposed model does not consider the tensor information from diffusion tensor imaging. Instead it uses a weighted parameter to promote higher diffusivity in white matter. The radiotherapy and chemotherapy effects are considered as a loss terms in the proposed model. The preliminary results of the proposed model on synthetic and 2 real MR images show that, our model can effectively simulate tumor growth with high accuracies when treatments are administrated to low grade glioma patients.
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86
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Basanta D, Anderson ARA. Homeostasis Back and Forth: An Ecoevolutionary Perspective of Cancer. Cold Spring Harb Perspect Med 2017; 7:cshperspect.a028332. [PMID: 28289244 DOI: 10.1101/cshperspect.a028332] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The role of genetic mutations in cancer is indisputable: They are a key source of tumor heterogeneity and drive its evolution to malignancy. But, the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here, we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, because they perturb the normal regulatory dynamics of these tissues, often coopting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor-centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This ecoevolutionary view of cancer requires mechanistic mathematical models to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.
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Affiliation(s)
- David Basanta
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, Florida 33612
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, Florida 33612
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87
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Hassanzadeh P, Atyabi F, Dinarvand R. Application of modelling and nanotechnology-based approaches: The emergence of breakthroughs in theranostics of central nervous system disorders. Life Sci 2017; 182:93-103. [DOI: 10.1016/j.lfs.2017.06.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Revised: 05/30/2017] [Accepted: 06/01/2017] [Indexed: 01/28/2023]
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88
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Bogdańska MU, Bodnar M, Piotrowska MJ, Murek M, Schucht P, Beck J, Martínez-González A, Pérez-García VM. A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications. PLoS One 2017; 12:e0179999. [PMID: 28763450 PMCID: PMC5538650 DOI: 10.1371/journal.pone.0179999] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 06/07/2017] [Indexed: 01/28/2023] Open
Abstract
Gliomas are the most frequent type of primary brain tumours. Low grade gliomas (LGGs, WHO grade II gliomas) may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas (HGGs, WHO grade III and IV gliomas). In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling approach is based on two cellular populations with transitions between them being driven by the tumour microenvironment transformation occurring when the tumour cell density grows beyond a critical level. We show that the proposed model describes real patient data well. We discuss the relationship between patient prognosis and model parameters. We approximate tumour radius and velocity before malignant transformation as well as estimate the onset of this process.
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Affiliation(s)
- Magdalena U. Bogdańska
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
- Departamento de Matemáticas, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Marek Bodnar
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Monika J. Piotrowska
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Michael Murek
- Universitätsklinik für Neurochirurgie, Bern University Hospital, Bern, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, Bern, Switzerland
| | - Jürgen Beck
- Universitätsklinik für Neurochirurgie, Bern University Hospital, Bern, Switzerland
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89
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McKenna MT, Weis JA, Barnes SL, Tyson DR, Miga MI, Quaranta V, Yankeelov TE. A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer. Sci Rep 2017; 7:5725. [PMID: 28720897 PMCID: PMC5516013 DOI: 10.1038/s41598-017-05902-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 06/06/2017] [Indexed: 12/30/2022] Open
Abstract
Doxorubicin forms the basis of chemotherapy regimens for several malignancies, including triple negative breast cancer (TNBC). Here, we present a coupled experimental/modeling approach to establish an in vitro pharmacokinetic/pharmacodynamic model to describe how the concentration and duration of doxorubicin therapy shape subsequent cell population dynamics. This work features a series of longitudinal fluorescence microscopy experiments that characterize (1) doxorubicin uptake dynamics in a panel of TNBC cell lines, and (2) cell population response to doxorubicin over 30 days. We propose a treatment response model, fully parameterized with experimental imaging data, to describe doxorubicin uptake and predict subsequent population dynamics. We found that a three compartment model can describe doxorubicin pharmacokinetics, and pharmacokinetic parameters vary significantly among the cell lines investigated. The proposed model effectively captures population dynamics and translates well to a predictive framework. In a representative cell line (SUM-149PT) treated for 12 hours with doxorubicin, the mean percent errors of the best-fit and predicted models were 14% (±10%) and 16% (±12%), which are notable considering these statistics represent errors over 30 days following treatment. More generally, this work provides both a template for studies quantitatively investigating treatment response and a scalable approach toward predictions of tumor response in vivo.
