1
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Metzcar J, Jutzeler CR, Macklin P, Köhn-Luque A, Brüningk SC. A review of mechanistic learning in mathematical oncology. Front Immunol 2024; 15:1363144. [PMID: 38533513 PMCID: PMC10963621 DOI: 10.3389/fimmu.2024.1363144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/20/2024] [Indexed: 03/28/2024] Open
Abstract
Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework.
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Affiliation(s)
- John Metzcar
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
- Informatics, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Catherine R. Jutzeler
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Paul Macklin
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Sarah C. Brüningk
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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2
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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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3
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Liu Y, Suh K, Maini PK, Cohen DJ, Baker RE. Parameter identifiability and model selection for partial differential equation models of cell invasion. J R Soc Interface 2024; 21:20230607. [PMID: 38442862 PMCID: PMC10914513 DOI: 10.1098/rsif.2023.0607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/31/2024] [Indexed: 03/07/2024] Open
Abstract
When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide ranges of unseen scenarios, as well as for understanding the underlying mechanisms. In this work, we use a profile-likelihood approach to investigate parameter identifiability for four extensions of the Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and that they require more data to be practically identifiable. As a result, we suggest that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection.
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Affiliation(s)
- Yue Liu
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Kevin Suh
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA
| | | | - Daniel J. Cohen
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA
| | - Ruth E. Baker
- Mathematical Institute, University of Oxford, Oxford, UK
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4
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Urcuyo JC, Curtin L, Langworthy JM, De Leon G, Anderies B, Singleton KW, Hawkins-Daarud A, Jackson PR, Bond KM, Ranjbar S, Lassiter-Morris Y, Clark-Swanson KR, Paulson LE, Sereduk C, Mrugala MM, Porter AB, Baxter L, Salomao M, Donev K, Hudson M, Meyer J, Zeeshan Q, Sattur M, Patra DP, Jones BA, Rahme RJ, Neal MT, Patel N, Kouloumberis P, Turkmani AH, Lyons M, Krishna C, Zimmerman RS, Bendok BR, Tran NL, Hu LS, Swanson KR. Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol. PLoS One 2023; 18:e0287767. [PMID: 38117803 PMCID: PMC10732423 DOI: 10.1371/journal.pone.0287767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 06/13/2023] [Indexed: 12/22/2023] Open
Abstract
Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.
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Affiliation(s)
- Javier C Urcuyo
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Lee Curtin
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jazlynn M. Langworthy
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Gustavo De Leon
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Barrett Anderies
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kyle W. Singleton
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pamela R. Jackson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kamila M. Bond
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Sara Ranjbar
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Yvette Lassiter-Morris
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kamala R. Clark-Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Lisa E. Paulson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Chris Sereduk
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Maciej M. Mrugala
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Alyx B. Porter
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Leslie Baxter
- Department of Neurophysiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Marcela Salomao
- Department of Pathology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kliment Donev
- Department of Pathology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Miles Hudson
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jenna Meyer
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Qazi Zeeshan
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Mithun Sattur
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Devi P. Patra
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Breck A. Jones
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Rudy J. Rahme
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Matthew T. Neal
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Naresh Patel
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pelagia Kouloumberis
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Ali H. Turkmani
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Mark Lyons
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Chandan Krishna
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Richard S. Zimmerman
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Bernard R. Bendok
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Nhan L. Tran
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kristin R. Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
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5
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Bond KM, Curtin L, Ranjbar S, Afshari AE, Hu LS, Rubin JB, Swanson KR. An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients. Front Oncol 2023; 13:1185738. [PMID: 37849813 PMCID: PMC10578440 DOI: 10.3389/fonc.2023.1185738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/21/2023] [Indexed: 10/19/2023] Open
Abstract
Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.
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Affiliation(s)
- Kamila M. Bond
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
- Hospital of University of Pennsylvania, Department of Neurosurgery, Philadelphia, PA, United States
| | - Lee Curtin
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Sara Ranjbar
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Ariana E. Afshari
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
| | - Leland S. Hu
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Joshua B. Rubin
- Departments of Neuroscience and Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
| | - Kristin R. Swanson
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States
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6
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Strobl MAR, Gallaher J, Robertson-Tessi M, West J, Anderson ARA. Treatment of evolving cancers will require dynamic decision support. Ann Oncol 2023; 34:867-884. [PMID: 37777307 PMCID: PMC10688269 DOI: 10.1016/j.annonc.2023.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 10/02/2023] Open
Abstract
Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.
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Affiliation(s)
- M A R Strobl
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa; Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, USA
| | - J Gallaher
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - M Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - J West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - A R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa.
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7
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Jørgensen ACS, Hill CS, Sturrock M, Tang W, Karamched SR, Gorup D, Lythgoe MF, Parrinello S, Marguerat S, Shahrezaei V. Data-driven spatio-temporal modelling of glioblastoma. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221444. [PMID: 36968241 PMCID: PMC10031411 DOI: 10.1098/rsos.221444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.
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Affiliation(s)
| | - Ciaran Scott Hill
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin D02 YN77, Ireland
| | - Wenhao Tang
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
| | - Saketh R. Karamched
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Dunja Gorup
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Mark F. Lythgoe
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Simona Parrinello
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Samuel Marguerat
- Genomics Translational Technology Platform, UCL Cancer Institute, University College London, London WC1E 6DD, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
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8
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Wang L, Hawkins-Daarud A, Swanson KR, Hu LS, Li J. Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING : A PUBLICATION OF THE IEEE ROBOTICS AND AUTOMATION SOCIETY 2022; 19:2203-2215. [PMID: 37700873 PMCID: PMC10497221 DOI: 10.1109/tase.2021.3076117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
The automated capability of generating spatial prediction for a variable of interest is desirable in various science and engineering domains. Take Precision Medicine of cancer as an example, in which the goal is to match patients with treatments based on molecular markers identified in each patient's tumor. A substantial challenge, however, is that the molecular markers can vary significantly at different spatial locations of a tumor. If this spatial distribution could be predicted, the precision of cancer treatment could be greatly improved by adapting treatment to the spatial molecular heterogeneity. This is a challenging task because no technology is available to measure the molecular markers at each spatial location within a tumor. Biopsy samples provide direct measurement, but they are scarce/local. Imaging, such as MRI, is global, but it only provides proxy/indirect measurement. Also available are mechanistic models or domain knowledge, which are often approximate or incomplete. This paper proposes a novel machine learning framework to fuse the three sources of data/information to generate spatial prediction, namely the knowledge-infused global-local data fusion (KGL) model. A novel mathematical formulation is proposed and solved with theoretical study. We present a real-data application of predicting the spatial distribution of Tumor Cell Density (TCD)-an important molecular marker for brain cancer. A total of 82 biopsy samples were acquired from 18 patients with glioblastoma, together with 6 MRI contrast images from each patient and biological knowledge encoded by a PDE simulator-based mechanistic model called Proliferation-Invasion (PI). KGL achieved the highest prediction accuracy and minimum prediction uncertainty compared with a variety of competing methods. The result has important implications for providing individualized, spatially-optimized treatment for each patient.
