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Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021; 16:5309-5338. [PMID: 34552262 PMCID: PMC9753909 DOI: 10.1038/s41596-021-00617-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - Anum S Kazerouni
- Departments of Biomedical Engineering, Austin, TX, USA
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - John Virostko
- Livestrong Cancer Institutes, Austin, TX, USA
- Departments of Diagnostic Medicine, Austin, TX, USA
- Departments of Oncology, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Julie C DiCarlo
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Ekrut
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | | | - Boone Goodgame
- Departments of Oncology, Austin, TX, USA
- Departments of Internal Medicine, The University of Texas at Austin, Austin, Texas, USA
- Seton Hospital, Austin, TX, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA.
- Livestrong Cancer Institutes, Austin, TX, USA.
- Departments of Biomedical Engineering, Austin, TX, USA.
- Departments of Diagnostic Medicine, Austin, TX, USA.
- Departments of Oncology, Austin, TX, USA.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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2
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Buhler CK, Terry RS, Link KG, Adler FR. Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:6305-6327. [PMID: 34517535 PMCID: PMC10625481 DOI: 10.3934/mbe.2021315] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
When eradication is impossible, cancer treatment aims to delay the emergence of resistance while minimizing cancer burden and treatment. Adaptive therapies may achieve these aims, with success based on three assumptions: resistance is costly, sensitive cells compete with resistant cells, and therapy reduces the population of sensitive cells. We use a range of mathematical models and treatment strategies to investigate the tradeoff between controlling cell populations and delaying the emergence of resistance. These models extend game theoretic and competition models with four additional components: 1) an Allee effect where cell populations grow more slowly at low population sizes, 2) healthy cells that compete with cancer cells, 3) immune cells that suppress cancer cells, and 4) resource competition for a growth factor like androgen. In comparing maximum tolerable dose, intermittent treatment, and adaptive therapy strategies, no therapeutic choice robustly breaks the three-way tradeoff among the three therapeutic aims. Almost all models show a tight tradeoff between time to emergence of resistant cells and cancer cell burden, with intermittent and adaptive therapies following identical curves. For most models, some adaptive therapies delay overall tumor growth more than intermittent therapies, but at the cost of higher cell populations. The Allee effect breaks these relationships, with some adaptive therapies performing poorly due to their failure to treat sufficiently to drive populations below the threshold. When eradication is impossible, no treatment can simultaneously delay emergence of resistance, limit total cancer cell numbers, and minimize treatment. Simple mathematical models can play a role in designing the next generation of therapies that balance these competing objectives.
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Affiliation(s)
- Cassidy K. Buhler
- Department of Decision Sciences and MIS, Drexel University, 3220 Market St, Philadelphia, PA 19104, USA
- Department of Mathematics, University of Utah, 155 S 1400 E, Salt Lake City, UT 84112, USA
| | - Rebecca S. Terry
- Department of Mathematics, University of Utah, 155 S 1400 E, Salt Lake City, UT 84112, USA
- Department of Mathematics, Computer Science and Statistics, St. Lawrence University, 23 Romoda Drive, Canton, NY 13617, USA
| | - Kathryn G. Link
- Department of Mathematics, University of California, Davis, One Shields Avenue, CA 95616, USA
| | - Frederick R. Adler
- Department of Mathematics, University of Utah, 155 S 1400 E, Salt Lake City, UT 84112, USA
- School of Biological Sciences, University of Utah, 257 S 1400 E, Salt Lake City, UT 84112, USA
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3
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Jarrett AM, Hormuth DA, Adhikarla V, Sahoo P, Abler D, Tumyan L, Schmolze D, Mortimer J, Rockne RC, Yankeelov TE. Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer. Sci Rep 2020; 10:20518. [PMID: 33239688 PMCID: PMC7688955 DOI: 10.1038/s41598-020-77397-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/29/2020] [Indexed: 12/20/2022] Open
Abstract
While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - Vikram Adhikarla
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
| | - Prativa Sahoo
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
| | - Daniel Abler
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Lusine Tumyan
- Department of Radiology, City of Hope National Medical Center, Duarte, CA, USA
| | - Daniel Schmolze
- Department of Pathology, City of Hope National Medical Center, Duarte, CA, USA
| | - Joanne Mortimer
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Russell C Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA.
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA.
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA.
- Department of Oncology, The University of Texas at Austin, Austin, TX, USA.