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Affiliation(s)
- Matthew T McKenna
- Vanderbilt University Institute of Imaging Science, Nashville, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, USA
| | - Stephanie L Barnes
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA.,Livestrong Cancer Institutes, The University of Texas at Austin, Austin, USA
| | - Darren R Tyson
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, USA
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, USA.,Department of Radiology & Radiological Sciences, Vanderbilt University School of Medicine, Nashville, USA
| | - Vito Quaranta
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA. .,Department of Diagnostic Medicine, Dell Medical School, The University of Texas at Austin, Austin, USA. .,Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, USA. .,Livestrong Cancer Institutes, The University of Texas at Austin, Austin, USA.
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90
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Poleszczuk J, Walker R, Moros EG, Latifi K, Caudell JJ, Enderling H. Predicting Patient-Specific Radiotherapy Protocols Based on Mathematical Model Choice for Proliferation Saturation Index. Bull Math Biol 2017; 80:1195-1206. [PMID: 28681150 DOI: 10.1007/s11538-017-0279-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 03/31/2017] [Indexed: 01/27/2023]
Abstract
Radiation is commonly used in cancer treatment. Over 50% of all cancer patients will undergo radiotherapy (RT) as part of cancer care. Scientific advances in RT have primarily focused on the physical characteristics of treatment including beam quality and delivery. Only recently have inroads been made into utilizing tumor biology and radiobiology to design more appropriate RT protocols. Tumors are composites of proliferating and growth-arrested cells, and overall response depends on their respective proportions at irradiation. Prokopiou et al. (Radiat Oncol 10:159, 2015) developed the concept of the proliferation saturation index (PSI) to augment the clinical decision process associated with RT. This framework is based on the application of the logistic equation to pre-treatment imaging data in order to estimate a patient-specific tumor carrying capacity, which is then used to recommend a specific RT protocol. It is unclear, however, how dependent clinical recommendations are on the underlying tumor growth law. We discuss a PSI framework with a generalized logistic equation that can capture kinetics of different well-known growth laws including logistic and Gompertzian growth. Estimation of model parameters on the basis of clinical data revealed that the generalized logistic model can describe data equally well for a wide range of the generalized logistic exponent value. Clinical recommendations based on the calculated PSI, however, are strongly dependent on the specific growth law assumed. Our analysis suggests that the PSI framework may best be utilized in clinical practice when the underlying tumor growth law is known, or when sufficiently many tumor growth models suggest similar fractionation protocols.
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Affiliation(s)
- Jan Poleszczuk
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33647, USA
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Ks. Trojdena 4 st., 02-109, Warsaw, Poland
| | - Rachel Walker
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33647, USA
| | - Eduardo G Moros
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33647, USA
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33647, USA
| | - Jimmy J Caudell
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33647, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33647, USA.
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33647, USA.
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91
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Walker R, Schoenfeld JD, Pilon-Thomas S, Poleszczuk J, Enderling H. Evaluating the potential for maximized T cell redistribution entropy to improve abscopal responses to radiotherapy. CONVERGENT SCIENCE PHYSICAL ONCOLOGY 2017; 3. [PMID: 29774168 DOI: 10.1088/2057-1739/aa7269] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The potential for local radiation therapy to elicit systemic (abscopal) anti-tumor immune responses has been receiving a significant amount of attention over the last decade. We recently developed a mathematical framework designed to simulate the systemic dissemination of activated T cells among multiple metastatic sites. This framework allowed the identification of non-intuitive patterns of T cell redistribution after localized therapy, and offered suggestions as to the optimal site to irradiate in order to increase the magnitude of an immune-mediated abscopal response. Here, we evaluate the potential for such a framework to provide clinical decision making support to radiation oncologists. Several challenges such as efficient segmentation and delineation of multiple tumor sites on PET/CT scans, validation of model prediction performance, and effective clinical trial design remain to be addressed prior to the incorporation of such a tool in the clinical setting.
