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Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. NATURE COMPUTATIONAL SCIENCE 2024; 4:184-191. [PMID: 38532133 PMCID: PMC11102043 DOI: 10.1038/s43588-024-00607-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/12/2024] [Indexed: 03/28/2024]
Abstract
Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - B Mehrad
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | | | - N Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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2
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Roh TH, Kim SH. Supramaximal Resection for Glioblastoma: Redefining the Extent of Resection Criteria and Its Impact on Survival. Brain Tumor Res Treat 2023; 11:166-172. [PMID: 37550815 PMCID: PMC10409622 DOI: 10.14791/btrt.2023.0012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/04/2023] [Accepted: 05/09/2023] [Indexed: 08/09/2023] Open
Abstract
Glioblastomas (GBMs) are the most common and aggressive primary brain tumors, and despite advances in treatment, prognosis remains poor. The extent of resection has been widely recognized as a key factor affecting survival outcomes in GBM patients. The surgical principle of "maximal safe resection" has been widely applied to balance tumor removal and neurological function preservation. Historically, T1-contrast enhanced (T1CE) extent of resection has been the focus of research; however, the "supramaximal resection" concept has emerged, advocating for even greater tumor resection while maintaining neurological function. Recent studies have demonstrated potential survival benefits associated with resection beyond T1CE extent in GBMs. This review explores the developing consensus and newly established criteria for "supramaximal resection" in GBMs, with a focus on T2-extent of resection. Systematic reviews and meta-analyses on supramaximal resection are summarized, and the Response Assessment in Neuro-Oncology (RANO) resect group classification for extent of resection is introduced. The evolving understanding of the role of supramaximal resection in GBMs may lead to improved patient outcomes and more objective criteria for evaluating the extent of tumor resection.
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Affiliation(s)
- Tae Hoon Roh
- Department of Neurosurgery, Brain Tumor Center, Ajou University Hospital, Ajou University School of Medicine, Suwon, Korea
| | - Se-Hyuk Kim
- Department of Neurosurgery, Brain Tumor Center, Ajou University Hospital, Ajou University School of Medicine, Suwon, Korea.
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3
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Yan F, Ha JH, Yan Y, Ton SB, Wang C, Mutembei B, Alhajeri ZA, McNiel AF, Keddissi AJ, Zhang Q, Jayaraman M, Dhanasekaran DN, Tang Q. Optical Coherence Tomography of Tumor Spheroids Identifies Candidates for Drug Repurposing in Ovarian Cancer. IEEE Trans Biomed Eng 2023; 70:1891-1901. [PMID: 37015385 PMCID: PMC10482500 DOI: 10.1109/tbme.2022.3231835] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Multicellular tumor spheroids (MCTs) are indispensable models for evaluating drug efficacy for precision cancer therapeutic strategies as well as for repurposing FDA-approved drugs for ovarian cancer. However, current imaging techniques cannot provide effective monitoring of pathological responses due to shallow penetration and experimentally operative destruction. We plan to utilize a noninvasive optical imaging tool to achieve in vivo longitudinal monitoring of the growth of MCTs and therapeutic responses to repurpose three FDA-approved drugs for ovarian cancer therapy. METHODS A swept-source optical coherence tomography (SS-OCT) system was used to monitor the volume growth of MCTs over 11 days. Three inhibitors of 2-Methoxyestradiol (2-ME), AZD1208, and R-Ketorolac (R-keto) with concentrations of 1, 10, and 25 µM were employed to treat ovarian MCTs on day 5. Three-dimensional (3D), intrinsic optical attenuation contrast, and degree of uniformity were applied to analyze the therapeutic effect of these inhibitors on ovarian MCTs. RESULTS We found that 2-ME, AZD1208, and R-keto with concentration of 10 and 25 µM significantly inhibited the volume growth of ovarian MCTs. There was no effect to necrotic tissues from all concentrations of 2-ME, AZD1208, and R-keto inhibitors from our OCT results. 2-ME and AZD1208 inhibited the growth of high uniformity tissues within MCTs and higher concentrations provided more significant inhibitory effects. CONCLUSION Our results indicated that OCT was capable and reliable to monitor the therapeutic effect of inhibitors to ovarian MCTs and it can be used for the rapid characterization of novel therapeutics for ovarian cancers in the future.
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Affiliation(s)
- Feng Yan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Ji-Hee Ha
- Department of Cell Biology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Yuyang Yan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Sam B. Ton
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Chen Wang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Bornface Mutembei
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Zaid A. Alhajeri
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Aubrey F. McNiel
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Andrew J. Keddissi
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Qinghao Zhang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Muralidharan Jayaraman
- Department of Cell Biology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Danny N. Dhanasekaran
- Department of Cell Biology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Qinggong Tang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
- Institute for Biomedical Engineering, Science, and Technology (IBEST), University of Oklahoma, Norman, OK 73019, USA
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Wilson N, Drapaca CS, Enderling H, Caudell JJ, Wilkie KP. Modelling Radiation Cancer Treatment with a Death-Rate Term in Ordinary and Fractional Differential Equations. Bull Math Biol 2023; 85:47. [PMID: 37186175 PMCID: PMC10127975 DOI: 10.1007/s11538-023-01139-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 02/28/2023] [Indexed: 05/17/2023]
Abstract
Fractional calculus has recently been applied to the mathematical modelling of tumour growth, but its use introduces complexities that may not be warranted. Mathematical modelling with differential equations is a standard approach to study and predict treatment outcomes for population-level and patient-specific responses. Here, we use patient data of radiation-treated tumours to discuss the benefits and limitations of introducing fractional derivatives into three standard models of tumour growth. The fractional derivative introduces a history-dependence into the growth function, which requires a continuous death-rate term for radiation treatment. This newly proposed radiation-induced death-rate term improves computational efficiency in both ordinary and fractional derivative models. This computational speed-up will benefit common simulation tasks such as model parameterization and the construction and running of virtual clinical trials.
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Affiliation(s)
- Nicole Wilson
- Department of Mathematics, Toronto Metropolitan University, Toronto, Canada
| | - Corina S Drapaca
- Engineering Science and Mechanics, Pennsylvania State University, University Park, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Jimmy J Caudell
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Kathleen P Wilkie
- Department of Mathematics, Toronto Metropolitan University, Toronto, Canada.