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Affiliation(s)
- Lujia Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Andrea Hawkins-Daarud
- Mathematical Neuro-Oncology Lab in the Department of Neurosurgery at Mayo Clinic Arizona, Phoenix, AZ 85054 USA
| | - Kristin R Swanson
- Mathematical Neuro-Oncology Lab in the Department of Neurosurgery at Mayo Clinic Arizona, Phoenix, AZ 85054 USA
| | - Leland S Hu
- Department of Radiology at Mayo Clinic Arizona, Phoenix, AZ 85054 USA
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
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9
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Han L, He C, Dinh H, Fricks J, Kuang Y. Learning Biological Dynamics From Spatio-Temporal Data by Gaussian Processes. Bull Math Biol 2022; 84:69. [DOI: 10.1007/s11538-022-01022-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 01/18/2022] [Indexed: 11/02/2022]
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10
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Ezhov I, Mot T, Shit S, Lipkova J, Paetzold JC, Kofler F, Pellegrini C, Kollovieh M, Navarro F, Li H, Metz M, Wiestler B, Menze B. Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1269-1278. [PMID: 34928790 DOI: 10.1109/tmi.2021.3136582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of tumor modeling often imply solving an inverse problem, requiring up to tens of thousands of forward model evaluations when used for a Bayesian model personalization via sampling. This results in a total inference time prohibitively expensive for clinical translation. While recent data-driven approaches become capable of emulating physics simulation, they tend to fail in generalizing over the variability of the boundary conditions imposed by the patient-specific anatomy. In this paper, we propose a learnable surrogate for simulating tumor growth which maps the biophysical model parameters directly to simulation outputs, i.e. the local tumor cell densities, whilst respecting patient geometry. We test the neural solver in a Bayesian model personalization task for a cohort of glioma patients. Bayesian inference using the proposed surrogate yields estimates analogous to those obtained by solving the forward model with a regular numerical solver. The near real-time computation cost renders the proposed method suitable for clinical settings. The code is available at https://github.com/IvanEz/tumor-surrogate.
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11
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Lipková J, Menze B, Wiestler B, Koumoutsakos P, Lowengrub JS. Modelling glioma progression, mass effect and intracranial pressure in patient anatomy. J R Soc Interface 2022; 19:20210922. [PMID: 35317645 PMCID: PMC8941421 DOI: 10.1098/rsif.2021.0922] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/21/2022] [Indexed: 02/06/2023] Open
Abstract
Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.
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Affiliation(s)
- Jana Lipková
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zürich, Zürich, Switzerland
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Petros Koumoutsakos
- Computational Science and Engineering Lab, ETH Zürich, Zürich, Switzerland
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - John S. Lowengrub
- Department of Mathematics, University of California, Irvine, CA, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, Chao Family Comprehensive Cancer Center, University of California, Irvine, CA, USA
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12
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Chen J, Lee H, Schmitt P, Choy CJ, Miller DM, Williams BJ, Bearer EL, Frieboes HB. Bioengineered Models to Study Microenvironmental Regulation of Glioblastoma Metabolism. J Neuropathol Exp Neurol 2021; 80:1012–1023. [PMID: 34524448 DOI: 10.1093/jnen/nlab092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Despite extensive research and aggressive therapies, glioblastoma (GBM) remains a central nervous system malignancy with poor prognosis. The varied histopathology of GBM suggests a landscape of differing microenvironments and clonal expansions, which may influence metabolism, driving tumor progression. Indeed, GBM metabolic plasticity in response to differing nutrient supply within these microenvironments has emerged as a key driver of aggressiveness. Additionally, emergent biophysical and biochemical interactions in the tumor microenvironment (TME) are offering new perspectives on GBM metabolism. Perivascular and hypoxic niches exert crucial roles in tumor maintenance and progression, facilitating metabolic relationships between stromal and tumor cells. Alterations in extracellular matrix and its biophysical characteristics, such as rigidity and topography, regulate GBM metabolism through mechanotransductive mechanisms. This review highlights insights gained from deployment of bioengineering models, including engineered cell culture and mathematical models, to study the microenvironmental regulation of GBM metabolism. Bioengineered approaches building upon histopathology measurements may uncover potential therapeutic strategies that target both TME-dependent mechanotransductive and biomolecular drivers of metabolism to tackle this challenging disease. Longer term, a concerted effort integrating in vitro and in silico models predictive of patient therapy response may offer a powerful advance toward tailoring of treatment to patient-specific GBM characteristics.
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Affiliation(s)
- Joseph Chen
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Hyunchul Lee
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Philipp Schmitt
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Caleb J Choy
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Donald M Miller
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Brian J Williams
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Elaine L Bearer
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
| | - Hermann B Frieboes
- From the Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA (JC, CJC, HBF); Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, USA (JC, DMM, HBF); Department of Neurological Surgery, University of Louisville, Louisville, Kentucky, USA (HL, BJW); Department of Medicine, University of Louisville, Louisville, Kentucky, USA (PS, DMM); Department of Radiation Oncology, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, USA (DMM, BJW, HBF); Center for Predictive Medicine, University of Louisville, Louisville, Kentucky, USA (HBF); Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA (ELB)
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A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type. J Digit Imaging 2021; 33:439-446. [PMID: 31654174 DOI: 10.1007/s10278-019-00282-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The explosion of medical imaging data along with the advent of big data analytics has launched an exciting era for clinical research. One factor affecting the ability to aggregate large medical image collections for research is the lack of infrastructure for automated data annotation. Among all imaging modalities, annotation of magnetic resonance (MR) images is particularly challenging due to the non-standard labeling of MR image types. In this work, we aimed to train a deep neural network to annotate MR image sequence type for scans of brain tumor patients. We focused on the four most common MR sequence types within neuroimaging: T1-weighted (T1W), T1-weighted post-gadolinium contrast (T1Gd), T2-weighted (T2W), and T2-weighted fluid-attenuated inversion recovery (FLAIR). Our repository contains images acquired using a variety of pulse sequences, sequence parameters, field strengths, and scanner manufacturers. Image selection was agnostic to patient demographics, diagnosis, and the presence of tumor in the imaging field of view. We used a total of 14,400 two-dimensional images, each visualizing a different part of the brain. Data was split into train, validation, and test sets (9600, 2400, and 2400 images, respectively) and sets consisted of equal-sized groups of image types. Overall, the model reached an accuracy of 99% on the test set. Our results showed excellent performance of deep learning techniques in predicting sequence types for brain tumor MR images. We conclude deep learning models can serve as tools to support clinical research and facilitate efficient database management.
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Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations. PLoS Comput Biol 2021; 17:e1009206. [PMID: 34310608 PMCID: PMC8341701 DOI: 10.1371/journal.pcbi.1009206] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 08/05/2021] [Accepted: 06/22/2021] [Indexed: 11/19/2022] Open
Abstract
Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell-level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, models simulating individual cells’ behavior are needed. Here, we provide a link between the average data values recorded for radiologic images and the cellular and vascular architecture of the corresponding tissues. Using hybrid agent-based modeling, we generate a set of tissue morphologies capable of reproducing oxygenation levels observed in radiologic images. We then use these in silico tissues to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies show that intravascular changes in oxygen supply reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, larger-magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in a biologically feasible manner. Additionally, we develop a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications, this approach can be used to generate a set of tissues comparable with radiology images and to simulate tumor responses to various anti-cancer treatments at the tissue-scale level. Low levels of oxygen, called hypoxia, are observable in many solid tumors. They are associated with more aggressive malignant cells that are resistant to chemo-, radio-, and immunotherapies. Recently developed imaging techniques provide a way to measure the magnitude of frequent short-term oxygen fluctuations, but they operate on a macro-scale voxel level. To examine the possible causes of rapid oxygen fluctuations at the cell level, we developed a hybrid agent-based mathematical model. We tested two different mechanisms that may be responsible for these cyclic effects on tissue oxygenation: temporal variations in vascular influx of oxygen and modulations in cellular oxygen absorption. Additionally, we developed a procedure to identify plausible tissue morphologies from data collected from radiological images. This can provide a bridge between the micro-scale simulations with individual cells and the longitudinal medical images containing average values. In future applications, this approach can be used to generate a set of tissues compatible with radiology images and to simulate tumor responses to various anticancer treatments at the cell-scale level.
