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Shah S, Philipp LM, Giaimo S, Sebens S, Traulsen A, Raatz M. Understanding and leveraging phenotypic plasticity during metastasis formation. NPJ Syst Biol Appl 2023; 9:48. [PMID: 37803056 PMCID: PMC10558468 DOI: 10.1038/s41540-023-00309-1] [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: 02/09/2023] [Accepted: 09/15/2023] [Indexed: 10/08/2023] Open
Abstract
Cancer metastasis is the process of detrimental systemic spread and the primary cause of cancer-related fatalities. Successful metastasis formation requires tumor cells to be proliferative and invasive; however, cells cannot be effective at both tasks simultaneously. Tumor cells compensate for this trade-off by changing their phenotype during metastasis formation through phenotypic plasticity. Given the changing selection pressures and competitive interactions that tumor cells face, it is poorly understood how plasticity shapes the process of metastasis formation. Here, we develop an ecology-inspired mathematical model with phenotypic plasticity and resource competition between phenotypes to address this knowledge gap. We find that phenotypically plastic tumor cell populations attain a stable phenotype equilibrium that maintains tumor cell heterogeneity. Considering treatment types inspired by chemo- and immunotherapy, we highlight that plasticity can protect tumors against interventions. Turning this strength into a weakness, we corroborate current clinical practices to use plasticity as a target for adjuvant therapy. We present a parsimonious view of tumor plasticity-driven metastasis that is quantitative and experimentally testable, and thus potentially improving the mechanistic understanding of metastasis at the cell population level, and its treatment consequences.
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Affiliation(s)
- Saumil Shah
- Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany.
| | - Lisa-Marie Philipp
- Institute for Experimental Cancer Research, Kiel University and University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, Building U30, Entrance 1, 24105, Kiel, Germany
| | - Stefano Giaimo
- Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany
| | - Susanne Sebens
- Institute for Experimental Cancer Research, Kiel University and University Hospital Schleswig-Holstein, Campus Kiel, Arnold-Heller-Str. 3, Building U30, Entrance 1, 24105, Kiel, Germany
| | - Arne Traulsen
- Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany
| | - Michael Raatz
- Department of Theoretical Biology, Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306, Plön, Germany
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2
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Surendran A, Jenner AL, Karimi E, Fiset B, Quail DF, Walsh LA, Craig M. Agent-Based Modelling Reveals the Role of the Tumor Microenvironment on the Short-Term Success of Combination Temozolomide/Immune Checkpoint Blockade to Treat Glioblastoma. J Pharmacol Exp Ther 2023; 387:66-77. [PMID: 37442619 DOI: 10.1124/jpet.122.001571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/08/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Glioblastoma is the most common and deadly primary brain tumor in adults. All glioblastoma patients receiving standard-of-care surgery-radiotherapy-chemotherapy (i.e., temozolomide (TMZ)) recur, with an average survival time of only 15 months. New approaches to the treatment of glioblastoma, including immune checkpoint blockade and oncolytic viruses, offer the possibility of improving glioblastoma outcomes and have as such been under intense study. Unfortunately, these treatment modalities have thus far failed to achieve approval. Recently, in an attempt to bolster efficacy and improve patient outcomes, regimens combining chemotherapy and immune checkpoint inhibitors have been tested in trials. Unfortunately, these efforts have not resulted in significant increases to patient survival. To better understand the various factors impacting treatment outcomes of combined TMZ and immune checkpoint blockade, we developed a systems-level, computational model that describes the interplay between glioblastoma, immune, and stromal cells with this combination treatment. Initializing our model to spatial resection patient samples labeled using imaging mass cytometry, our model's predictions show how the localization of glioblastoma cells, influence therapeutic success. We further validated these predictions in samples of brain metastases from patients given they generally respond better to checkpoint blockade compared with primary glioblastoma. Ultimately, our model provides novel insights into the mechanisms of therapeutic success of immune checkpoint inhibitors in brain tumors and delineates strategies to translate combination immunotherapy regimens more effectively into the clinic. SIGNIFICANCE STATEMENT: Extending survival times for glioblastoma patients remains a critical challenge. Although immunotherapies in combination with chemotherapy hold promise, clinical trials have not shown much success. Here, systems models calibrated to and validated against patient samples can improve preclinical and clinical studies by shedding light on the factors distinguishing responses/failures. By initializing our model with imaging mass cytometry visualization of patient samples, we elucidate how factors such as localization of glioblastoma cells and CD8+ T cell infiltration impact treatment outcomes.