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W Dean Keeton Street Stop C0800, Austin, TX, 78712, USA.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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4
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Craig M, Jenner AL, Namgung B, Lee LP, Goldman A. Engineering in Medicine To Address the Challenge of Cancer Drug Resistance: From Micro- and Nanotechnologies to Computational and Mathematical Modeling. Chem Rev 2020; 121:3352-3389. [PMID: 33152247 DOI: 10.1021/acs.chemrev.0c00356] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug resistance has profoundly limited the success of cancer treatment, driving relapse, metastasis, and mortality. Nearly all anticancer drugs and even novel immunotherapies, which recalibrate the immune system for tumor recognition and destruction, have succumbed to resistance development. Engineers have emerged across mechanical, physical, chemical, mathematical, and biological disciplines to address the challenge of drug resistance using a combination of interdisciplinary tools and skill sets. This review explores the developing, complex, and under-recognized role of engineering in medicine to address the multitude of challenges in cancer drug resistance. Looking through the "lens" of intrinsic, extrinsic, and drug-induced resistance (also referred to as "tolerance"), we will discuss three specific areas where active innovation is driving novel treatment paradigms: (1) nanotechnology, which has revolutionized drug delivery in desmoplastic tissues, harnessing physiochemical characteristics to destroy tumors through photothermal therapy and rationally designed nanostructures to circumvent cancer immunotherapy failures, (2) bioengineered tumor models, which have benefitted from microfluidics and mechanical engineering, creating a paradigm shift in physiologically relevant environments to predict clinical refractoriness and enabling platforms for screening drug combinations to thwart resistance at the individual patient level, and (3) computational and mathematical modeling, which blends in silico simulations with molecular and evolutionary principles to map mutational patterns and model interactions between cells that promote resistance. On the basis that engineering in medicine has resulted in discoveries in resistance biology and successfully translated to clinical strategies that improve outcomes, we suggest the proliferation of multidisciplinary science that embraces engineering.
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Affiliation(s)
- Morgan Craig
- Department of Mathematics and Statistics, University of Montreal, Montreal, Quebec H3C 3J7, Canada.,Sainte-Justine University Hospital Research Centre, Montreal, Quebec H3S 2G4, Canada
| | - Adrianne L Jenner
- Department of Mathematics and Statistics, University of Montreal, Montreal, Quebec H3C 3J7, Canada.,Sainte-Justine University Hospital Research Centre, Montreal, Quebec H3S 2G4, Canada
| | - Bumseok Namgung
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
| | - Luke P Lee
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
| | - Aaron Goldman
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States.,Department of Medicine, Harvard Medical School, Boston, Massachusetts 02139, United States
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5
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Tran AP, Ali Al-Radhawi M, Kareva I, Wu J, Waxman DJ, Sontag ED. Delicate Balances in Cancer Chemotherapy: Modeling Immune Recruitment and Emergence of Systemic Drug Resistance. Front Immunol 2020; 11:1376. [PMID: 32695118 PMCID: PMC7338613 DOI: 10.3389/fimmu.2020.01376] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/29/2020] [Indexed: 01/21/2023] Open
Abstract
Metronomic chemotherapy can drastically enhance immunogenic tumor cell death. However, the mechanisms responsible are still incompletely understood. Here, we develop a mathematical model to elucidate the underlying complex interactions between tumor growth, immune system activation, and therapy-mediated immunogenic cell death. Our model is conceptually simple, yet it provides a surprisingly excellent fit to empirical data obtained from a GL261 SCID mouse glioma model treated with cyclophosphamide on a metronomic schedule. The model includes terms representing immune recruitment as well as the emergence of drug resistance during prolonged metronomic treatments. Strikingly, a single fixed set of parameters, adjusted neither for individuals nor for drug schedule, recapitulates experimental data across various drug regimens remarkably well, including treatments administered at intervals ranging from 6 to 12 days. Additionally, the model predicts peak immune activation times, rediscovering experimental data that had not been used in parameter fitting or in model construction. Notably, the validated model suggests that immunostimulatory and immunosuppressive intermediates are responsible for the observed phenomena of resistance and immune cell recruitment, and thus for variation of responses with respect to different schedules of drug administration.