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Scott M, Żychaluk K, Bearon RN. A mathematical framework for modelling 3D cell motility: applications to glioblastoma cell migration. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2021; 38:333-354. [PMID: 34189581 DOI: 10.1093/imammb/dqab009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 11/14/2022]
Abstract
The collection of 3D cell tracking data from live images of micro-tissues is a recent innovation made possible due to advances in imaging techniques. As such there is increased interest in studying cell motility in 3D in vitro model systems but a lack of rigorous methodology for analysing the resulting data sets. One such instance of the use of these in vitro models is in the study of cancerous tumours. Growing multicellular tumour spheroids in vitro allows for modelling of the tumour microenvironment and the study of tumour cell behaviours, such as migration, which improves understanding of these cells and in turn could potentially improve cancer treatments. In this paper, we present a workflow for the rigorous analysis of 3D cell tracking data, based on the persistent random walk model, but adaptable to other biologically informed mathematical models. We use statistical measures to assess the fit of the model to the motility data and to estimate model parameters and provide confidence intervals for those parameters, to allow for parametrization of the model taking correlation in the data into account. We use in silico simulations to validate the workflow in 3D before testing our method on cell tracking data taken from in vitro experiments on glioblastoma tumour cells, a brain cancer with a very poor prognosis. The presented approach is intended to be accessible to both modellers and experimentalists alike in that it provides tools for uncovering features of the data set that may suggest amendments to future experiments or modelling attempts.
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Affiliation(s)
- M Scott
- Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
| | - K Żychaluk
- Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
| | - R N Bearon
- Department of Mathematical Sciences, University of Liverpool, Liverpool L69 7ZL, UK
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16
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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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17
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Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2021; 12658:157-167. [PMID: 34514469 DOI: 10.1007/978-3-030-72084-1_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Glioblastoma ( GBM ) is arguably the most aggressive, infiltrative, and heterogeneous type of adult brain tumor. Biophysical modeling of GBM growth has contributed to more informed clinical decision-making. However, deploying a biophysical model to a clinical environment is challenging since underlying computations are quite expensive and can take several hours using existing technologies. Here we present a scheme to accelerate the computation. In particular, we present a deep learning ( DL )-based logistic regression model to estimate the GBM's biophysical growth in seconds. This growth is defined by three tumor-specific parameters: 1) a diffusion coefficient in white matter ( Dw ), which prescribes the rate of infiltration of tumor cells in white matter, 2) a mass-effect parameter ( Mp ), which defines the average tumor expansion, and 3) the estimated time ( T ) in number of days that the tumor has been growing. Preoperative structural multi-parametric MRI ( mpMRI ) scans from n = 135 subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal FLAIR signal envelope for training one DL model for each of the tumor-specific growth parameters. We train and validate the DL-based predictions against parameters derived from biophysical inversion models. The average Pearson correlation coefficients between our DL-based estimations and the biophysical parameters are 0.85 for Dw, 0.90 for Mp, and 0.94 for T, respectively. This study unlocks the power of tumor-specific parameters from biophysical tumor growth estimation. It paves the way towards their clinical translation and opens the door for leveraging advanced radiomic descriptors in future studies by means of a significantly faster parameter reconstruction compared to biophysical growth modeling approaches.
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Kulesa PM, Kasemeier-Kulesa JC, Morrison JA, McLennan R, McKinney MC, Bailey C. Modelling Cell Invasion: A Review of What JD Murray and the Embryo Can Teach Us. Bull Math Biol 2021; 83:26. [PMID: 33594536 DOI: 10.1007/s11538-021-00859-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/08/2021] [Indexed: 12/11/2022]
Abstract
Cell invasion and cell plasticity are critical to human development but are also striking features of cancer metastasis. By distributing a multipotent cell type from a place of birth to distal locations, the vertebrate embryo builds organs. In comparison, metastatic tumor cells often acquire a de-differentiated phenotype and migrate away from a primary site to inhabit new microenvironments, disrupting normal organ function. Countless observations of both embryonic cell migration and tumor metastasis have demonstrated complex cell signaling and interactive behaviors that have long confounded scientist and clinician alike. James D. Murray realized the important role of mathematics in biology and developed a unique strategy to address complex biological questions such as these. His work offers a practical template for constructing clear, logical, direct and verifiable models that help to explain complex cell behaviors and direct new experiments. His pioneering work at the interface of development and cancer made significant contributions to glioblastoma cancer and embryonic pattern formation using often simple models with tremendous predictive potential. Here, we provide a brief overview of advances in cell invasion and cell plasticity using the embryonic neural crest and its ancestral relationship to aggressive cancers that put into current context the timeless aspects of his work.
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Affiliation(s)
- Paul M Kulesa
- Stowers Institute for Medical Research, Kansas City, MO, 64110, USA. .,Department of Anatomy and Cell Biology, School of Medicine, University of Kansas, Kansas City, KS, 66160, USA.
| | | | - Jason A Morrison
- Stowers Institute for Medical Research, Kansas City, MO, 64110, USA
| | - Rebecca McLennan
- Stowers Institute for Medical Research, Kansas City, MO, 64110, USA
| | | | - Caleb Bailey
- Department of Biology, Brigham Young University-Idaho, Rexburg, ID, 83460, USA
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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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Scheufele K, Subramanian S, Biros G. Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:193-204. [PMID: 32931431 PMCID: PMC8565678 DOI: 10.1109/tmi.2020.3024264] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the original, localized site(s) of tumor initiation. Our method is based on a sparse reconstruction algorithm for the tumor initial location (TIL). This problem is particularly challenging due to nonlinearity, ill-posedeness, and ill conditioning. We propose a coarse-to-fine multi-resolution continuation scheme with parameter decomposition to stabilize the inversion. We demonstrate robustness and practicality of our method by applying the proposed method to clinical data of 206 GBM patients. We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.
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21
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Kumbale CM, Davis JD, Voit EO. Models for Personalized Medicine. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11349-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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Ruiz-Arrebola S, Tornero-López AM, Guirado D, Villalobos M, Lallena AM. An on-lattice agent-based Monte Carlo model simulating the growth kinetics of multicellular tumor spheroids. Phys Med 2020; 77:194-203. [PMID: 32882615 DOI: 10.1016/j.ejmp.2020.07.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/19/2020] [Indexed: 11/26/2022] Open
Abstract
PURPOSE To develop an on-lattice agent-based model describing the growth of multicellular tumor spheroids using simple Monte Carlo tools. METHODS Cells are situated on the vertices of a cubic grid. Different cell states (proliferative, hypoxic or dead) and cell evolution rules, driven by 10 parameters, and the effects of the culture medium are included. About twenty spheroids of MCF-7 human breast cancer were cultivated and the experimental data were used for tuning the model parameters. RESULTS Simulated spheroids showed adequate sizes of the necrotic nuclei and of the hypoxic and proliferative cell phases as a function of the growth time, mimicking the overall characteristics of the experimental spheroids. The relation between the radii of the necrotic nucleus and the whole spheroid obtained in the simulations was similar to the experimental one and the number of cells, as a function of the spheroid volume, was well reproduced. The statistical variability of the Monte Carlo model described the whole volume range observed for the experimental spheroids. Assuming that the model parameters vary within Gaussian distributions it was obtained a sample of spheroids that reproduced much better the experimental findings. CONCLUSIONS The model developed allows describing the growth of in vitro multicellular spheroids and the experimental variability can be well reproduced. Its flexibility permits to vary both the agents involved and the rules that govern the spheroid growth. More general situations, such as, e. g., tumor vascularization, radiotherapy effects on solid tumors, or the validity of the tumor growth mathematical models can be studied.
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Affiliation(s)
- S Ruiz-Arrebola
- Servicio de Oncología Radioterápica, Hospital Universitario Marqués de Valdecilla, E-39008 Santander, Spain
| | - A M Tornero-López
- Servicio de Radiofísica y Protección Radiológica, Hospital Universitario Dr. Negrín, E-35010 Gran Canaria, Spain
| | - D Guirado
- Unidad de Radiofísica, Hospital Universitario San Cecilio, E-18016 Granada, Spain; Instituto de Investigación Biosanitaria (ibs.GRANADA), Complejo Hospitalario Universitario de Granada/Universidad de Granada, E-18016 Granada, Spain
| | - M Villalobos
- Instituto de Investigación Biosanitaria (ibs.GRANADA), Complejo Hospitalario Universitario de Granada/Universidad de Granada, E-18016 Granada, Spain; Departamento de Radiología y Medicina Física, Universidad de Granada, E-18071 Granada, Spain; Instituto de Biopatología y Medicina Regenerativa (IBIMER), Universidad de Granada, E-18071 Granada, Spain
| | - A M Lallena
- Departamento de Física Atómica, Molecular y Nuclear, Universidad de Granada, E-18071 Granada, Spain; Instituto de Investigación Biosanitaria (ibs.GRANADA), Complejo Hospitalario Universitario de Granada/Universidad de Granada, E-18016 Granada, Spain.