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Affiliation(s)
- Anudeep Surendran
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada (A.S., M.C.); Centre de recherches mathématiques, Montréal, Canada (A.S.); School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia (A.L.J.); Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada (E.K., B.F., D.F.Q., L.A.W.); Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Medicine, Division of Experimental Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Human Genetics, McGill University, Montréal, Canada (L.A.W.); and Sainte-Justine University Hospital Research Centre, Montréal, Canada (M.C.)
| | - Adrianne L Jenner
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada (A.S., M.C.); Centre de recherches mathématiques, Montréal, Canada (A.S.); School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia (A.L.J.); Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada (E.K., B.F., D.F.Q., L.A.W.); Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Medicine, Division of Experimental Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Human Genetics, McGill University, Montréal, Canada (L.A.W.); and Sainte-Justine University Hospital Research Centre, Montréal, Canada (M.C.)
| | - Elham Karimi
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada (A.S., M.C.); Centre de recherches mathématiques, Montréal, Canada (A.S.); School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia (A.L.J.); Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada (E.K., B.F., D.F.Q., L.A.W.); Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Medicine, Division of Experimental Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Human Genetics, McGill University, Montréal, Canada (L.A.W.); and Sainte-Justine University Hospital Research Centre, Montréal, Canada (M.C.)
| | - Benoit Fiset
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada (A.S., M.C.); Centre de recherches mathématiques, Montréal, Canada (A.S.); School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia (A.L.J.); Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada (E.K., B.F., D.F.Q., L.A.W.); Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Medicine, Division of Experimental Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Human Genetics, McGill University, Montréal, Canada (L.A.W.); and Sainte-Justine University Hospital Research Centre, Montréal, Canada (M.C.)
| | - Daniela F Quail
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada (A.S., M.C.); Centre de recherches mathématiques, Montréal, Canada (A.S.); School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia (A.L.J.); Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada (E.K., B.F., D.F.Q., L.A.W.); Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Medicine, Division of Experimental Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Human Genetics, McGill University, Montréal, Canada (L.A.W.); and Sainte-Justine University Hospital Research Centre, Montréal, Canada (M.C.)
| | - Logan A Walsh
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada (A.S., M.C.); Centre de recherches mathématiques, Montréal, Canada (A.S.); School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia (A.L.J.); Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada (E.K., B.F., D.F.Q., L.A.W.); Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Medicine, Division of Experimental Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Human Genetics, McGill University, Montréal, Canada (L.A.W.); and Sainte-Justine University Hospital Research Centre, Montréal, Canada (M.C.)
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada (A.S., M.C.); Centre de recherches mathématiques, Montréal, Canada (A.S.); School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia (A.L.J.); Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, Canada (E.K., B.F., D.F.Q., L.A.W.); Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Medicine, Division of Experimental Medicine, McGill University, Montréal, Canada (D.F.Q.); Department of Human Genetics, McGill University, Montréal, Canada (L.A.W.); and Sainte-Justine University Hospital Research Centre, Montréal, Canada (M.C.)
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3
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Jenner AL, Smalley M, Goldman D, Goins WF, Cobbs CS, Puchalski RB, Chiocca EA, Lawler S, Macklin P, Goldman A, Craig M. Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy. iScience 2022; 25:104395. [PMID: 35637733 PMCID: PMC9142563 DOI: 10.1016/j.isci.2022.104395] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/18/2022] [Accepted: 04/08/2022] [Indexed: 11/26/2022] Open
Abstract
Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clinic.