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Affiliation(s)
- Anh Phong Tran
- Department of Chemical Engineering, Northeastern University, Boston, MA, United States
| | - M Ali Al-Radhawi
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Irina Kareva
- Mathematical and Computational Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, United States
| | - Junjie Wu
- Clinical Research Institute, Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - David J Waxman
- Department of Biology and Bioinformatics Program, Boston University, Boston, MA, United States
| | - Eduardo D Sontag
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
- Department of Bioengineering, Northeastern University, Boston, MA, United States
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, United States
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6
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Berrouet C, Dorilas N, Rejniak KA, Tuncer N. Comparison of Drug Inhibitory Effects ([Formula: see text]) in Monolayer and Spheroid Cultures. Bull Math Biol 2020; 82:68. [PMID: 32495209 PMCID: PMC9773863 DOI: 10.1007/s11538-020-00746-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 05/06/2020] [Indexed: 12/25/2022]
Abstract
Traditionally, the monolayer (two-dimensional) cell cultures are used for initial evaluation of the effectiveness of anticancer drugs. In particular, these experiments provide the [Formula: see text] curves that determine drug concentration that can inhibit growth of a tumor colony by half when compared to the cells grown with no exposure to the drug. Low [Formula: see text] value means that the drug is effective at low concentrations, and thus will show lower systemic toxicity when administered to the patient. However, in these experiments cells are grown in a monolayer, all well exposed to the drug, while in vivo tumors expand as three-dimensional multicellular masses, where inner cells have a limited access to the drug. Therefore, we performed computational studies to compare the [Formula: see text] curves for cells grown as a two-dimensional monolayer and a cross section through a three-dimensional spheroid. Our results identified conditions (drug diffusivity, drug action mechanisms and cell proliferation capabilities) under which these [Formula: see text] curves differ significantly. This will help experimentalists to better determine drug dosage for future in vivo experiments and clinical trials.
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Affiliation(s)
- Catherine Berrouet
- Department of Mathematical Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, USA
| | - Naika Dorilas
- Department of Mathematical Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, USA
| | - Katarzyna A. Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Necibe Tuncer
- Department of Mathematical Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, USA
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7
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Greene JM, Gevertz JL, Sontag ED. Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment. JCO Clin Cancer Inform 2020; 3:1-20. [PMID: 30969799 PMCID: PMC6873992 DOI: 10.1200/cci.18.00087] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Purpose Drug resistance is a major impediment to the success of cancer treatment. Resistance is typically thought to arise from random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that progression to drug resistance need not occur randomly, but instead may be induced by the treatment itself via either genetic changes or epigenetic alterations. This relatively novel notion of resistance complicates the already challenging task of designing effective treatment protocols. Materials and Methods To better understand resistance, we have developed a mathematical modeling framework that incorporates both spontaneous and drug-induced resistance. Results Our model demonstrates that the ability of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. We have also proven that the induction parameter in our model is theoretically identifiable and propose an in vitro protocol that could be used to determine a treatment’s propensity to induce resistance.
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Affiliation(s)
| | | | - Eduardo D Sontag
- Northeastern University, Boston, MA.,Harvard Medical School, Cambridge, MA
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8
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Pérez-Velázquez J, Rejniak KA. Drug-Induced Resistance in Micrometastases: Analysis of Spatio-Temporal Cell Lineages. Front Physiol 2020; 11:319. [PMID: 32362836 PMCID: PMC7180185 DOI: 10.3389/fphys.2020.00319] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 03/20/2020] [Indexed: 12/16/2022] Open
Abstract
Resistance to anti-cancer drugs is a major cause of treatment failure. While several intracellular mechanisms of resistance have been postulated, the role of extrinsic factors in the development of resistance in individual tumor cells is still not fully understood. Here we used a hybrid agent-based model to investigate how sensitive tumor cells develop drug resistance in the heterogeneous tumor microenvironment. We characterized the spatio-temporal evolution of lineages of the resistant cells and examined how resistance at the single-cell level contributes to the overall tumor resistance. We also developed new methods to track tumor cell adaptation, to trace cell viability trajectories and to examine the three-dimensional spatio-temporal lineage trees. Our findings indicate that drug-induced resistance can result from cells adaptation to the changes in drug distribution. Two modes of cell adaptation were identified that coincide with microenvironmental niches—areas sheltered by cell micro-communities (protectorates) or regions with limited drug penetration (refuga or sanctuaries). We also recognized that certain cells gave rise to lineages of resistant cells (precursors of resistance) and pinpointed three temporal periods and spatial locations at which such cells emerged. This supports the hypothesis that tumor micrometastases do not need to harbor cell populations with pre-existing resistance, but that individual tumor cells can adapt and develop resistance induced by the drug during the treatment.