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5
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Tunç B, Rodin GJ, Yankeelov TE. Implementing multiphysics models in FEniCS: Viscoelastic flows, poroelasticity, and tumor growth. BIOMEDICAL ENGINEERING ADVANCES 2023. [DOI: 10.1016/j.bea.2023.100074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
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Bortfeld T, Buti G. Modeling the propagation of tumor fronts with shortest path and diffusion models—implications for the definition of the clinical target volume. Phys Med Biol 2022; 67. [PMID: 35817046 PMCID: PMC9388053 DOI: 10.1088/1361-6560/ac8043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 07/11/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. The overarching objective is to make the definition of the clinical target volume (CTV) in radiation oncology less subjective and more scientifically based. The specific objective of this study is to investigate similarities and differences between two methods that model tumor spread beyond the visible gross tumor volume (GTV): (1) the shortest path model, which is the standard method of adding a geometric GTV-CTV margin, and (2) the reaction-diffusion model. Approach. These two models to capture the invisible tumor ‘fire front’ are defined and compared in mathematical terms. The models are applied to example cases that represent tumor spread in non-uniform and anisotropic media with anatomical barriers. Main results. The two seemingly disparate models bring forth traveling waves that can be associated with the front of tumor growth outward from the GTV. The shape of the fronts is similar for both models. Differences are seen in cases where the diffusive flow is reduced due to anatomical barriers, and in complex spatially non-uniform cases. The diffusion model generally leads to smoother fronts. The smoothness can be controlled with a parameter defined by the ratio of the diffusion coefficient and the proliferation rate. Significance. Defining the CTV has been described as the weakest link of the radiotherapy chain. There are many similarities in the mathematical description and the behavior of the common geometric GTV-CTV expansion method, and the definition of the CTV tumor front via the reaction-diffusion model. Its mechanistic basis and the controllable smoothness make the diffusion model an attractive alternative to the standard GTV-CTV margin model.
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Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022; 187:114367. [PMID: 35654212 PMCID: PMC11165420 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/25/2022] [Accepted: 05/25/2022] [Indexed: 11/01/2022]
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Maguy Farhat
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Chase Christenson
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Curl
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Caroline Chung
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA
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Wu C, Lorenzo G, Hormuth DA, Lima EABF, Slavkova KP, DiCarlo JC, Virostko J, Phillips CM, Patt D, Chung C, Yankeelov TE. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. BIOPHYSICS REVIEWS 2022; 3:021304. [PMID: 35602761 PMCID: PMC9119003 DOI: 10.1063/5.0086789] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/29/2022] [Indexed: 12/11/2022]
Abstract
Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | | | - Kalina P. Slavkova
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Debra Patt
- Texas Oncology, Austin, Texas 78731, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas 77030, USA
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Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021; 16:5309-5338. [PMID: 34552262 PMCID: PMC9753909 DOI: 10.1038/s41596-021-00617-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - Anum S Kazerouni
- Departments of Biomedical Engineering, Austin, TX, USA
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - John Virostko
- Livestrong Cancer Institutes, Austin, TX, USA
- Departments of Diagnostic Medicine, Austin, TX, USA
- Departments of Oncology, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Julie C DiCarlo
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Ekrut
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | | | - Boone Goodgame
- Departments of Oncology, Austin, TX, USA
- Departments of Internal Medicine, The University of Texas at Austin, Austin, Texas, USA
- Seton Hospital, Austin, TX, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA.
- Livestrong Cancer Institutes, Austin, TX, USA.
- Departments of Biomedical Engineering, Austin, TX, USA.
- Departments of Diagnostic Medicine, Austin, TX, USA.
- Departments of Oncology, Austin, TX, USA.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Yan F, Gunay G, Valerio TI, Wang C, Wilson JA, Haddad MS, Watson M, Connell MO, Davidson N, Fung KM, Acar H, Tang Q. Characterization and quantification of necrotic tissues and morphology in multicellular ovarian cancer tumor spheroids using optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2021; 12:3352-3371. [PMID: 34221665 PMCID: PMC8221959 DOI: 10.1364/boe.425512] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/03/2021] [Accepted: 05/07/2021] [Indexed: 05/02/2023]
Abstract
The three-dimensional (3D) tumor spheroid model is a critical tool for high-throughput ovarian cancer research and anticancer drug development in vitro. However, the 3D structure prevents high-resolution imaging of the inner side of the spheroids. We aim to visualize and characterize 3D morphological and physiological information of the contact multicellular ovarian tumor spheroids growing over time. We intend to further evaluate the distinctive evolutions of the tumor spheroid and necrotic tissue volumes in different cell numbers and determine the most appropriate mathematical model for fitting the growth of tumor spheroids and necrotic tissues. A label-free and noninvasive swept-source optical coherence tomography (SS-OCT) imaging platform was applied to obtain two-dimensional (2D) and 3D morphologies of ovarian tumor spheroids over 18 days. Ovarian tumor spheroids of two different initial cell numbers (5,000- and 50,000- cells) were cultured and imaged (each day) over the time of growth in 18 days. Four mathematical models (Exponential-Linear, Gompertz, logistic, and Boltzmann) were employed to describe the growth kinetics of the tumor spheroids volume and necrotic tissues. Ovarian tumor spheroids have different growth curves with different initial cell numbers and their growths contain different stages with various growth rates over 18 days. The volumes of 50,000-cells spheroids and the corresponding necrotic tissues are larger than that of the 5,000-cells spheroids. The formation of necrotic tissue in 5,000-cells numbers is slower than that in the 50,000-cells ones. Moreover, the Boltzmann model exhibits the best fitting performance for the growth of tumor spheroids and necrotic tissues. Optical coherence tomography (OCT) can serve as a promising imaging modality to visualize and characterize morphological and physiological features of multicellular ovarian tumor spheroids. The Boltzmann model integrating with 3D OCT data of ovarian tumor spheroids provides great potential for high-throughput cancer research in vitro and aiding in drug development.
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Affiliation(s)
- Feng Yan
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
- Equal contribution
| | - Gokhan Gunay
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
- Equal contribution
| | - Trisha I Valerio
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
- Equal contribution
| | - Chen Wang
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
| | - Jayla A Wilson
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
| | - Majood S Haddad
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
| | - Maegan Watson
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
| | - Michael O Connell
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
| | - Noah Davidson
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
| | - Kar-Ming Fung
- Department of Pathology, The University of Oklahoma Health Sciences Center, Oklahoma City 73104, USA
- Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City 73104, USA
| | - Handan Acar
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
- Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City 73104, USA
| | - Qinggong Tang
- Stephenson School of Biomedical Engineering, University of Oklahoma, OK 73019, USA
- Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City 73104, USA
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Hormuth DA, Al Feghali KA, Elliott AM, Yankeelov TE, Chung C. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation. Sci Rep 2021; 11:8520. [PMID: 33875739 PMCID: PMC8055874 DOI: 10.1038/s41598-021-87887-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 03/30/2021] [Indexed: 12/16/2022] Open
Abstract
High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T1-weighted, and T2-FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: - 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, 78712-1229, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Austin, TX, USA.
| | - Karine A Al Feghali
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Andrew M Elliott
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, 78712-1229, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Austin, TX, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, Austin, TX, USA
- Department of Oncology, The University of Texas at Austin, Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Austin, TX, USA
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
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12
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Advani D, Sharma S, Kumari S, Ambasta RK, Kumar P. Precision Oncology, Signaling and Anticancer Agents in Cancer Therapeutics. Anticancer Agents Med Chem 2021; 22:433-468. [PMID: 33687887 DOI: 10.2174/1871520621666210308101029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/05/2021] [Accepted: 01/12/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND The global alliance for genomics and healthcare facilities provides innovational solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention and treatment based on patients' genetic profile. Advancements in "omics" techniques, next-generation sequencing, artificial intelligence and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures. METHOD Data were collected from Pubmed and Google scholar using keywords: "Precision medicine", "precision medicine and cancer", "anticancer agents in precision medicine" and reviewed comprehensively. RESULTS Personalized therapeutics including immunotherapy, cancer vaccines, serve as a groundbreaking solution for cancer treatment. Herein, we take a measurable view of precision therapies and novel diagnostic approaches targeting cancer treatment. The contemporary applications of precision medicine have also been described along with various hurdles identified in the successful establishment of precision therapeutics. CONCLUSION This review highlights the key breakthroughs related to immunotherapies, targeted anticancer agents, and target interventions related to cancer signaling mechanisms. The success story of this field in context to drug resistance, safety, patient survival and in improving quality of life is yet to be elucidated. We conclude that, in the near future, the field of individualized treatments may truly revolutionize the nature of cancer patient care.