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Affiliation(s)
- Adrianne L. Jenner
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
| | - Munisha Smalley
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | | | - William F. Goins
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Charles S. Cobbs
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - Ralph B. Puchalski
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - E. Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Sean Lawler
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Aaron Goldman
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
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4
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Storey K, Leder K, Hawkins-Daarud A, Swanson K, Ahmed AU, Rockne RC, Foo J. Glioblastoma Recurrence and the Role of O 6-Methylguanine-DNA Methyltransferase Promoter Methylation. JCO Clin Cancer Inform 2020; 3:1-12. [PMID: 30758983 DOI: 10.1200/cci.18.00062] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Tumor recurrence in glioblastoma multiforme (GBM) is often attributed to acquired resistance to the standard chemotherapeutic agent, temozolomide (TMZ). Promoter methylation of the DNA repair gene MGMT (O6-methylguanine-DNA methyltransferase) has been associated with sensitivity to TMZ, whereas increased expression of MGMT has been associated with TMZ resistance. Clinical studies have observed a downward shift in MGMT methylation percentage from primary to recurrent stage tumors; however, the evolutionary processes that drive this shift and more generally the emergence and growth of TMZ-resistant tumor subpopulations are still poorly understood. Here, we develop a mathematical model, parameterized using clinical and experimental data, to investigate the role of MGMT methylation in TMZ resistance during the standard treatment regimen for GBM-surgery, chemotherapy, and radiation. We first found that the observed downward shift in MGMT promoter methylation status between detection and recurrence cannot be explained solely by evolutionary selection. Next, our model suggests that TMZ has an inhibitory effect on maintenance methylation of MGMT after cell division. Finally, incorporating this inhibitory effect, we study the optimal number of TMZ doses per adjuvant cycle for patients with GBM with high and low levels of MGMT methylation at diagnosis.
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Affiliation(s)
- Katie Storey
- University of Minnesota Twin Cities, Minneapolis, MN
| | - Kevin Leder
- University of Minnesota Twin Cities, Minneapolis, MN
| | | | | | - Atique U Ahmed
- Northwestern University Feinberg School of Medicine, Chicago, IL
| | | | - Jasmine Foo
- University of Minnesota Twin Cities, Minneapolis, MN
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5
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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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Bregenzer ME, Horst EN, Mehta P, Novak CM, Raghavan S, Snyder CS, Mehta G. Integrated cancer tissue engineering models for precision medicine. PLoS One 2019; 14:e0216564. [PMID: 31075118 PMCID: PMC6510431 DOI: 10.1371/journal.pone.0216564] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Tumors are not merely cancerous cells that undergo mindless proliferation. Rather, they are highly organized and interconnected organ systems. Tumor cells reside in complex microenvironments in which they are subjected to a variety of physical and chemical stimuli that influence cell behavior and ultimately the progression and maintenance of the tumor. As cancer bioengineers, it is our responsibility to create physiologic models that enable accurate understanding of the multi-dimensional structure, organization, and complex relationships in diverse tumor microenvironments. Such models can greatly expedite clinical discovery and translation by closely replicating the physiological conditions while maintaining high tunability and control of extrinsic factors. In this review, we discuss the current models that target key aspects of the tumor microenvironment and their role in cancer progression. In order to address sources of experimental variation and model limitations, we also make recommendations for methods to improve overall physiologic reproducibility, experimental repeatability, and rigor within the field. Improvements can be made through an enhanced emphasis on mathematical modeling, standardized in vitro model characterization, transparent reporting of methodologies, and designing experiments with physiological metrics. Taken together these considerations will enhance the relevance of in vitro tumor models, biological understanding, and accelerate treatment exploration ultimately leading to improved clinical outcomes. Moreover, the development of robust, user-friendly models that integrate important stimuli will allow for the in-depth study of tumors as they undergo progression from non-transformed primary cells to metastatic disease and facilitate translation to a wide variety of biological and clinical studies.
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Affiliation(s)
- Michael E. Bregenzer
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Eric N. Horst
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Pooja Mehta
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Caymen M. Novak
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Shreya Raghavan
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Catherine S. Snyder
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Geeta Mehta
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- Rogel Cancer Center, School of Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
- Macromolecular Science and Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
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