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Affiliation(s)
- Rachel Walker
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jonathan D Schoenfeld
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Shari Pilon-Thomas
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jan Poleszczuk
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.,Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Center, Boston, MA, USA
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92
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Heidari N, Abroun S, Bertacchini J, Vosoughi T, Rahim F, Saki N. Significance of Inactivated Genes in Leukemia: Pathogenesis and Prognosis. CELL JOURNAL 2017; 19:9-26. [PMID: 28580304 PMCID: PMC5448318 DOI: 10.22074/cellj.2017.4908] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2016] [Accepted: 02/14/2017] [Indexed: 11/04/2022]
Abstract
Epigenetic and genetic alterations are two mechanisms participating in leukemia, which can inactivate genes involved in leukemia pathogenesis or progression. The purpose of this review was to introduce various inactivated genes and evaluate their possible role in leukemia pathogenesis and prognosis. By searching the mesh words "Gene, Silencing AND Leukemia" in PubMed website, relevant English articles dealt with human subjects as of 2000 were included in this study. Gene inactivation in leukemia is largely mediated by promoter's hypermethylation of gene involving in cellular functions such as cell cycle, apoptosis, and gene transcription. Inactivated genes, such as ASPP1, TP53, IKZF1 and P15, may correlate with poor prognosis in acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), chronic myelogenous leukemia (CML) and acute myeloid leukemia (AML), respectively. Gene inactivation may play a considerable role in leukemia pathogenesis and prognosis, which can be considered as complementary diagnostic tests to differentiate different leukemia types, determine leukemia prognosis, and also detect response to therapy. In general, this review showed some genes inactivated only in leukemia (with differences between B-ALL, T-ALL, CLL, AML and CML). These differences could be of interest as an additional tool to better categorize leukemia types. Furthermore; based on inactivated genes, a diverse classification of Leukemias could represent a powerful method to address a targeted therapy of the patients, in order to minimize side effects of conventional therapies and to enhance new drug strategies.
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Affiliation(s)
- Nazanin Heidari
- Health Research Institute, Thalassemia and Hemoglobinopathy Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Saeid Abroun
- Department of Hematology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Jessika Bertacchini
- Signal Transduction Unit, Department of Surgery, Medicine, Dentistry and Morphology, University of Modena and Reggio Emilia, Modena, Italy
| | - Tina Vosoughi
- Health Research Institute, Thalassemia and Hemoglobinopathy Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Fakher Rahim
- Health Research Institute, Thalassemia and Hemoglobinopathy Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Najmaldin Saki
- Health Research Institute, Thalassemia and Hemoglobinopathy Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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93
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Henares-Molina A, Benzekry S, Lara PC, García-Rojo M, Pérez-García VM, Martínez-González A. Non-standard radiotherapy fractionations delay the time to malignant transformation of low-grade gliomas. PLoS One 2017; 12:e0178552. [PMID: 28570587 PMCID: PMC5453550 DOI: 10.1371/journal.pone.0178552] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 05/15/2017] [Indexed: 12/15/2022] Open
Abstract
Grade II gliomas are slowly growing primary brain tumors that affect mostly young patients. Cytotoxic therapies (radiotherapy and/or chemotherapy) are used initially only for patients having a bad prognosis. These therapies are planned following the “maximum dose in minimum time” principle, i. e. the same schedule used for high-grade brain tumors in spite of their very different behavior. These tumors transform after a variable time into high-grade gliomas, which significantly decreases the patient’s life expectancy. In this paper we study mathematical models describing the growth of grade II gliomas in response to radiotherapy. We find that protracted metronomic fractionations, i.e. therapeutical schedules enlarging the time interval between low-dose radiotherapy fractions, may lead to a better tumor control without an increase in toxicity. Other non-standard fractionations such as protracted or hypoprotracted schemes may also be beneficial. The potential survival improvement depends on the tumor’s proliferation rate and can be even of the order of years. A conservative metronomic scheme, still being a suboptimal treatment, delays the time to malignant progression by at least one year when compared to the standard scheme.