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Affiliation(s)
- Dia Advani
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
| | - Sudhanshu Sharma
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
| | - Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory Shahbad Daulatpur, Bawana Road, Delhi 110042. India
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Chatterjee K, Atay N, Abler D, Bhargava S, Sahoo P, Rockne RC, Munson JM. Utilizing Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Analyze Interstitial Fluid Flow and Transport in Glioblastoma and the Surrounding Parenchyma in Human Patients. Pharmaceutics 2021; 13:pharmaceutics13020212. [PMID: 33557069 PMCID: PMC7913790 DOI: 10.3390/pharmaceutics13020212] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/22/2021] [Accepted: 01/23/2021] [Indexed: 01/04/2023] Open
Abstract
Background: Glioblastoma (GBM) is the deadliest and most common brain tumor in adults, with poor survival and response to aggressive therapy. Limited access of drugs to tumor cells is one reason for such grim clinical outcomes. A driving force for therapeutic delivery is interstitial fluid flow (IFF), both within the tumor and in the surrounding brain parenchyma. However, convective and diffusive transport mechanisms are understudied. In this study, we examined the application of a novel image analysis method to measure fluid flow and diffusion in GBM patients. Methods: Here, we applied an imaging methodology that had been previously tested and validated in vitro, in silico, and in preclinical models of disease to archival patient data from the Ivy Glioblastoma Atlas Project (GAP) dataset. The analysis required the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is readily available in the database. The analysis results, which consisted of IFF flow velocity and diffusion coefficients, were then compared to patient outcomes such as survival. Results: We characterized IFF and diffusion patterns in patients. We found strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Analyzing overall magnitudes indicated a significant correlation with both age and survival in this patient cohort. Additionally, we found that neither tumor size nor resection significantly altered the velocity magnitude. Lastly, we mapped the flow pathways in patient tumors and found a variability in the degree of directionality that we hypothesize may lead to information concerning treatment, invasive spread, and progression in future studies. Conclusions: An analysis of standard DCE-MRI in patients with GBM offers more information regarding IFF and transport within and around the tumor, shows that IFF is still detected post-resection, and indicates that velocity magnitudes correlate with patient prognosis.
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Affiliation(s)
- Krishnashis Chatterjee
- Department of Biomedical Engineering & Mechanics, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA 24016, USA; (K.C.); (N.A.); (S.B.)
| | - Naciye Atay
- Department of Biomedical Engineering & Mechanics, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA 24016, USA; (K.C.); (N.A.); (S.B.)
| | - Daniel Abler
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA; (D.A.); (P.S.); (R.C.R.)
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland
| | - Saloni Bhargava
- Department of Biomedical Engineering & Mechanics, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA 24016, USA; (K.C.); (N.A.); (S.B.)
| | - Prativa Sahoo
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA; (D.A.); (P.S.); (R.C.R.)
| | - Russell C. Rockne
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA; (D.A.); (P.S.); (R.C.R.)
| | - Jennifer M. Munson
- Department of Biomedical Engineering & Mechanics, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA 24016, USA; (K.C.); (N.A.); (S.B.)
- Correspondence: ; Tel.: +1-(540)-532-6392
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14
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Jarrett AM, Hormuth DA, Wu C, Kazerouni AS, Ekrut DA, Virostko J, Sorace AG, DiCarlo JC, Kowalski J, Patt D, Goodgame B, Avery S, Yankeelov TE. Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data. Neoplasia 2020; 22:820-830. [PMID: 33197744 PMCID: PMC7677708 DOI: 10.1016/j.neo.2020.10.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 12/12/2022]
Abstract
The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA; Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA; Livestrong Cancer Institutes, Austin, TX, USA
| | - Chengyue Wu
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Anum S Kazerouni
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - David A Ekrut
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - John Virostko
- Livestrong Cancer Institutes, Austin, TX, USA; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA; Department of Oncology, The University of Texas at Austin, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA; Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Julie C DiCarlo
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - Jeanne Kowalski
- Livestrong Cancer Institutes, Austin, TX, USA; Department of Oncology, The University of Texas at Austin, Austin, TX, USA
| | | | - Boone Goodgame
- Department of Oncology, The University of Texas at Austin, Austin, TX, USA; Department of Internal Medicine, The University of Texas at Austin, Austin, TX, USA; Seton Hospital, Austin, TX, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA; Livestrong Cancer Institutes, Austin, TX, USA; Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA; Department of Oncology, The University of Texas at Austin, Austin, TX, USA; Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA.
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15
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Jarrett AM, Hormuth DA, Adhikarla V, Sahoo P, Abler D, Tumyan L, Schmolze D, Mortimer J, Rockne RC, Yankeelov TE. Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer. Sci Rep 2020; 10:20518. [PMID: 33239688 PMCID: PMC7688955 DOI: 10.1038/s41598-020-77397-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/29/2020] [Indexed: 12/20/2022] Open
Abstract
While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - Vikram Adhikarla
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
| | - Prativa Sahoo
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
| | - Daniel Abler
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Lusine Tumyan
- Department of Radiology, City of Hope National Medical Center, Duarte, CA, USA
| | - Daniel Schmolze
- Department of Pathology, City of Hope National Medical Center, Duarte, CA, USA
| | - Joanne Mortimer
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Russell C Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA.
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA.
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA.
- Department of Oncology, The University of Texas at Austin, Austin, TX, USA.
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W Dean Keeton Street Stop C0800, Austin, TX, 78712, USA.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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16
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Dehghan M, Narimani N. Radial basis function-generated finite difference scheme for simulating the brain cancer growth model under radiotherapy in various types of computational domains. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105641. [PMID: 32726719 DOI: 10.1016/j.cmpb.2020.105641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 06/28/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES We extend the original mathematical model, i.e., Swanson's reaction-diffusion equation to the surfaces with no boundary, and we find a new numerical method based on a meshless approach for solving numerically Swanson's reaction-diffusion model in the square and on the sphere. METHODS To solve numerically the Swanson's reaction-diffusion model and its extension version, a collocation meshless technique, namely radial basis function-generated finite difference (RBF-FD) scheme is employed for approximating the spatial variables in the square domain and on the sphere, respectively. Also, to approximate the time variable of the studied models, a first-order semi-implicit backward Euler scheme is used. The resulting fully discrete scheme is a linear system of algebraic equations per time step that is solved via the biconjugate gradient stabilized (BiCGSTAB) iterative algorithm with a zero-fill incomplete lower-upper (ILU) preconditioner. RESULTS The numerical simulations show the growth of untreated and treated brain tumors with radiotherapy using estimated and clinical data (given from magnetic resonance imaging (MRI) scans of patients). Moreover, the results reported here can be used for improving the treatment strategies of the invasive brain tumor. CONCLUSIONS Using the developed numerical scheme in this paper, we can simulate the behavior of the invasive form of brain tumor response to radiotherapy. Also, we can see the effects of radiation response on the brain tumor cell concentration of individual patients. The proposed meshless technique, which is applied for solving numerically the studied model, does not depend on any background mesh or triangulation for approximation in comparison with mesh-dependent methods. Moreover, we apply this technique to the sphere via any set of distributed points easily.