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Morris B, Curtin L, Hawkins-Daarud A, Hubbard ME, Rahman R, Smith SJ, Auer D, Tran NL, Hu LS, Eschbacher JM, Smith KA, Stokes A, Swanson KR, Owen MR. Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:4905-4941. [PMID: 33120534 PMCID: PMC8382158 DOI: 10.3934/mbe.2020267] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another in a competitive or cooperative manner; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.
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Affiliation(s)
- Bethan Morris
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Lee Curtin
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, 85054, USA
| | | | - Matthew E. Hubbard
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Ruman Rahman
- School of Medicine and Health Sciences, University of Nottingham, Nottingham, NG7 2UH, UK
| | - Stuart J. Smith
- School of Medicine and Health Sciences, University of Nottingham, Nottingham, NG7 2UH, UK
| | - Dorothee Auer
- School of Medicine and Health Sciences, University of Nottingham, Nottingham, NG7 2UH, UK
| | - Nhan L. Tran
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, 85054, USA
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona 85054, USA
| | - Leland S. Hu
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, 85054, USA
- Department of Radiology, Mayo Clinic, Phoenix, Arizona 85054, USA
| | - Jennifer M. Eschbacher
- Department of Pathology, Barrow Neurological Institute - St. Joseph’s Hospital and Medical Center, Phoenix, Arizona 85013, USA
| | - Kris A. Smith
- Department of Neurosurgery, Barrow Neurological Institute - St. Joseph’s Hospital and Medical Center, Phoenix, Arizona 85013, USA
| | - Ashley Stokes
- Department of Imaging Research, Barrow Neurological Institute - St. Joseph’s Hospital and Medical Center, Phoenix, Arizona 85013, USA
| | - Kristin R. Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, 85054, USA
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona 85054, USA
| | - Markus R. Owen
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
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24
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Glazar DJ, Grass GD, Arrington JA, Forsyth PA, Raghunand N, Yu HHM, Sahebjam S, Enderling H. Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma. J Clin Med 2020; 9:E2019. [PMID: 32605050 PMCID: PMC7409184 DOI: 10.3390/jcm9072019] [Citation(s) in RCA: 12] [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: 06/11/2020] [Revised: 06/18/2020] [Accepted: 06/20/2020] [Indexed: 11/16/2022] Open
Abstract
Recurrent high-grade glioma (HGG) remains incurable with inevitable evolution of resistance and high inter-patient heterogeneity in time to progression (TTP). Here, we evaluate if early tumor volume response dynamics can calibrate a mathematical model to predict patient-specific resistance to develop opportunities for treatment adaptation for patients with a high risk of progression. A total of 95 T1-weighted contrast-enhanced (T1post) MRIs from 14 patients treated in a phase I clinical trial with hypo-fractionated stereotactic radiation (HFSRT; 6 Gy × 5) plus pembrolizumab (100 or 200 mg, every 3 weeks) and bevacizumab (10 mg/kg, every 2 weeks; NCT02313272) were delineated to derive longitudinal tumor volumes. We developed, calibrated, and validated a mathematical model that simulates and forecasts tumor volume dynamics with rate of resistance evolution as the single patient-specific parameter. Model prediction performance is evaluated based on how early progression is predicted and the number of false-negative predictions. The model with one patient-specific parameter describing the rate of evolution of resistance to therapy fits untrained data ( R 2 = 0.70 ). In a leave-one-out study, for the nine patients that had T1post tumor volumes ≥1 cm3, the model was able to predict progression on average two imaging cycles early, with a median of 9.3 (range: 3-39.3) weeks early (median progression-free survival was 27.4 weeks). Our results demonstrate that early tumor volume dynamics measured on T1post MRI has the potential to predict progression following the protocol therapy in select patients with recurrent HGG. Future work will include testing on an independent patient dataset and evaluation of the developed framework on T2/FLAIR-derived data.
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Affiliation(s)
- Daniel J. Glazar
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - G. Daniel Grass
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (G.D.G.); (H.-H.M.Y.)
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA; (J.A.A.); (P.A.F.); (N.R.)
| | - John A. Arrington
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA; (J.A.A.); (P.A.F.); (N.R.)
- Department of Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
- Department of Orthopaedics & Sports Medicine, University of South Florida, Tampa, FL 33612, USA
- Department of Radiology, University of South Florida, Tampa, FL 33612, USA
| | - Peter A. Forsyth
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA; (J.A.A.); (P.A.F.); (N.R.)
- Department of Neuro-Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Natarajan Raghunand
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA; (J.A.A.); (P.A.F.); (N.R.)
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Hsiang-Hsuan Michael Yu
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (G.D.G.); (H.-H.M.Y.)
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA; (J.A.A.); (P.A.F.); (N.R.)
| | - Solmaz Sahebjam
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA; (J.A.A.); (P.A.F.); (N.R.)
- Department of Neuro-Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (G.D.G.); (H.-H.M.Y.)
- Department of Oncologic Sciences, University of South Florida, Tampa, FL 33612, USA; (J.A.A.); (P.A.F.); (N.R.)
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25
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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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26
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Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G. Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology. Annu Rev Biomed Eng 2020; 22:309-341. [PMID: 32501772 PMCID: PMC7520881 DOI: 10.1146/annurev-bioeng-062117-121105] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.
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Affiliation(s)
- Andreas Mang
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Spyridon Bakas
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Shashank Subramanian
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA); Department of Radiology; and Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ,
| | - George Biros
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
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27
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Browning AP, Jin W, Plank MJ, Simpson MJ. Identifying density-dependent interactions in collective cell behaviour. J R Soc Interface 2020; 17:20200143. [PMID: 32343933 DOI: 10.1098/rsif.2020.0143] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Scratch assays are routinely used to study collective cell behaviour in vitro. Typical experimental protocols do not vary the initial density of cells, and typical mathematical modelling approaches describe cell motility and proliferation based on assumptions of linear diffusion and logistic growth. Jin et al. (Jin et al. 2016 J. Theor. Biol. 390, 136-145 (doi:10.1016/j.jtbi.2015.10.040)) find that the behaviour of cells in scratch assays is density-dependent, and show that standard modelling approaches cannot simultaneously describe data initiated across a range of initial densities. To address this limitation, we calibrate an individual-based model to scratch assay data across a large range of initial densities. Our model allows proliferation, motility, and a direction bias to depend on interactions between neighbouring cells. By considering a hierarchy of models where we systematically and sequentially remove interactions, we perform model selection analysis to identify the minimum interactions required for the model to simultaneously describe data across all initial densities. The calibrated model is able to match the experimental data across all densities using a single parameter distribution, and captures details about the spatial structure of cells. Our results provide strong evidence to suggest that motility is density-dependent in these experiments. On the other hand, we do not see the effect of crowding on proliferation in these experiments. These results are significant as they are precisely the opposite of the assumptions in standard continuum models, such as the Fisher-Kolmogorov equation and its generalizations.
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Affiliation(s)
- Alexander P Browning
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Wang Jin
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Michael J Plank
- Biomathematics Research Centre, University of Canterbury, Christchurch, New Zealand.,Te Pūnaha Matatini, a New Zealand Centre of Research Excellence, New Zealand
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
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28
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Gallaher JA, Massey SC, Hawkins-Daarud A, Noticewala SS, Rockne RC, Johnston SK, Gonzalez-Cuyar L, Juliano J, Gil O, Swanson KR, Canoll P, Anderson ARA. From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response. PLoS Comput Biol 2020; 16:e1007672. [PMID: 32101537 PMCID: PMC7062288 DOI: 10.1371/journal.pcbi.1007672] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 03/09/2020] [Accepted: 01/21/2020] [Indexed: 11/18/2022] Open
Abstract
Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens.