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Affiliation(s)
- Judith Pérez-Velázquez
- Mathematical Modeling of Biological Systems, Centre for Mathematical Science, Technical University of Munich, Garching, Germany
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, Tampa, FL, United States
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9
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Mukaya EH, Mbianda XY. Macromolecular Prodrugs Containing Organoiron-Based Compounds in Cancer Research: A Review. Mini Rev Med Chem 2019; 20:726-738. [PMID: 31702511 DOI: 10.2174/1389557519666191107142926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/10/2019] [Accepted: 10/03/2019] [Indexed: 11/22/2022]
Abstract
Among the methods used for the treatment of cancer, chemotherapy is widely used, and it is by far one of the most unpleasant procedures given to a patient because of its severe side effects; while being necessary. One of the major problems in cancer chemotherapy is the limited selectivity of most of the drugs in current clinical use. Following administration, the active agent is distributed over the entire body and reaches not only the target cells or tissues but also interacts with healthy cells. In an attempt to overcome the side effects of anticancer drugs, the modification of the anticancer bioactive compounds has been a topic of active research for years. Numerous delivery systems such as drugcontaining liposomes, microencapsulation, nanoparticles, and water-soluble polymers have been used for the delivery of bioactive compounds to the site of action. Water-soluble polymeric conjugates and co-conjugates have remained the most outstanding delivery technique. This review will discuss the development of polymeric conjugates and co-conjugates of ferrocene in cancer research.
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Affiliation(s)
- Elie Hembe Mukaya
- Department of Applied Chemistry, University of Johannesburg, Doornfontein Campus P.O. Box 17011, Johannesburg, South Africa
| | - Xavier Yangkou Mbianda
- Department of Applied Chemistry, University of Johannesburg, Doornfontein Campus P.O. Box 17011, Johannesburg, South Africa
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10
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Abstract
For cancer, we develop a 2-D agent-based continuous-space game-theoretical model that considers cancer cells’ proximity to a blood vessel. Based on castrate resistant metastatic prostate cancer (mCRPC), the model considers the density and frequency (eco-evolutionary) dynamics of three cancer cell types: those that require exogenous testosterone ( T + ), those producing testosterone ( T P ), and those independent of testosterone ( T - ). We model proximity to a blood vessel by imagining four zones around the vessel. Zone 0 is the blood vessel. As rings, zones 1–3 are successively farther from the blood vessel and have successively lower carrying capacities. Zone 4 represents the space too far from the blood vessel and too poor in nutrients for cancer cell proliferation. Within the other three zones that are closer to the blood vessel, the cells’ proliferation probabilities are determined by zone-specific payoff matrices. We analyzed how zone width, dispersal, interactions across zone boundaries, and blood vessel dynamics influence the eco-evolutionary dynamics of cell types within zones and across the entire cancer cell population. At equilibrium, zone 3’s composition deviates from its evolutionary stable strategy (ESS) towards that of zone 2. Zone 2 sees deviations from its ESS because of dispersal from zones 1 and 3; however, its composition begins to resemble zone 1’s more so than zone 3’s. Frequency-dependent interactions between cells across zone boundaries have little effect on zone 2’s and zone 3’s composition but have decisive effects on zone 1. The composition of zone 1 diverges dramatically from both its own ESS, but also that of zone 2. That is because T + cells (highest frequency in zone 1) benefit from interacting with T P cells (highest frequency in zone 2). Zone 1 T + cells interacting with cells in zone 2 experience a higher likelihood of encountering a T P cell than when restricted to their own zone. As expected, increasing the width of zones decreases these impacts of cross-boundary dispersal and interactions. Increasing zone widths increases the persistence likelihood of the cancer subpopulation in the face of blood vessel dynamics, where the vessel may die or become occluded resulting in the “birth” of another blood vessel elsewhere in the space. With small zone widths, the cancer cell subpopulations cannot persist. With large zone widths, blood vessel dynamics create cancer cell subpopulations that resemble the ESS of zone 3 as the larger area of zone 3 and its contribution to cells within the necrotic zone 4 mean that zones 3 and 4 provide the likeliest colonizers for the new blood vessel. In conclusion, our model provides an alternative modeling approach for considering density-dependent, frequency-dependent, and dispersal dynamics into cancer models with spatial gradients around blood vessels. Additionally, our model can consider the occurrence of circulating tumor cells (cells that disperse into the blood vessel from zone 1) and the presence of live cancer cells within the necrotic regions of a tumor.