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Affiliation(s)
- Mehdi Dehghan
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914, Iran.
| | - Niusha Narimani
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914, Iran.
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Henscheid N. Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:6531-6556. [PMID: 33378865 PMCID: PMC7780222 DOI: 10.3934/mbe.2020341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The use of mathematical tumor growth models coupled to noisy imaging data has been suggested as a possible component in the push towards precision medicine. We discuss the generation of population and patient-specific virtual populations in this context, providing in silico experiments to demonstrate how intra- and inter-patient heterogeneity can be estimated by applying rigorous statistical procedures to noisy molecular imaging data, and how the noise properties of such data can be analyzed to estimate uncertainties in predicted patient outcomes.
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18
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105443. [PMID: 32311510 DOI: 10.1016/j.cmpb.2020.105443] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 02/17/2020] [Accepted: 03/08/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Glioblastoma multiforme (GBM) is the most frequent primary brain tumor in adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In Silico models of GBM growth provide an appropriate foundation for analysis and comparison of different regimens. We propose a mathematical frame for patient specific design of optimal chemotherapy regimens for GBM patients. METHODS The proposed frame includes online interaction of a virtual GBM with an optimizing agent. Spatiotemporal dynamics of GBM growth and its response to TMZ are simulated with a three dimensional hybrid cellular automaton. Q learning is tailored to the virtual GBM for treatment optimization aimed at minimizing tumor size at the end of treatment course. Q learning consists of a learning agent that interacts with the virtual GBM. System state is affected by the agent decisions and the obtained rewards guide Q learning to the optimal schedule. RESULTS Computational results confirm that the optimal chemotherapy schedule depends on some patient specific parameters including body weight, tumor size and its position in the brain. Furthermore, the algorithm is used for scheduling 2100 mg of TMZ on a virtual GBM and the obtained schedule is to administer150 mg of TMZ every other day. The obtained schedule is compared to the standard 7/14 regimen and the results show that it is superior to the 7/14 regimen in minimizing tumor size. CONCLUSION The proposed frame is an appropriate decision support system for patient specific design of TMZ administration regimens on GBM patients. Also, since the obtained optimal schedule outperforms the standard 7/14 regimen, it is worthy of further clinical testing.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - Madjid Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1969764499, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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19
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Comas S, Luguera E, Molero J, Balaña C, Estival A, Castañer S, Carrato C, Hostalot C, Teixidor P, Villà S. Influence of glioblastoma contact with the subventricular zone on survival and recurrence patterns. Clin Transl Oncol 2020; 23:554-564. [PMID: 32728970 DOI: 10.1007/s12094-020-02448-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/02/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND There is growing evidence that the subventricular zone (SVZ) may be involved in both the initiation and progression of glioblastoma (GB). We aimed to assess tumor proximity to the SVZ as a potential prognostic factor in GB. METHOD Retrospective study of 133 patients diagnosed with primary GB who underwent surgery followed by temozolomide-based chemoradiation between 2010 and 2016. All lesions were classified according to their anatomic relation with the SVZ. We determined the effect of tumor contact with the SVZ on progression-free survival (PFS), overall survival (OS), type, and patterns of recurrence. RESULTS At a median follow-up of 18.6 months (95% CI 15.9-21.2), PFS and OS were 7.5 (95% CI 6.7-8.3) and 13.9 (95% CI 10.9-16.9) months, respectively. On the univariate analyses, initial contact with the SVZ was a factor for poor prognosis for both PFS (6.1 vs. 8.7 months; p = 0.006) and OS (10.6 vs. 17.9 months; p = 0.037). On the multivariate analysis, tumor contact with the SVZ remained statistically significant for PFS, but not OS. Patients with SVZ-contacting tumors presented a higher rate of aggressive clinical progression (30.9% vs. 11.3%; p = 0.007) and contralateral relapse patterns (23.4% vs. 9.1%; p = 0.048). CONCLUSIONS Our results suggest that glioblastoma contact with the SVZ appears to be an independent prognostic factor for poor PFS. The presence of an SVZ-contacting tumor was associated with more aggressive recurrences and a higher rate of contralateral relapses. These findings suggest that this variable may be a new prognostic factor in glioblastoma.
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Affiliation(s)
- S Comas
- Radiation Oncology, Institut Català D'Oncologia, c/ del Canyet SN, 08916, Badalona, Catalonia, Spain
| | - E Luguera
- Physics, Institut Català D'Oncologia, Badalona, Catalonia, Spain
| | - J Molero
- Physics, Institut Català D'Oncologia, Badalona, Catalonia, Spain
| | - C Balaña
- Medical Oncology, Institut Català D'Oncologia, Badalona, Catalonia, Spain
| | - A Estival
- Medical Oncology, Institut Català D'Oncologia, Badalona, Catalonia, Spain
| | - S Castañer
- Neuroradiology, Institut de Diagnòstic Per La Imatge, Badalona, Catalonia, Spain
| | - C Carrato
- Pathology. Hospital Universitari Germans Trias I Pujol, Badalona, Catalonia, Spain
| | - C Hostalot
- Neurosurgery. Hospital Universitari Germans Trias I Pujol, Badalona, Catalonia, Spain
| | - P Teixidor
- Neurosurgery. Hospital Universitari Germans Trias I Pujol, Badalona, Catalonia, Spain
| | - S Villà
- Radiation Oncology, Institut Català D'Oncologia, c/ del Canyet SN, 08916, Badalona, Catalonia, Spain.
- Universitat Autònoma de Barcelona, Badalona, Catalonia, Spain.
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Hawkins-Daarud A, Johnston SK, Swanson KR. Quantifying Uncertainty and Robustness in a Biomathematical Model-Based Patient-Specific Response Metric for Glioblastoma. JCO Clin Cancer Inform 2020; 3:1-8. [PMID: 30758984 PMCID: PMC6633916 DOI: 10.1200/cci.18.00066] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Purpose Glioblastomas, lethal primary brain tumors, are known for their heterogeneity and invasiveness. A growing body of literature has been developed demonstrating the clinical relevance of a biomathematical model, the proliferation-invasion model, of glioblastoma growth. Of interest here is the development of a treatment response metric, days gained (DG). This metric is based on individual tumor kinetics estimated through segmented volumes of hyperintense regions on T1-weighted gadolinium-enhanced and T2-weighted magnetic resonance images. This metric was shown to be prognostic of time to progression. Furthermore, it was shown to be more prognostic of outcome than standard response metrics. Although promising, the original article did not account for uncertainty in the calculation of the DG metric, leaving the robustness of this cutoff in question. Methods We harnessed the Bayesian framework to consider the impact of two sources of uncertainty: (1) image acquisition and (2) interobserver error in image segmentation. We first used synthetic data to characterize what nonerror variants are influencing the final uncertainty in the DG metric. We then considered the original patient cohort to investigate clinical patterns of uncertainty and to determine how robust this metric is for predicting time to progression and overall survival. Results Our results indicate that the key clinical variants are the time between pretreatment images and the underlying tumor growth kinetics, matching our observations in the clinical cohort. Finally, we demonstrated that for this cohort, there was a continuous range of cutoffs between 94 and 105 for which the prediction of the time to progression was over 80% reliable. Conclusion Although additional validation must be performed, this work represents a key step in ascertaining the clinical utility of this metric.