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Affiliation(s)
- Jill A. Gallaher
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Susan C. Massey
- Precision NeuroTherapeutics Innovation Program, Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Precision NeuroTherapeutics Innovation Program, Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Sonal S. Noticewala
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, United States of America
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Russell C. Rockne
- Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, California, United States of America
| | - Sandra K. Johnston
- Precision NeuroTherapeutics Innovation Program, Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiology, University of Washington, Seattle, Washington, United States of America
| | - Luis Gonzalez-Cuyar
- Department of Pathology, University of Washington, Seattle, Washington, United States of America
| | - Joseph Juliano
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Orlando Gil
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, United States of America
- Department of Biology, Hunter College, City University of New York, New York, New York, United States of America
| | - Kristin R. Swanson
- Precision NeuroTherapeutics Innovation Program, Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Peter Canoll
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, United States of America
| | - Alexander R. A. Anderson
- Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
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29
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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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30
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Smalley I, Kim E, Li J, Spence P, Wyatt CJ, Eroglu Z, Sondak VK, Messina JL, Babacan NA, Maria-Engler SS, De Armas L, Williams SL, Gatenby RA, Chen YA, Anderson ARA, Smalley KSM. Leveraging transcriptional dynamics to improve BRAF inhibitor responses in melanoma. EBioMedicine 2019; 48:178-190. [PMID: 31594749 PMCID: PMC6838387 DOI: 10.1016/j.ebiom.2019.09.023] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/13/2019] [Accepted: 09/13/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Melanoma is a heterogeneous tumour, but the impact of this heterogeneity upon therapeutic response is not well understood. METHODS Single cell mRNA analysis was used to define the transcriptional heterogeneity of melanoma and its dynamic response to BRAF inhibitor therapy and treatment holidays. Discrete transcriptional states were defined in cell lines and melanoma patient specimens that predicted initial sensitivity to BRAF inhibition and the potential for effective re-challenge following resistance. A mathematical model was developed to maintain competition between the drug-sensitive and resistant states, which was validated in vivo. FINDINGS Our analyses showed melanoma cell lines and patient specimens to be composed of >3 transcriptionally distinct states. The cell state composition was dynamically regulated in response to BRAF inhibitor therapy and drug holidays. Transcriptional state composition predicted for therapy response. The differences in fitness between the different transcriptional states were leveraged to develop a mathematical model that optimized therapy schedules to retain the drug sensitive population. In vivo validation demonstrated that the personalized adaptive dosing schedules outperformed continuous or fixed intermittent BRAF inhibitor schedules. INTERPRETATION Our study provides the first evidence that transcriptional heterogeneity at the single cell level predicts for initial BRAF inhibitor sensitivity. We further demonstrate that manipulating transcriptional heterogeneity through personalized adaptive therapy schedules can delay the time to resistance. FUNDING This work was funded by the National Institutes of Health. The funder played no role in assembly of the manuscript.
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Affiliation(s)
- Inna Smalley
- The Department of Tumor Biology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Eunjung Kim
- Department of Integrated Mathematical Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Jiannong Li
- Department of Bioinformatics and Biostatistics, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Paige Spence
- The Department of Tumor Biology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Clayton J Wyatt
- The Department of Tumor Biology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Zeynep Eroglu
- Department of Cutaneous Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Vernon K Sondak
- Department of Cutaneous Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Jane L Messina
- Department of Cutaneous Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA; Department of Anatomic Pathology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Nalan Akgul Babacan
- Department of Cutaneous Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Silvya Stuchi Maria-Engler
- Department of Clinical Analysis and Toxicology, School of Pharmaceutical Sciences, University of Sao Paulo, Brazil
| | - Lesley De Armas
- Sylvester Comprehensive Cancer Center, The University of Miami, Miami, FL, USA
| | - Sion L Williams
- Sylvester Comprehensive Cancer Center, The University of Miami, Miami, FL, USA
| | - Robert A Gatenby
- Department of Bioinformatics and Biostatistics, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA
| | - Y Ann Chen
- Department of Bioinformatics and Biostatistics, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA.
| | - Alexander R A Anderson
- Department of Integrated Mathematical Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA.
| | - Keiran S M Smalley
- The Department of Tumor Biology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA; Department of Cutaneous Oncology, The Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, FL, USA.
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31
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Lipkova J, Angelikopoulos P, Wu S, Alberts E, Wiestler B, Diehl C, Preibisch C, Pyka T, Combs SE, Hadjidoukas P, Van Leemput K, Koumoutsakos P, Lowengrub J, Menze B. Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1875-1884. [PMID: 30835219 PMCID: PMC7170051 DOI: 10.1109/tmi.2019.2902044] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.
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32
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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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33
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Molecular and Clinical Insights into the Invasive Capacity of Glioblastoma Cells. JOURNAL OF ONCOLOGY 2019; 2019:1740763. [PMID: 31467533 PMCID: PMC6699388 DOI: 10.1155/2019/1740763] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 07/01/2019] [Accepted: 07/07/2019] [Indexed: 12/22/2022]
Abstract
The invasive capacity of GBM is one of the key tumoral features associated with treatment resistance, recurrence, and poor overall survival. The molecular machinery underlying GBM invasiveness comprises an intricate network of signaling pathways and interactions with the extracellular matrix and host cells. Among them, PI3k/Akt, Wnt, Hedgehog, and NFkB play a crucial role in the cellular processes related to invasion. A better understanding of these pathways could potentially help in developing new therapeutic approaches with better outcomes. Nevertheless, despite significant advances made over the last decade on these molecular and cellular mechanisms, they have not been translated into the clinical practice. Moreover, targeting the infiltrative tumor and its significance regarding outcome is still a major clinical challenge. For instance, the pre- and intraoperative methods used to identify the infiltrative tumor are limited when trying to accurately define the tumor boundaries and the burden of tumor cells in the infiltrated parenchyma. Besides, the impact of treating the infiltrative tumor remains unclear. Here we aim to highlight the molecular and clinical hallmarks of invasion in GBM.
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34
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Gaw N, Hawkins-Daarud A, Hu LS, Yoon H, Wang L, Xu Y, Jackson PR, Singleton KW, Baxter LC, Eschbacher J, Gonzales A, Nespodzany A, Smith K, Nakaji P, Mitchell JR, Wu T, Swanson KR, Li J. Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Sci Rep 2019; 9:10063. [PMID: 31296889 PMCID: PMC6624304 DOI: 10.1038/s41598-019-46296-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 06/26/2019] [Indexed: 01/30/2023] Open
Abstract
Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.
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Affiliation(s)
- Nathan Gaw
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Andrea Hawkins-Daarud
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA.
| | - Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Hyunsoo Yoon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Lujia Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Yanzhe Xu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Pamela R Jackson
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Kyle W Singleton
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Jennifer Eschbacher
- Department of Pathology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Ashlyn Gonzales
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Ashley Nespodzany
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Kris Smith
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Peter Nakaji
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - J Ross Mitchell
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, 33612, USA
| | - Teresa Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Kristin R Swanson
- Precision NeuroTherapeutics (PNT) Lab, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, Arizona, 85054, USA
| | - Jing Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
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35
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Han L, Eikenberry S, He C, Johnson L, Preul MC, Kostelich EJ, Kuang Y. Patient-specific parameter estimates of glioblastoma multiforme growth dynamics from a model with explicit birth and death rates. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2019; 16:5307-5323. [PMID: 31499714 PMCID: PMC7304543 DOI: 10.3934/mbe.2019265] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Glioblastoma multiforme (GBM) is an aggressive primary brain cancer with a grim prog-nosis. Its morphology is heterogeneous, but prototypically consists of an inner, largely necrotic core surrounded by an outer, contrast-enhancing rim, and often extensive tumor-associated edema beyond. This structure is usually demonstrated by magnetic resonance imaging (MRI). To help relate the three highly idealized components of GBMs (i.e., necrotic core, enhancing rim, and maximum edema ex-tent) to the underlying growth "laws," a mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. This model generates a traveling-wave solution that mimics tumor progression. We develop several novel methods to approximate key characteristics of the wave profile, which can be compared with MRI data. Several simplified forms of growth and death terms and their parameter identifiability are studied. We use several test cases of MRI data of GBM patients to yield personalized parameterizations of the model, and the biological and clinical implications are discussed.