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Affiliation(s)
- Araceli Henares-Molina
- Department of Mathematics, University of Castilla-La Mancha, Ciudad Real, Castilla-La Mancha, Spain
| | - Sebastien Benzekry
- INRIA Bordeaux Sud-Ouest, team MONC, Institut de Mathematiques de Bordeaux, Bordeaux, Nouvelle-Aquitaine, France
| | - Pedro C Lara
- Department of Radiation Oncology, Negrín Las Palmas University Hospital, Las Palmas GC, Canarias, Spain
| | - Marcial García-Rojo
- Department of Pathology, Hospital de Jerez de la Frontera, Jerez de la Frontera, Cádiz, Spain
| | - Víctor M Pérez-García
- Department of Mathematics, University of Castilla-La Mancha, Ciudad Real, Castilla-La Mancha, Spain
| | - Alicia Martínez-González
- Department of Mathematics, University of Castilla-La Mancha, Ciudad Real, Castilla-La Mancha, Spain
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94
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A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread. Bull Math Biol 2017; 80:1259-1291. [DOI: 10.1007/s11538-017-0271-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 03/15/2017] [Indexed: 02/01/2023]
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95
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Patel V, Hathout L. Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme. Theor Biol Med Model 2017; 14:10. [PMID: 28464925 PMCID: PMC5414170 DOI: 10.1186/s12976-017-0056-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 04/20/2017] [Indexed: 12/03/2022] Open
Abstract
Background The heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression. In this work, we develop a novel computational model for the proliferation and necrosis of glioblastoma multiforme. Methods The model parameters are selected based on the magnetic resonance imaging features of each tumor, and the proposed technique accounts for intrinsic cell division, tumor cell migration along white matter tracts, as well as central tumor necrosis. As a validation of this approach, tumor growth is simulated in the brain of a healthy adult volunteer using parameters derived from the imaging of a patient with glioblastoma multiforme. A mutual information metric is calculated between the simulated tumor profile and observed tumor. Results The tumor progression profile generated by the proposed model is compared with those produced by existing models and with the actual observed tumor progression. Both qualitative and quantitative analyses show that the model introduced in this work replicates the observed progression of glioblastoma more accurately relative to prior techniques. Conclusions This image-driven model generates improved tumor progression profiles and may contribute to the development of more reliable prognostic estimates in patients with glioblastoma multiforme.
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Affiliation(s)
- Vishal Patel
- Department of Radiological Sciences Ronald Reagan-UCLA Medical Center, University of California, Los Angeles, 757 Westwood Plaza, Suite 1638, Los Angeles, 90095, CA, USA.
| | - Leith Hathout
- Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, USA
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96
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Elazab A, Bai H, Abdulazeem YM, Abdelhamid T, Zhou S, Wong KKL, Hu Q. Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment. Sci Rep 2017; 7:1222. [PMID: 28450707 PMCID: PMC5430870 DOI: 10.1038/s41598-017-01189-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 03/22/2017] [Indexed: 01/17/2023] Open
Abstract
Reaction diffusion is the most common growth modelling methodology due to its simplicity and consistency with the biological tumor growth process. However, current extensions of the reaction diffusion model lack one or more of the following: efficient inclusion of treatments' effects, taking into account the viscoelasticity of brain tissues, and guaranteed stability of the numerical solution. We propose a new model to overcome the aforementioned drawbacks. Guided by directional information derived from diffusion tensor imaging, our model relates tissue heterogeneity with the absorption of the chemotherapy, adopts the linear-quadratic term to simulate the radiotherapy effect, employs Maxwell-Weichert model to incorporate brain viscoelasticity, and ensures the stability of the numerical solution. The performance is verified through experiments on synthetic and real MR images. Experiments on 9 MR datasets of patients with low grade gliomas undergoing surgery with different treatment regimens are carried out and validated using Jaccard score and Dice coefficient. The growth simulation accuracies of the proposed model are in ranges of [0.673 0.822] and [0.805 0.902] for Jaccard scores and Dice coefficients, respectively. The accuracies decrease up to 4% and 2.4% when ignoring treatment effects and the tensor information, while brain viscoelasticity has no significant impact on the accuracies.
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Affiliation(s)
- Ahmed Elazab
- Research Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China
- Department of Computer Science, Faculty Computers and Information, Mansoura University, Mansoura City, Egypt
- Department of Computer Science, Misr Higher Institute for commerce and computers, Mansoura City, Egypt
| | - Hongmin Bai
- Department of Neurosurgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
| | - Yousry M Abdulazeem
- Department of Computer Engineering, Misr Higher Institute for Engineering and Technology, Mansoura City, Egypt
| | - Talaat Abdelhamid
- Department of Physics and Mathematical Engineering, Faculty of Electronic Engineering, Menoufiya University, Al Minufiyah, Egypt
| | - Sijie Zhou
- Department of Neurosurgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
| | - Kelvin K L Wong
- School of Medicine, Western Sydney University, Campbelltown, New South Wales, Australia.
| | - Qingmao Hu
- Research Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China.
- Key Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China.