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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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22
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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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23
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Karolak A, Markov DA, McCawley LJ, Rejniak KA. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 2019; 15:rsif.2017.0703. [PMID: 29367239 DOI: 10.1098/rsif.2017.0703] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/02/2018] [Indexed: 02/06/2023] Open
Abstract
A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.
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Affiliation(s)
- Aleksandra Karolak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Dmitry A Markov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Lisa J McCawley
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA .,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
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24
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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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25
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Setty Y. eBrain: a Three Dimensional Simulation Tool to Study Drug Delivery in the Brain. Sci Rep 2019; 9:6162. [PMID: 30992468 PMCID: PMC6467991 DOI: 10.1038/s41598-019-42261-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 02/20/2019] [Indexed: 12/18/2022] Open
Abstract
Neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease are severe disorders with acute symptoms that gradually progress. In the course of developing disease-modifying treatments for neurodegenerative disorders there is a need to develop novel strategies to increase efficacy of drugs and accelerate the development process. We developed a tool for simulating drug delivery in the brain by translating MRI data into an interactive 3D model. This tool, the eBrain, superimposes simulated drug diffusion and tissue uptake by inferring from the MRI data with a seamless display from any angle, magnification, or position. We discuss a representative implementation of eBrain that is inspired by clinical data in which insulin is intranasally administered to Alzheimer patients. Using extensive analysis of multiple eBrain simulations with varying parameters, we show the potential for eBrain to determine the optimal dosage to ensure drug delivery without overdosing the tissue. Specifically, we examined the efficacy of combined drug doses and potential compounds for tissue stimulation. Interestingly, our analysis uncovered that the drug efficacy is inferred from tissue intensity levels. Finally, we discuss the potential of eBrain and possible applications of eBrain to aid both inexperienced and experienced medical professionals as well as patients.
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Affiliation(s)
- Yaki Setty
- Gateway Institute for Brain Research, 3321 College Avenue, Davie, 33314, Florida, USA.
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26
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Mahlbacher GE, Reihmer KC, Frieboes HB. Mathematical modeling of tumor-immune cell interactions. J Theor Biol 2019; 469:47-60. [PMID: 30836073 DOI: 10.1016/j.jtbi.2019.03.002] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 02/14/2019] [Accepted: 03/01/2019] [Indexed: 12/22/2022]
Abstract
The anti-tumor activity of the immune system is increasingly recognized as critical for the mounting of a prolonged and effective response to cancer growth and invasion, and for preventing recurrence following resection or treatment. As the knowledge of tumor-immune cell interactions has advanced, experimental investigation has been complemented by mathematical modeling with the goal to quantify and predict these interactions. This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models. The focus is on work published in the past decade, incorporating one or more immune cell types and evaluating immune cell effects on tumor progression. Due to their relevance to cancer, the following immune cells and their combinations are described: macrophages, Cytotoxic T Lymphocytes, Natural Killer cells, dendritic cells, T regulatory cells, and CD4+ T helper cells. Although important insight has been gained from a mathematical modeling perspective, the development of models incorporating patient-specific data remains an important goal yet to be realized for potential clinical benefit.
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Affiliation(s)
| | - Kara C Reihmer
- Department of Bioengineering, University of Louisville, KY, USA
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, KY, USA; James Graham Brown Cancer Center, University of Louisville, KY, USA; Department of Pharmacology & Toxicology, University of Louisville, KY, USA.
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Use of Generative Disease Models for Analysis and Selection of Radiomic Features in PET. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2018.2844171] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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28
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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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Jarrett AM, Lima EABF, Hormuth DA, McKenna MT, Feng X, Ekrut DA, Resende ACM, Brock A, Yankeelov TE. Mathematical models of tumor cell proliferation: A review of the literature. Expert Rev Anticancer Ther 2018; 18:1271-1286. [PMID: 30252552 PMCID: PMC6295418 DOI: 10.1080/14737140.2018.1527689] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
INTRODUCTION A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. Areas covered: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. Expert commentary: The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation.
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Affiliation(s)
- Angela M Jarrett
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
| | - Ernesto A B F Lima
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - David A Hormuth
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
| | - Matthew T McKenna
- c Department of Biomedical Engineering , Vanderbilt University , Nashville , USA
| | - Xinzeng Feng
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - David A Ekrut
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
| | - Anna Claudia M Resende
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- d Department of Computational Modeling , National Laboratory for Scientific Computing , Petrópolis , Brazil
| | - Amy Brock
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
- e Department of Biomedical Engineering , The University of Texas at Austin , Austin , USA
| | - Thomas E Yankeelov
- a Institute for Computational Engineering and Sciences , The University of Texas at Austin , Austin , USA
- b Livestrong Cancer Institutes , The University of Texas at Austin , Austin , USA
- e Department of Biomedical Engineering , The University of Texas at Austin , Austin , USA
- f Department of Diagnostic Medicine , The University of Texas at Austin , Austin , USA
- g Department of Oncology , The University of Texas at Austin , Austin , USA
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30
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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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31
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Jacobs JJ, Capek S, Spinner RJ, Swanson KR. Mathematical model of perineural tumor spread: a pilot study. Acta Neurochir (Wien) 2018; 160:655-661. [PMID: 29264779 DOI: 10.1007/s00701-017-3423-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 12/03/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND Perineural spread (PNS) of pelvic cancer along the lumbosacral plexus is an emerging explanation for neoplastic lumbosacral plexopathy (nLSP) and an underestimated source of patient morbidity and mortality. Despite the increased incidence of PNS, these patients are often times a clinical conundrum-to diagnose and to treat. Building on previous results in modeling glioblastoma multiforme (GBM), we present a mathematical model for predicting the course and extent of the PNS of recurrent tumors. METHODS We created three-dimensional models of perineurally spreading tumor along the lumbosacral plexus from consecutive magnetic resonance imaging scans of two patients (one each with prostate cancer and cervical cancer). We adapted and applied a previously reported mathematical model of GBM to progression of tumor growth along the nerves on an anatomical model obtained from a healthy subject. RESULTS We were able to successfully model and visualize perineurally spreading pelvic cancer in two patients; average growth rates were 60.7 mm/year for subject 1 and 129 mm/year for subject 2. The model correlated well with extent of PNS on MRI scans at given time points. CONCLUSIONS This is the first attempt to model perineural tumor spread and we believe that it provides a glimpse into the future of disease progression monitoring. Every tumor and every patient are different, and the possibility to report treatment response using a unified scale-as "days gained"-will be a necessity in the era of individualized medicine. We hope our work will serve as a springboard for future connections between mathematics and medicine.