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Affiliation(s)
- Lifeng Han
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Steffen Eikenberry
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Changhan He
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Lauren Johnson
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Mark C. Preul
- Department of Neurosurgery, Barrow Neurological Institute, St. Josephs Hospital and Medical Center, Phoenix, AZ 85013, USA
| | - Eric J. Kostelich
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Yang Kuang
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
- Correspondence:
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36
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Mascheroni P, López Alfonso JC, Kalli M, Stylianopoulos T, Meyer-Hermann M, Hatzikirou H. On the Impact of Chemo-Mechanically Induced Phenotypic Transitions in Gliomas. Cancers (Basel) 2019; 11:cancers11050716. [PMID: 31137643 PMCID: PMC6562768 DOI: 10.3390/cancers11050716] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 05/07/2019] [Accepted: 05/17/2019] [Indexed: 11/16/2022] Open
Abstract
Tumor microenvironment is a critical player in glioma progression, and novel therapies for its targeting have been recently proposed. In particular, stress-alleviation strategies act on the tumor by reducing its stiffness, decreasing solid stresses and improving blood perfusion. However, these microenvironmental changes trigger chemo-mechanically induced cellular phenotypic transitions whose impact on therapy outcomes is not completely understood. In this work we analyze the effects of mechanical compression on migration and proliferation of glioma cells. We derive a mathematical model of glioma progression focusing on cellular phenotypic plasticity. Our results reveal a trade-off between tumor infiltration and cellular content as a consequence of stress-alleviation approaches. We discuss how these novel findings increase the current understanding of glioma/microenvironment interactions and can contribute to new strategies for improved therapeutic outcomes.
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Affiliation(s)
- Pietro Mascheroni
- Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, 38106 Braunschweig, Germany.
| | - Juan Carlos López Alfonso
- Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, 38106 Braunschweig, Germany.
- Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, 30625 Hannover, Germany.
| | - Maria Kalli
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, 1678 Nicosia, Cyprus.
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, 1678 Nicosia, Cyprus.
| | - Michael Meyer-Hermann
- Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, 38106 Braunschweig, Germany.
- Centre for Individualized Infection Medicine, 30625 Hannover, Germany.
- Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, 38106 Braunschweig, Germany.
| | - Haralampos Hatzikirou
- Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, 38106 Braunschweig, Germany.
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37
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Lai X, Geier OM, Fleischer T, Garred Ø, Borgen E, Funke SW, Kumar S, Rognes ME, Seierstad T, Børresen-Dale AL, Kristensen VN, Engebraaten O, Köhn-Luque A, Frigessi A. Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data. Cancer Res 2019; 79:4293-4304. [PMID: 31118201 DOI: 10.1158/0008-5472.can-18-1804] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 02/13/2019] [Accepted: 05/17/2019] [Indexed: 11/16/2022]
Abstract
The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes. SIGNIFICANCE: Mathematical modeling is used to validate possible mechanisms of tumor growth, resistance, and treatment outcome.
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Affiliation(s)
- Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Oliver M Geier
- Department of Diagnostic Physics, Clinic of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Thomas Fleischer
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Øystein Garred
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Elin Borgen
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Simon W Funke
- Center for Biomedical Computing, Simula Research Laboratory, Lysaker, Norway
| | - Surendra Kumar
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Marie E Rognes
- Center for Biomedical Computing, Simula Research Laboratory, Lysaker, Norway
| | - Therese Seierstad
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Anne-Lise Børresen-Dale
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Vessela N Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Olav Engebraaten
- Department of Oncology, Oslo University Hospital, Oslo, Norway.,Department of Tumor Biology, Institute for Cancer Research, University of Oslo, Oslo, Norway
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway. .,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
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38
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Scheufele K, Mang A, Gholami A, Davatzikos C, Biros G, Mehl M. Coupling brain-tumor biophysical models and diffeomorphic image registration. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2019; 347:533-567. [PMID: 31857736 PMCID: PMC6922029 DOI: 10.1016/j.cma.2018.12.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a framework for joint image registration and biophysical inversion and we apply it to analyze MR images of glioblastomas (primary brain tumors). We have two applications in mind. The first one is normal-to-abnormal image registration in the presence of tumor-induced topology differences. The second one is biophysical inversion based on single-time patient data. The underlying optimization problem is highly non-linear and non-convex and has not been solved before with a gradient-based approach. Given the segmentation of a normal brain MRI and the segmentation of a cancer patient MRI, we determine tumor growth parameters and a registration map so that if we "grow a tumor" (using our tumor model) in the normal brain and then register it to the patient image, then the registration mismatch is as small as possible. This "coupled problem" two-way couples the biophysical inversion and the registration problem. In the image registration step we solve a large-deformation diffeomorphic registration problem parameterized by an Eulerian velocity field. In the biophysical inversion step we estimate parameters in a reaction-diffusion tumor growth model that is formulated as a partial differential equation (PDE). In SIBIA, we couple these two sub-components in an iterative manner. We first presented the components of SIBIA in "Gholami et al., Framework for Scalable Biophysics-based Image Analysis, IEEE/ACM Proceedings of the SC2017", in which we derived parallel distributed memory algorithms and software modules for the decoupled registration and biophysical inverse problems. In this paper, our contributions are the introduction of a PDE-constrained optimization formulation of the coupled problem, and the derivation of a Picard iterative solution scheme. We perform extensive tests to experimentally assess the performance of our method on synthetic and clinical datasets. We demonstrate the convergence of the SIBIA optimization solver in different usage scenarios. We demonstrate that using SIBIA, we can accurately solve the coupled problem in three dimensions (2563 resolution) in a few minutes using 11 dual-x86 nodes.
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Affiliation(s)
- Klaudius Scheufele
- University of Stuttgart, IPVS, Universitätstraße 38, 70569 Stuttgart, Germany
| | - Andreas Mang
- University of Houston, Department of Mathematics, 3551 Cullen Blvd., Houston, TX 77204-3008, USA
| | - Amir Gholami
- University of California Berkeley, EECS, Berkeley, CA 94720-1776, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania School of Medicine, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
| | - George Biros
- University of Texas, ICES, 201 East 24th St, Austin, TX 78712-1229, USA
| | - Miriam Mehl
- University of Stuttgart, IPVS, Universitätstraße 38, 70569 Stuttgart, Germany
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39
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Using Experimental Data and Information Criteria to Guide Model Selection for Reaction–Diffusion Problems in Mathematical Biology. Bull Math Biol 2019; 81:1760-1804. [DOI: 10.1007/s11538-019-00589-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 02/20/2019] [Indexed: 12/20/2022]
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40
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Angeli S, Emblem KE, Due-Tonnessen P, Stylianopoulos T. Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI. NEUROIMAGE-CLINICAL 2018; 20:664-673. [PMID: 30211003 PMCID: PMC6134360 DOI: 10.1016/j.nicl.2018.08.032] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 07/25/2018] [Accepted: 08/31/2018] [Indexed: 01/09/2023]
Abstract
Previous studies to simulate brain tumor progression, often investigate either temporal changes in cancer cell density or the overall tissue-level growth of the tumor mass. Here, we developed a computational model to bridge these two approaches. The model incorporates the tumor biomechanical response at the tissue level and accounts for cellular events by modeling cancer cell proliferation, infiltration to surrounding tissues, and invasion to distant locations. Moreover, acquisition of high resolution human data from anatomical magnetic resonance imaging, diffusion tensor imaging and perfusion imaging was employed within the simulations towards a realistic and patient specific model. The model predicted the intratumoral mechanical stresses to range from 20 to 34 kPa, which caused an up to 4.5 mm displacement to the adjacent healthy tissue. Furthermore, the model predicted plausible cancer cell invasion patterns within the brain along the white matter fiber tracts. Finally, by varying the tumor vascular density and its invasive outer ring thickness, our model showed the potential of these parameters for guiding the timing (83–90 days) of cancer cell distant invasion as well as the number (0–2 sites) and location (temportal and/or parietal lobe) of the invasion sites.