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97
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Le M, Delingette H, Kalpathy-Cramer J, Gerstner ER, Batchelor T, Unkelbach J, Ayache N. Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:815-825. [PMID: 28113925 DOI: 10.1109/tmi.2016.2626443] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. The model is personalized to the magnetic resonance images (MRIs) of a given patient. It takes into account the uncertainty in the model parameters, together with the uncertainty in the MRI segmentations. The computed probability distribution over tumor cell densities, together with the cell survival model, is used to define the prescription dose distribution, which is the basis for subsequent Intensity Modulated Radiation Therapy (IMRT) planning. Depending on the clinical data available, we compare three different scenarios to personalize the model. First, we consider a single MRI acquisition before therapy, as it would usually be the case in clinical routine. Second, we use two MRI acquisitions at two distinct time points in order to personalize the model and plan radiotherapy. Third, we include the uncertainty in the segmentation process. We present the application of our approach on two patients diagnosed with high grade glioma. We introduce two methods to derive the radiotherapy prescription dose distribution, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density. We show how our method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration. We further present extensions of the method in order to spare adjacent organs at risk by re-distributing the dose. The presented approach and its proof of concept may help in the future to better target the tumor and spare organs at risk.
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98
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Lee M, Chen GT, Puttock E, Wang K, Edwards RA, Waterman ML, Lowengrub J. Mathematical modeling links Wnt signaling to emergent patterns of metabolism in colon cancer. Mol Syst Biol 2017; 13:912. [PMID: 28183841 PMCID: PMC5327728 DOI: 10.15252/msb.20167386] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 01/05/2017] [Accepted: 01/12/2017] [Indexed: 12/14/2022] Open
Abstract
Cell-intrinsic metabolic reprogramming is a hallmark of cancer that provides anabolic support to cell proliferation. How reprogramming influences tumor heterogeneity or drug sensitivities is not well understood. Here, we report a self-organizing spatial pattern of glycolysis in xenograft colon tumors where pyruvate dehydrogenase kinase (PDK1), a negative regulator of oxidative phosphorylation, is highly active in clusters of cells arranged in a spotted array. To understand this pattern, we developed a reaction-diffusion model that incorporates Wnt signaling, a pathway known to upregulate PDK1 and Warburg metabolism. Partial interference with Wnt alters the size and intensity of the spotted pattern in tumors and in the model. The model predicts that Wnt inhibition should trigger an increase in proteins that enhance the range of Wnt ligand diffusion. Not only was this prediction validated in xenograft tumors but similar patterns also emerge in radiochemotherapy-treated colorectal cancer. The model also predicts that inhibitors that target glycolysis or Wnt signaling in combination should synergize and be more effective than each treatment individually. We validated this prediction in 3D colon tumor spheroids.
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Affiliation(s)
- Mary Lee
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
| | - George T Chen
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, USA
| | - Eric Puttock
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
| | - Kehui Wang
- Department of Pathology, School of Medicine, University of California, Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA
| | - Robert A Edwards
- Department of Pathology, School of Medicine, University of California, Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA
| | - Marian L Waterman
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
| | - John Lowengrub
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
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99
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Kuznetsov MB, Gubernov VV, Kolobov AV. Influence of interstitial fluid dynamics on growth and therapy of angiogenic tumor. Analysis by mathematical model. Biophysics (Nagoya-shi) 2017. [DOI: 10.1134/s0006350917010110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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100
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Fast and high temperature hyperthermia coupled with radiotherapy as a possible new treatment for glioblastoma. J Ther Ultrasound 2016; 4:32. [PMID: 27980785 PMCID: PMC5143464 DOI: 10.1186/s40349-016-0078-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 11/18/2016] [Indexed: 12/21/2022] Open
Abstract
Background A new transcranial focused ultrasound device has been developed that can induce hyperthermia in a large tissue volume. The purpose of this work is to investigate theoretically how glioblastoma multiforme (GBM) can be effectively treated by combining the fast hyperthermia generated by this focused ultrasound device with external beam radiotherapy. Methods/Design To investigate the effect of tumor growth, we have developed a mathematical description of GBM proliferation and diffusion in the context of reaction–diffusion theory. In addition, we have formulated equations describing the impact of radiotherapy and heat on GBM in the reaction–diffusion equation, including tumor regrowth by stem cells. This formulation has been used to predict the effectiveness of the combination treatment for a realistic focused ultrasound heating scenario. Our results show that patient survival could be significantly improved by this combined treatment modality. Discussion High priority should be given to experiments to validate the therapeutic benefit predicted by our model. Electronic supplementary material The online version of this article (doi:10.1186/s40349-016-0078-3) contains supplementary material, which is available to authorized users.
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