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Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients. PLoS Comput Biol 2018; 14:e1005924. [PMID: 29293494 PMCID: PMC5766249 DOI: 10.1371/journal.pcbi.1005924] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2017] [Revised: 01/12/2018] [Accepted: 12/12/2017] [Indexed: 12/15/2022] Open
Abstract
Human primary glioblastomas (GBM) often harbor mutations within the epidermal growth factor receptor (EGFR). Treatment of EGFR-mutant GBM cell lines with the EGFR/HER2 tyrosine kinase inhibitor lapatinib can effectively induce cell death in these models. However, EGFR inhibitors have shown little efficacy in the clinic, partly because of inappropriate dosing. Here, we developed a computational approach to model the in vitro cellular dynamics of the EGFR-mutant cell line SF268 in response to different lapatinib concentrations and dosing schedules. We then used this approach to identify an effective treatment strategy within the clinical toxicity limits of lapatinib, and developed a partial differential equation modeling approach to study the in vivo GBM treatment response by taking into account the heterogeneous and diffusive nature of the disease. Despite the inability of lapatinib to induce tumor regressions with a continuous daily schedule, our modeling approach consistently predicts that continuous dosing remains the best clinically feasible strategy for slowing down tumor growth and lowering overall tumor burden, compared to pulsatile schedules currently known to be tolerated, even when considering drug resistance, reduced lapatinib tumor concentrations due to the blood brain barrier, and the phenotypic switch from proliferative to migratory cell phenotypes that occurs in hypoxic microenvironments. Our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment schedules in search for optimal dosing strategies for glioblastoma and other cancer types.
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Sehl ME, Wicha MS. Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response. Methods Mol Biol 2018; 1711:333-349. [PMID: 29344897 PMCID: PMC6322404 DOI: 10.1007/978-1-4939-7493-1_16] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Mathematical models of cancer stem cells are useful in translational cancer research for facilitating the understanding of tumor growth dynamics and for predicting treatment response and resistance to combined targeted therapies. In this chapter, we describe appealing aspects of different methods used in mathematical oncology and discuss compelling questions in oncology that can be addressed with these modeling techniques. We describe a simplified version of a model of the breast cancer stem cell niche, illustrate the visualization of the model, and apply stochastic simulation to generate full distributions and average trajectories of cell type populations over time. We further discuss the advent of single-cell data in studying cancer stem cell heterogeneity and how these data can be integrated with modeling to advance understanding of the dynamics of invasive and proliferative populations during cancer progression and response to therapy.
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Affiliation(s)
- Mary E Sehl
- Division of Hematology-Oncology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Max S Wicha
- Department of Internal Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA.
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Hormuth DA, Eldridge SL, Weis JA, Miga MI, Yankeelov TE. Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details. Methods Mol Biol 2018; 1711:225-241. [PMID: 29344892 DOI: 10.1007/978-1-4939-7493-1_11] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.
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Affiliation(s)
- David A Hormuth
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA.
| | - Stephanie L Eldridge
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA.,Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC, USA.,Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC, USA
| | - Michael I Miga
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Department of Radiology, Vanderbilt University, Nashville, TN, USA.,Department of Radiological Sciences, Vanderbilt University, Nashville, TN, USA.,Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
| | - Thomas E Yankeelov
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA. .,Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA. .,Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA. .,Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA.
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35
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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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36
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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: 19] [Impact Index Per Article: 2.7] [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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37
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Oraiopoulou ME, Tzamali E, Tzedakis G, Vakis A, Papamatheakis J, Sakkalis V. In Vitro/In Silico Study on the Role of Doubling Time Heterogeneity among Primary Glioblastoma Cell Lines. BIOMED RESEARCH INTERNATIONAL 2017; 2017:8569328. [PMID: 29226151 PMCID: PMC5684616 DOI: 10.1155/2017/8569328] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 09/18/2017] [Indexed: 12/14/2022]
Abstract
The application of accurate cancer predictive algorithms validated with experimental data is a field concerning both basic researchers and clinicians, especially regarding a highly aggressive form of cancer, such as Glioblastoma. In an aim to enhance prediction accuracy in realistic patient-specific environments, accounting for both inter- and intratumoral heterogeneity, we use patient-derived Glioblastoma cells from different patients. We focus on cell proliferation using in vitro experiments to estimate cell doubling times and sizes for established primary Glioblastoma cell lines. A preclinically driven mathematical model parametrization is accomplished by taking into account the experimental measurements. As a control cell line we use the well-studied U87MG cells. Both in vitro and in silico results presented support that the variance between tumor staging can be attributed to the differential proliferative capacity of the different Glioblastoma cells. More specifically, the intratumoral heterogeneity together with the overall proliferation reflected in both the proliferation rate and the mechanical cell contact inhibition can predict the in vitro evolution of different Glioblastoma cell lines growing under the same conditions. Undoubtedly, additional imaging techniques capable of providing spatial information of tumor cell physiology and microenvironment will enhance our understanding regarding Glioblastoma nature and verify and further improve our predictability.
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Affiliation(s)
- M.-E. Oraiopoulou
- Department of Medicine, University of Crete, Heraklion, Greece
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - E. Tzamali
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - G. Tzedakis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
| | - A. Vakis
- Department of Medicine, University of Crete, Heraklion, Greece
- Neurosurgery Clinic, University General Hospital of Heraklion, Heraklion, Greece
| | - J. Papamatheakis
- Gene Expression Laboratory, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece
- Department of Biology, University of Crete, Heraklion, Greece
| | - V. Sakkalis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
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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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39
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Wijeratne PA, Hipwell JH, Hawkes DJ, Stylianopoulos T, Vavourakis V. Multiscale biphasic modelling of peritumoural collagen microstructure: The effect of tumour growth on permeability and fluid flow. PLoS One 2017; 12:e0184511. [PMID: 28902902 PMCID: PMC5597211 DOI: 10.1371/journal.pone.0184511] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 08/27/2017] [Indexed: 11/18/2022] Open
Abstract
We present an in-silico model of avascular poroelastic tumour growth coupled with a multiscale biphasic description of the tumour–host environment. The model is specified to in-vitro data, facilitating biophysically realistic simulations of tumour spheroid growth into a dense collagen hydrogel. We use the model to first confirm that passive mechanical remodelling of collagen fibres at the tumour boundary is driven by solid stress, and not fluid pressure. The model is then used to demonstrate the influence of collagen microstructure on peritumoural permeability and interstitial fluid flow. Our model suggests that at the tumour periphery, remodelling causes the peritumoural stroma to become more permeable in the circumferential than radial direction, and the interstitial fluid velocity is found to be dependent on initial collagen alignment. Finally we show that solid stresses are negatively correlated with peritumoural permeability, and positively correlated with interstitial fluid velocity. These results point to a heterogeneous, microstructure-dependent force environment at the tumour–peritumoural stroma interface.