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Affiliation(s)
- Stelios Angeli
- Cancer Biophysics laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - Kyrre E Emblem
- Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Paulina Due-Tonnessen
- Department of Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
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41
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Luján E, Soto D, Rosito MS, Soba A, Guerra LN, Calvo JC, Marshall G, Suárez C. Microenvironmental influence on microtumour infiltration patterns: 3D-mathematical modelling supported by in vitro studies. Integr Biol (Camb) 2018; 10:325-334. [PMID: 29741547 DOI: 10.1039/c8ib00049b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Mathematical modelling approaches have become increasingly abundant in cancer research. Tumour infiltration extent and its spatial organization depend both on the tumour type and stage and on the bio-physicochemical characteristics of the microenvironment. This sets a complex scenario that often requires a multidisciplinary and individually adjusted approach. The ultimate goal of this work is to present an experimental/numerical combined method for the development of a three-dimensional mathematical model with the ability to reproduce the growth and infiltration patterns of a given avascular microtumour in response to different microenvironmental conditions. The model is based on a diffusion-convection reaction equation that considers logistic proliferation, volumetric growth, a rim of proliferative cells at the tumour surface, and invasion with diffusive and convective components. The parameter values of the model were fitted to experimental results while radial velocity and diffusion coefficients were made spatially variable in a case-specific way through the introduction of a shape function and a diffusion-limited-aggregation (DLA)-derived fractal matrix, respectively, according to the infiltration pattern observed. The in vitro model consists of multicellular tumour spheroids (MTSs) of an epithelial mammary tumour cell line (LM3) immersed in a collagen I gel matrix with a standard culture medium ("naive" matrix) or a conditioned medium from adipocytes or preadipocytes ("conditioned" matrix). It was experimentally determined that both adipocyte and preadipocyte conditioned media had the ability to change the MTS infiltration pattern from collective and laminar to an individual and atomized one. Numerical simulations were able to adequately reproduce qualitatively and quantitatively both kinds of infiltration patterns, which were determined by area quantification, analysis of fractal dimensions and lacunarity, and Bland-Altman analysis. These results suggest that the combined approach presented here could be established as a new framework with interesting potential applications at both the basic and clinical levels in the oncology area.
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Affiliation(s)
- Emmanuel Luján
- Laboratorio de Sistemas Complejos, Instituto de Física del Plasma, CONICET-UBA, Buenos Aires, Argentina.
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42
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Rutter EM, Banks HT, Flores KB. Estimating intratumoral heterogeneity from spatiotemporal data. J Math Biol 2018; 77:1999-2022. [DOI: 10.1007/s00285-018-1238-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 04/13/2018] [Indexed: 11/24/2022]
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43
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Dufour A, Gontran E, Deroulers C, Varlet P, Pallud J, Grammaticos B, Badoual M. Modeling the dynamics of oligodendrocyte precursor cells and the genesis of gliomas. PLoS Comput Biol 2018; 14:e1005977. [PMID: 29590097 PMCID: PMC5903643 DOI: 10.1371/journal.pcbi.1005977] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 04/17/2018] [Accepted: 01/10/2018] [Indexed: 11/24/2022] Open
Abstract
Oligodendrocyte precursor cells (OPCs) have remarkable properties: they represent the most abundant cycling cell population in the adult normal brain and they manage to achieve a uniform and constant density throughout the adult brain. This equilibrium is obtained by the interplay of four processes: division, differentiation or death, migration and active self-repulsion. They are also strongly suspected to be at the origin of gliomas, when their equilibrium is disrupted. In this article, we present a model of the dynamics of OPCs, first in a normal tissue. This model is based on a cellular automaton and its rules are mimicking the ones that regulate the dynamics of real OPCs. The model is able to reproduce the homeostasis of the cell population, with the maintenance of a constant and uniform cell density and the healing of a lesion. We show that there exists a fair quantitative agreement between the simulated and experimental parameters, such as the cell velocity, the time taken to close a lesion, and the duration of the cell cycle. We present three possible scenarios of disruption of the equilibrium: the appearance of an over-proliferating cell, of a deadless/non-differentiating cell, or of a cell that lost any contact-inhibition. We show that the appearance of an over-proliferating cell is sufficient to trigger the growth of a tumor that has low-grade glioma features: an invasive behaviour, a linear radial growth of the tumor with a corresponding growth velocity of less than 2 mm per year, as well a cell density at the center which exceeds the one in normal tissue by a factor of less than two. The loss of contact inhibition leads to a more high-grade-like glioma. The results of our model contribute to the body of evidence that identify OPCs as possible cells of origin of gliomas. Gliomas are the most common brain tumors and result in more years of life lost than any other tumor. Standard treatments only confer a limited improvement in overall survival, underscoring the need for new therapies. Finding the type of cells at the origin of these tumors could lead to the development of new drugs, specifically targeted towards these cells. The oligodendrocyte precursor cells are suspected to be these cells of origin, because they continue to proliferate through all the adult life. In this article, we present a model of the dynamics of these cells, first in the normal brain, and then we extrapolate our model to the pathological situation. We study several scenarios where, from the normal situation, a cell appears with one property different from those of the normal cells. We show that the alteration of only one of the properties of these cells in the model can lead to the formation of gliomas with different aggressiveness and very similar to real gliomas, reinforcing the suspicion that the precursor cells are at the origin of gliomas.
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Affiliation(s)
- Aloys Dufour
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
| | - Emilie Gontran
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
| | - Christophe Deroulers
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
| | - Pascale Varlet
- Department of Neuropathology, Sainte-Anne Hospital, IMA-Brain, INSERM U894, Univ Paris Descartes, Paris, France
| | - Johan Pallud
- Department of Neurosurgery, Sainte-Anne Hospital, IMA-Brain, INSERM U894, Univ Paris Descartes, Paris, France
| | - Basile Grammaticos
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
| | - Mathilde Badoual
- IMNC Laboratory, CNRS, Univ Paris Saclay, Univ Paris-Sud, Univ Paris Diderot, France
- * E-mail:
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44
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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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45
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Abstract
Systems Biology represents nowadays a promising standard framework for natural and human sciences to attack complicated problems involving Life. Here a particular application of such a program is discussed in the case of Cancer, by using a basic toy model for solid tumor spread for framing together two apparently different conceptual leading paradigms of Oncogenesis.
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Affiliation(s)
- Christian Cherubini
- Unit of Nonlinear Physics and Mathematical Modeling, Departmental Faculty of Engineering, University Campus Bio-Medico of Rome, Via A. del Portillo 21, 00128, Rome, Italy
- International Center for Relativistic Astrophysics-I.C.R.A, University Campus Bio-Medico of Rome, Via A. del Portillo 21, 00128, Rome, Italy
| | - Simonetta Filippi
- Unit of Nonlinear Physics and Mathematical Modeling, Departmental Faculty of Engineering, University Campus Bio-Medico of Rome, Via A. del Portillo 21, 00128, Rome, Italy.
- International Center for Relativistic Astrophysics-I.C.R.A, University Campus Bio-Medico of Rome, Via A. del Portillo 21, 00128, Rome, Italy.
| | - Alessandro Loppini
- Unit of Nonlinear Physics and Mathematical Modeling, Departmental Faculty of Engineering, University Campus Bio-Medico of Rome, Via A. del Portillo 21, 00128, Rome, Italy
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46
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Kolch W, Fey D. Personalized Computational Models as Biomarkers. J Pers Med 2017; 7:jpm7030009. [PMID: 28862657 PMCID: PMC5618155 DOI: 10.3390/jpm7030009] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 08/29/2017] [Accepted: 08/30/2017] [Indexed: 12/24/2022] Open
Abstract
Biomarkers are cornerstones of clinical medicine, and personalized medicine, in particular, is highly dependent on reliable and highly accurate biomarkers for individualized diagnosis and treatment choice. Modern omics technologies, such as genome sequencing, allow molecular profiling of individual patients with unprecedented resolution, but biomarkers based on these technologies often lack the dynamic element to follow the progression of a disease or response to therapy. Here, we discuss computational models as a new conceptual approach to biomarker discovery and design. Being able to integrate a large amount of information, including dynamic information, computational models can simulate disease evolution and response to therapy with high sensitivity and specificity. By populating these models with personal data, they can be highly individualized and will provide a powerful new tool in the armory of personalized medicine.