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11
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Xie H, Jiao Y, Fan Q, Hai M, Yang J, Hu Z, Yang Y, Shuai J, Chen G, Liu R, Liu L. Modeling three-dimensional invasive solid tumor growth in heterogeneous microenvironment under chemotherapy. PLoS One 2018; 13:e0206292. [PMID: 30365511 PMCID: PMC6203364 DOI: 10.1371/journal.pone.0206292] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Accepted: 10/10/2018] [Indexed: 01/08/2023] Open
Abstract
A systematic understanding of the evolution and growth dynamics of invasive solid tumors in response to different chemotherapy strategies is crucial for the development of individually optimized oncotherapy. Here, we develop a hybrid three-dimensional (3D) computational model that integrates pharmacokinetic model, continuum diffusion-reaction model and discrete cell automaton model to investigate 3D invasive solid tumor growth in heterogeneous microenvironment under chemotherapy. Specifically, we consider the effects of heterogeneous environment on drug diffusion, tumor growth, invasion and the drug-tumor interaction on individual cell level. We employ the hybrid model to investigate the evolution and growth dynamics of avascular invasive solid tumors under different chemotherapy strategies. Our simulations indicate that constant dosing is generally more effective in suppressing primary tumor growth than periodic dosing, due to the resulting continuous high drug concentration. In highly heterogeneous microenvironment, the malignancy of the tumor is significantly enhanced, leading to inefficiency of chemotherapies. The effects of geometrically-confined microenvironment and non-uniform drug dosing are also investigated. Our computational model, when supplemented with sufficient clinical data, could eventually lead to the development of efficient in silico tools for prognosis and treatment strategy optimization.
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Affiliation(s)
- Hang Xie
- College of Physics, Chongqing University, Chongqing, China
| | - Yang Jiao
- Materials Science and Engineering, Arizona State University, Tempe, AZ, United States of America
| | - Qihui Fan
- Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Science, Beijing, China
| | - Miaomiao Hai
- College of Physics, Chongqing University, Chongqing, China
| | - Jiaen Yang
- College of Physics, Chongqing University, Chongqing, China
| | - Zhijian Hu
- College of Physics, Chongqing University, Chongqing, China
| | - Yue Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University School of Oncology, Beijing Cancer Hospital and Institute, Haidian District, Beijing, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen, China
| | - Guo Chen
- College of Physics, Chongqing University, Chongqing, China
| | - Ruchuan Liu
- College of Physics, Chongqing University, Chongqing, China
| | - Liyu Liu
- College of Physics, Chongqing University, Chongqing, China
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12
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Fan B, Shen C, Wu M, Zhao J, Guo Q, Luo Y. miR-17-92 cluster is connected with disease progression and oxaliplatin/capecitabine chemotherapy efficacy in advanced gastric cancer patients: A preliminary study. Medicine (Baltimore) 2018; 97:e12007. [PMID: 30170406 PMCID: PMC6392796 DOI: 10.1097/md.0000000000012007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 07/30/2018] [Indexed: 12/22/2022] Open
Abstract
This study aimed to determine the role of plasma miR-17-92 cluster level in predicting chemoresistance in patients with gastric cancer (GC) undergoing oxaliplatin/capecitabine (XELOX) chemotherapy.Patients recently diagnosed with advanced GC were chosen as participants based on the inclusion criteria. The plasma levels of miR-17-5p, miR-18a, miR-19a/b, miR-20a, and miR-92-1 (miR-17-92 cluster) were determined through quantitative RT-PCR of blood samples from GC patients and healthy volunteers. All the patients received XELOX chemotherapy, and the effectiveness of the chemotherapy was evaluated.The miR-17-92 plasma level was increased in advanced GC patients and decreased after XELOX chemotherapy. Moreover, the miR-17-92 cluster level was associated with chemotherapy response but not with chemotherapy-related toxicity. The miR-17-92 cluster plasma level was decreased in chemosensitive patients, but not in chemoresistant patients, after chemotherapy. The sensitivity and specificity of the combined detection of the miR-17-92 cluster in patients with advanced GC were 100% each.The results suggest that the miR-17-92 plasma level is associated with the progression of advanced GC and effectiveness of XELOX chemotherapy.