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Affiliation(s)
- Peter A. Wijeratne
- Department of Computer Science, University College London, London, United Kingdom
- * E-mail:
| | - John H. Hipwell
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - David J. Hawkes
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | - Vasileios Vavourakis
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
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40
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Stamatakos GS, Giatili SG. A Numerical Handling of the Boundary Conditions Imposed by the Skull on an Inhomogeneous Diffusion-Reaction Model of Glioblastoma Invasion Into the Brain: Clinical Validation Aspects. Cancer Inform 2017; 16:1176935116684824. [PMID: 28469383 PMCID: PMC5392020 DOI: 10.1177/1176935116684824] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Accepted: 11/24/2016] [Indexed: 12/22/2022] Open
Abstract
A novel explicit triscale reaction-diffusion numerical model of glioblastoma multiforme tumor growth is presented. The model incorporates the handling of Neumann boundary conditions imposed by the cranium and takes into account both the inhomogeneous nature of human brain and the complexity of the skull geometry. The finite-difference time-domain method is adopted. To demonstrate the workflow of a possible clinical validation procedure, a clinical case/scenario is addressed. A good agreement of the in silico calculated value of the doubling time (ie, the time for tumor volume to double) with the value of the same quantity based on tomographic imaging data has been observed. A theoretical exploration suggests that a rough but still quite informative value of the doubling time may be calculated based on a homogeneous brain model. The model could serve as the main component of a continuous mathematics-based glioblastoma oncosimulator aiming at supporting the clinician in the optimal patient-individualized design of treatment using the patient's multiscale data and experimenting in silico (ie, on the computer).
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Affiliation(s)
- Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografou, Greece
| | - Stavroula G Giatili
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografou, Greece
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41
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Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial. Bull Math Biol 2017; 79:939-974. [DOI: 10.1007/s11538-017-0258-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 02/14/2017] [Indexed: 10/20/2022]
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42
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Priya R, Gomez GA, Budnar S, Acharya BR, Czirok A, Yap AS, Neufeld Z. Bistable front dynamics in a contractile medium: Travelling wave fronts and cortical advection define stable zones of RhoA signaling at epithelial adherens junctions. PLoS Comput Biol 2017; 13:e1005411. [PMID: 28273072 PMCID: PMC5362241 DOI: 10.1371/journal.pcbi.1005411] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 03/22/2017] [Accepted: 02/07/2017] [Indexed: 12/24/2022] Open
Abstract
Mechanical coherence of cell layers is essential for epithelia to function as tissue barriers and to control active tissue dynamics during morphogenesis. RhoA signaling at adherens junctions plays a key role in this process by coupling cadherin-based cell-cell adhesion together with actomyosin contractility. Here we propose and analyze a mathematical model representing core interactions involved in the spatial localization of junctional RhoA signaling. We demonstrate how the interplay between biochemical signaling through positive feedback, combined with diffusion on the cell membrane and mechanical forces generated in the cortex, can determine the spatial distribution of RhoA signaling at cell-cell junctions. This dynamical mechanism relies on the balance between a propagating bistable signal that is opposed by an advective flow generated by an actomyosin stress gradient. Experimental observations on the behavior of the system when contractility is inhibited are in qualitative agreement with the predictions of the model.
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Affiliation(s)
- Rashmi Priya
- Institute for Molecular Bioscience, Division of Cell Biology and Molecular Medicine, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Guillermo A. Gomez
- Institute for Molecular Bioscience, Division of Cell Biology and Molecular Medicine, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Srikanth Budnar
- Institute for Molecular Bioscience, Division of Cell Biology and Molecular Medicine, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Bipul R. Acharya
- Institute for Molecular Bioscience, Division of Cell Biology and Molecular Medicine, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Andras Czirok
- Department of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Alpha S. Yap
- Institute for Molecular Bioscience, Division of Cell Biology and Molecular Medicine, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Zoltan Neufeld
- School of Mathematics and Physics, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia
- * E-mail:
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43
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Gomez H. Quantitative analysis of the proliferative-to-invasive transition of hypoxic glioma cells. Integr Biol (Camb) 2017; 9:257-262. [DOI: 10.1039/c6ib00208k] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Impact of the proliferative-to-invasive transformation of glioma cells on the global growth kinetics of the tumor.
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Affiliation(s)
- Hector Gomez
- School of Mechanical Engineering
- Purdue University
- West Lafayette
- USA
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44
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Alfonso JCL, Köhn-Luque A, Stylianopoulos T, Feuerhake F, Deutsch A, Hatzikirou H. Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights. Sci Rep 2016; 6:37283. [PMID: 27876890 PMCID: PMC5120360 DOI: 10.1038/srep37283] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 10/26/2016] [Indexed: 12/18/2022] Open
Abstract
Gliomas are highly invasive brain tumours characterised by poor prognosis and limited response to therapy. There is an ongoing debate on the therapeutic potential of vaso-modulatory interventions against glioma invasion. Prominent vasculature-targeting therapies involve tumour blood vessel deterioration and normalisation. The former aims at tumour infarction and nutrient deprivation induced by blood vessel occlusion/collapse. In contrast, the therapeutic intention of normalising the abnormal tumour vasculature is to improve the efficacy of conventional treatment modalities. Although these strategies have shown therapeutic potential, it remains unclear why they both often fail to control glioma growth. To shed some light on this issue, we propose a mathematical model based on the migration/proliferation dichotomy of glioma cells in order to investigate why vaso-modulatory interventions have shown limited success in terms of tumour clearance. We found the existence of a critical cell proliferation/diffusion ratio that separates glioma responses to vaso-modulatory interventions into two distinct regimes. While for tumours, belonging to one regime, vascular modulations reduce the front speed and increase the infiltration width, for those in the other regime, the invasion speed increases and infiltration width decreases. We discuss how these in silico findings can be used to guide individualised vaso-modulatory approaches to improve treatment success rates.
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Affiliation(s)
- J C L Alfonso
- Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, Braunschweig, Germany.,Center for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - A Köhn-Luque
- Department of Biostatistics, Faculty of Medicine, University of Oslo, Norway.,BigInsight, Centre for Research-based Innovation (SFI), Oslo, Norway
| | - T Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - F Feuerhake
- Institute of Pathology, Medical School of Hannover, Germany.,Institute of Neuropathology, University Clinic Freiburg, Germany
| | - A Deutsch
- Center for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - H Hatzikirou
- Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, Braunschweig, Germany
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45
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Kassab GS, An G, Sander EA, Miga MI, Guccione JM, Ji S, Vodovotz Y. Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective. Ann Biomed Eng 2016; 44:2611-25. [PMID: 27015816 DOI: 10.1007/s10439-016-1596-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Accepted: 03/18/2016] [Indexed: 12/18/2022]
Abstract
In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.
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Affiliation(s)
- Ghassan S Kassab
- California Medical Innovations Institute, San Diego, CA, 92121, USA
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, IL, 60637, USA
| | - Edward A Sander
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242, USA
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Julius M Guccione
- Department of Surgery, University of California, San Francisco, CA, 94143, USA
| | - Songbai Ji
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.,Department of Surgery and of Orthopaedic Surgery, Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, W944 Starzl Biomedical Sciences Tower, 200 Lothrop St., Pittsburgh, PA, 15213, USA. .,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA.