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Affiliation(s)
- Walter Kolch
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.
- Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.
- School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Dirk Fey
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.
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47
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Levin VA, Abrey LE, Heffron TP, Tonge PJ, Dar AC, Weiss WA, Gallo JM. CNS Anticancer Drug Discovery and Development: 2016 conference insights. CNS Oncol 2017; 6:167-177. [PMID: 28718326 PMCID: PMC6009211 DOI: 10.2217/cns-2017-0014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 03/23/2017] [Indexed: 11/21/2022] Open
Abstract
CNS Anticancer Drug Discovery and Development, 16-17 November 2016, Scottsdale, AZ, USA The 2016 second CNS Anticancer Drug Discovery and Development Conference addressed diverse viewpoints about why new drug discovery/development focused on CNS cancers has been sorely lacking. Despite more than 70,000 individuals in the USA being diagnosed with a primary brain malignancy and 151,669-286,486 suffering from metastatic CNS cancer, in 1999, temozolomide was the last drug approved by the US FDA as an anticancer agent for high-grade gliomas. Among the topics discussed were economic factors and pharmaceutical risk assessments, regulatory constraints and perceptions and the need for improved imaging surrogates of drug activity. Included were modeling tumor growth and drug effects in a medical environment in which direct tumor sampling for biological effects can be problematic, potential new drugs under investigation and targets for drug discovery and development. The long trajectory and diverse impediments to novel drug discovery, and expectation that more than one drug will be needed to adequately inhibit critical intracellular tumor pathways were viewed as major disincentives for most pharmaceutical/biotechnology companies. While there were a few unanimities, one consensus is the need for continued and focused discussion among academic and industry scientists and clinicians to address tumor targets, new drug chemistry, and more time- and cost-efficient clinical trials based on surrogate end points.
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Affiliation(s)
- Victor A Levin
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, 94143, USA
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | | | | | - Peter J Tonge
- Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Arvin C Dar
- Oncological & Pharmacological Sciences, Mount Sinai Icahn School of Medicine, New York, NY 10029, USA
| | - William A Weiss
- Department of Neurology, University of California San Francisco, San Francisco, CA 949143, USA (W.A.W.), CA, USA
| | - James M Gallo
- Albany College of Pharmacy & Health Sciences, Albany, NY 12208, USA (J.M.G.)
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48
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Rutter EM, Stepien TL, Anderies BJ, Plasencia JD, Woolf EC, Scheck AC, Turner GH, Liu Q, Frakes D, Kodibagkar V, Kuang Y, Preul MC, Kostelich EJ. Mathematical Analysis of Glioma Growth in a Murine Model. Sci Rep 2017; 7:2508. [PMID: 28566701 PMCID: PMC5451439 DOI: 10.1038/s41598-017-02462-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 03/13/2017] [Indexed: 11/21/2022] Open
Abstract
Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.
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Affiliation(s)
- Erica M Rutter
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA. .,Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, 27695, USA.
| | - Tracy L Stepien
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA.,Department of Mathematics, Univeristy of Arizona, Tucson, AZ, 85721, USA
| | - Barrett J Anderies
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA.,School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Jonathan D Plasencia
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Eric C Woolf
- School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA.,Neuro-Oncology Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Adrienne C Scheck
- School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA.,Neuro-Oncology Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA.,Department of Neurosurgery, Neurosurgery Research Lab, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Gregory H Turner
- BNI-ASU Center for Preclinical Imaging, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Qingwei Liu
- BNI-ASU Center for Preclinical Imaging, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - David Frakes
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Vikram Kodibagkar
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Yang Kuang
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA
| | - Mark C Preul
- Department of Neurosurgery, Neurosurgery Research Lab, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Eric J Kostelich
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA
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49
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Yan H, Romero-López M, Benitez LI, Di K, Frieboes HB, Hughes CCW, Bota DA, Lowengrub JS. 3D Mathematical Modeling of Glioblastoma Suggests That Transdifferentiated Vascular Endothelial Cells Mediate Resistance to Current Standard-of-Care Therapy. Cancer Res 2017; 77:4171-4184. [PMID: 28536277 DOI: 10.1158/0008-5472.can-16-3094] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 02/24/2017] [Accepted: 05/16/2017] [Indexed: 01/17/2023]
Abstract
Glioblastoma (GBM), the most aggressive brain tumor in human patients, is decidedly heterogeneous and highly vascularized. Glioma stem/initiating cells (GSC) are found to play a crucial role by increasing cancer aggressiveness and promoting resistance to therapy. Recently, cross-talk between GSC and vascular endothelial cells has been shown to significantly promote GSC self-renewal and tumor progression. Furthermore, GSC also transdifferentiate into bona fide vascular endothelial cells (GEC), which inherit mutations present in GSC and are resistant to traditional antiangiogenic therapies. Here we use three-dimensional mathematical modeling to investigate GBM progression and response to therapy. The model predicted that GSCs drive invasive fingering and that GEC spontaneously form a network within the hypoxic core, consistent with published experimental findings. Standard-of-care treatments using DNA-targeted therapy (radiation/chemo) together with antiangiogenic therapies reduced GBM tumor size but increased invasiveness. Anti-GEC treatments blocked the GEC support of GSCs and reduced tumor size but led to increased invasiveness. Anti-GSC therapies that promote differentiation or disturb the stem cell niche effectively reduced tumor invasiveness and size, but were ultimately limited in reducing tumor size because GECs maintain GSCs. Our study suggests that a combinatorial regimen targeting the vasculature, GSCs, and GECs, using drugs already approved by the FDA, can reduce both tumor size and invasiveness and could lead to tumor eradication. Cancer Res; 77(15); 4171-84. ©2017 AACR.
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Affiliation(s)
- Huaming Yan
- Department of Mathematics, University of California, Irvine, California
| | - Mónica Romero-López
- Department of Biomedical Engineering, University of California, Irvine, California
| | - Lesly I Benitez
- Department of Molecular Biology and Biochemistry, University of California, Irvine, California
| | - Kaijun Di
- Chao Comprehensive Cancer Center, University of California, Irvine, California.,Department of Neurological Surgery, University of California, Irvine, California
| | - Hermann B Frieboes
- James Graham Brown Cancer Center, University of Louisville.,Department of Bioengineering, University of Louisville, Louisville, Kentucky
| | - Christopher C W Hughes
- Department of Biomedical Engineering, University of California, Irvine, California.,Department of Molecular Biology and Biochemistry, University of California, Irvine, California.,Chao Comprehensive Cancer Center, University of California, Irvine, California.,Center for Complex Biological Systems, University of California, Irvine, California
| | - Daniela A Bota
- Chao Comprehensive Cancer Center, University of California, Irvine, California.,Department of Neurological Surgery, University of California, Irvine, California.,Department of Neurology, University of California, Irvine, California
| | - John S Lowengrub
- Department of Mathematics, University of California, Irvine, California. .,Department of Biomedical Engineering, University of California, Irvine, California.,Chao Comprehensive Cancer Center, University of California, Irvine, California.,Center for Complex Biological Systems, University of California, Irvine, California
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50
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A Patient-Specific Anisotropic Diffusion Model for Brain Tumour Spread. Bull Math Biol 2017; 80:1259-1291. [DOI: 10.1007/s11538-017-0271-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 03/15/2017] [Indexed: 02/01/2023]
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