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Rahmim A, Schmidtlein CR, Jackson A, Sheikhbahaei S, Marcus C, Ashrafinia S, Soltani M, Subramaniam RM. A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction. Phys Med Biol 2015; 61:227-42. [PMID: 26639024 DOI: 10.1088/0031-9155/61/1/227] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Oncologic PET images provide valuable information that can enable enhanced prognosis of disease. Nonetheless, such information is simplified significantly in routine clinical assessment to meet workflow constraints. Examples of typical FDG PET metrics include: (i) SUVmax, (2) total lesion glycolysis (TLG), and (3) metabolic tumor volume (MTV). We have derived and implemented a novel metric for tumor quantification, inspired in essence by a model of generalized equivalent uniform dose as used in radiation therapy. The proposed metric, denoted generalized effective total uptake (gETU), is attractive as it encompasses the abovementioned commonly invoked metrics, and generalizes them, for both homogeneous and heterogeneous tumors, using a single parameter a. We evaluated this new metric for improved overall survival (OS) prediction on two different baseline FDG PET/CT datasets: (a) 113 patients with squamous cell cancer of the oropharynx, and (b) 72 patients with locally advanced pancreatic adenocarcinoma. Kaplan-Meier survival analysis was performed, where the subjects were subdivided into two groups using the median threshold, from which the hazard ratios (HR) were computed in Cox proportional hazards regression. For the oropharyngeal cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak produced HR values of 1.86, 3.02, 1.34, 1.36 and 1.62, while the proposed gETU metric for a = 0.25 (greater emphasis on volume information) enabled significantly enhanced OS prediction with HR = 3.94. For the pancreatic cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak resulted in HR values of 1.05, 1.25, 1.42, 1.45 and 1.52, while gETU at a = 3.2 (greater emphasis on SUV information) arrived at an improved HR value of 1.61. Overall, the proposed methodology allows placement of differing degrees of emphasis on tumor volume versus uptake for different types of tumors to enable enhanced clinical outcome prediction.
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Affiliation(s)
- Arman Rahmim
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21287, USA. Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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Roniotis A, Oraiopoulou ME, Tzamali E, Kontopodis E, Van Cauter S, Sakkalis V, Marias K. A Proposed Paradigm Shift in Initializing Cancer Predictive Models with DCE-MRI Based PK Parameters: A Feasibility Study. Cancer Inform 2015; 14:7-18. [PMID: 26085787 PMCID: PMC4463799 DOI: 10.4137/cin.s19339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 03/02/2015] [Accepted: 03/06/2015] [Indexed: 01/12/2023] Open
Abstract
Glioblastoma multiforme is the most aggressive type of glioma and the most common malignant primary intra-axial brain tumor. In an effort to predict the evolution of the disease and optimize therapeutical decisions, several models have been proposed for simulating the growth pattern of glioma. One of the latest models incorporates cell proliferation and invasion, angiogenic net rates, oxygen consumption, and vasculature. These factors, particularly oxygenation levels, are considered fundamental factors of tumor heterogeneity and compartmentalization. This paper focuses on the initialization of the cancer cell populations and vasculature based on imaging examinations of the patient and presents a feasibility study on vasculature prediction over time. To this end, pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging using Toft’s model are used in order to feed the model. Ktrans is used as a metric of the density of endothelial cells (vasculature); at the same time, it also helps to discriminate distinct image areas of interest, under a set of assumptions. Feasibility results of applying the model to a real clinical case are presented, including a study on the effect of certain parameters on the pattern of the simulated tumor.
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Affiliation(s)
- Alexandros Roniotis
- Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece
| | - Mariam-Eleni Oraiopoulou
- Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece. ; Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Eleftheria Tzamali
- Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece
| | - Eleftherios Kontopodis
- Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece
| | - Sofie Van Cauter
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Vangelis Sakkalis
- Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece
| | - Kostas Marias
- Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece
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Yankeelov TE, Quaranta V, Evans KJ, Rericha EC. Toward a science of tumor forecasting for clinical oncology. Cancer Res 2015; 75:918-23. [PMID: 25592148 DOI: 10.1158/0008-5472.can-14-2233] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.
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Affiliation(s)
- Thomas E Yankeelov
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee. Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee. Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee. Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee.
| | - Vito Quaranta
- Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee
| | - Katherine J Evans
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Nashville, Tennessee
| | - Erin C Rericha
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
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Benzekry S, Lamont C, Beheshti A, Tracz A, Ebos JML, Hlatky L, Hahnfeldt P. Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol 2014; 10:e1003800. [PMID: 25167199 PMCID: PMC4148196 DOI: 10.1371/journal.pcbi.1003800] [Citation(s) in RCA: 260] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Accepted: 07/08/2014] [Indexed: 01/03/2023] Open
Abstract
Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic.
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Affiliation(s)
- Sébastien Benzekry
- Inria Bordeaux Sud-Ouest, Institut de Mathématiques de Bordeaux, Bordeaux, France
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Clare Lamont
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Afshin Beheshti
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Amanda Tracz
- Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York, United States of America
| | - John M. L. Ebos
- Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York, United States of America
| | - Lynn Hlatky
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Philip Hahnfeldt
- Center of Cancer Systems Biology, GRI, Tufts University School of Medicine, Boston, Massachusetts, United States of America
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Chen Y, Lowengrub JS. Tumor growth in complex, evolving microenvironmental geometries: a diffuse domain approach. J Theor Biol 2014; 361:14-30. [PMID: 25014472 DOI: 10.1016/j.jtbi.2014.06.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2013] [Revised: 06/10/2014] [Accepted: 06/20/2014] [Indexed: 12/21/2022]
Abstract
We develop a mathematical model of tumor growth in complex, dynamic microenvironments with active, deformable membranes. Using a diffuse domain approach, the complex domain is captured implicitly using an auxiliary function and the governing equations are appropriately modified, extended and solved in a larger, regular domain. The diffuse domain method enables us to develop an efficient numerical implementation that does not depend on the space dimension or the microenvironmental geometry. We model homotypic cell-cell adhesion and heterotypic cell-basement membrane (BM) adhesion with the latter being implemented via a membrane energy that models cell-BM interactions. We incorporate simple models of elastic forces and the degradation of the BM and ECM by tumor-secreted matrix degrading enzymes. We investigate tumor progression and BM response as a function of cell-BM adhesion and the stiffness of the BM. We find tumor sizes tend to be positively correlated with cell-BM adhesion since increasing cell-BM adhesion results in thinner, more elongated tumors. Prior to invasion of the tumor into the stroma, we find a negative correlation between tumor size and BM stiffness as the elastic restoring forces tend to inhibit tumor growth. In order to model tumor invasion of the stroma, we find it necessary to downregulate cell-BM adhesiveness, which is consistent with experimental observations. A stiff BM promotes invasiveness because at early stages the opening in the BM created by MDE degradation from tumor cells tends to be narrower when the BM is stiffer. This requires invading cells to squeeze through the narrow opening and thus promotes fragmentation that then leads to enhanced growth and invasion. In three dimensions, the opening in the BM was found to increase in size even when the BM is stiff because of pressure induced by growing tumor clusters. A larger opening in the BM can increase the potential for further invasiveness by increasing the possibility that additional tumor cells could invade the stroma.
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Affiliation(s)
- Ying Chen
- Department of Mathematics, University of California, Irvine, USA.
| | - John S Lowengrub
- Department of Mathematics, Department of Biomedical Engineering, Center for Complex Biological Systems, University of California, Irvine, USA.
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