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Jarrett AM, Hormuth DA, Barnes SL, Feng X, Huang W, Yankeelov TE. Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results. Phys Med Biol 2018; 63:105015. [PMID: 29697054 PMCID: PMC5985823 DOI: 10.1088/1361-6560/aac040] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used-obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety-Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p < 0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and represents a step toward the goal of achieving individualized prediction of tumor response to therapy.
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Affiliation(s)
- Angela M. Jarrett
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - David A. Hormuth
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Stephane L. Barnes
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Xinzeng Feng
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
| | - Wei Huang
- Advanced Imaging Research Center Oregon Health and Science University Portland, Oregon USA
| | - Thomas E. Yankeelov
- Institute for Computational Engineering and Sciences, The University of Texas at Austin Austin, Texas USA
- Livestrong Cancer Institutes, The University of Texas at Austin Austin, Texas USA
- Department of Biomedical Engineering, The University of Texas at Austin Austin, Texas USA
- Department of Oncology, The University of Texas at Austin Austin, Texas USA
- Department of Diagnostic Medicine, The University of Texas at Austin Austin, Texas USA
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14
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Gallaher JA, Enriquez-Navas PM, Luddy KA, Gatenby RA, Anderson ARA. Spatial Heterogeneity and Evolutionary Dynamics Modulate Time to Recurrence in Continuous and Adaptive Cancer Therapies. Cancer Res 2018; 78:2127-2139. [PMID: 29382708 PMCID: PMC5899666 DOI: 10.1158/0008-5472.can-17-2649] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 12/05/2017] [Accepted: 01/24/2018] [Indexed: 12/31/2022]
Abstract
Treatment of advanced cancers has benefited from new agents that supplement or bypass conventional therapies. However, even effective therapies fail as cancer cells deploy a wide range of resistance strategies. We propose that evolutionary dynamics ultimately determine survival and proliferation of resistant cells. Therefore, evolutionary strategies should be used with conventional therapies to delay or prevent resistance. Using an agent-based framework to model spatial competition among sensitive and resistant populations, we applied antiproliferative drug treatments to varying ratios of sensitive and resistant cells. We compared a continuous maximum-tolerated dose schedule with an adaptive schedule aimed at tumor control via competition between sensitive and resistant cells. Continuous treatment cured mostly sensitive tumors, but with any resistant cells, recurrence was inevitable. We identified two adaptive strategies that control heterogeneous tumors: dose modulation controls most tumors with less drug, while a more vacation-oriented schedule can control more invasive tumors. These findings offer potential modifications to treatment regimens that may improve outcomes and reduce resistance and recurrence.Significance: By using drug dose modulation or treatment vacations, adaptive therapy strategies control the emergence of tumor drug resistance by spatially suppressing less fit resistant populations in favor of treatment sensitive ones. Cancer Res; 78(8); 2127-39. ©2018 AACR.
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Affiliation(s)
- Jill A Gallaher
- H. Lee Moffitt Cancer Center, Integrated Mathematical Oncology, Tampa, Florida
| | | | - Kimberly A Luddy
- H. Lee Moffitt Cancer Center, Cancer Imaging and Metabolism, Tampa, Florida
| | - Robert A Gatenby
- H. Lee Moffitt Cancer Center, Integrated Mathematical Oncology, Tampa, Florida
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15
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Ledzewicz U, Schättler H. Application of mathematical models to metronomic chemotherapy: What can be inferred from minimal parameterized models? Cancer Lett 2017; 401:74-80. [PMID: 28323033 DOI: 10.1016/j.canlet.2017.03.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 03/10/2017] [Accepted: 03/10/2017] [Indexed: 01/11/2023]
Abstract
Metronomic chemotherapy refers to the frequent administration of chemotherapy at relatively low, minimally toxic doses without prolonged treatment interruptions. Different from conventional or maximum-tolerated-dose chemotherapy which aims at an eradication of all malignant cells, in a metronomic dosing the goal often lies in the long-term management of the disease when eradication proves elusive. Mathematical modeling and subsequent analysis (theoretical as well as numerical) have become an increasingly more valuable tool (in silico) both for determining conditions under which specific treatment strategies should be preferred and for numerically optimizing treatment regimens. While elaborate, computationally-driven patient specific schemes that would optimize the timing and drug dose levels are still a part of the future, such procedures may become instrumental in making chemotherapy effective in situations where it currently fails. Ideally, mathematical modeling and analysis will develop into an additional decision making tool in the complicated process that is the determination of efficient chemotherapy regimens. In this article, we review some of the results that have been obtained about metronomic chemotherapy from mathematical models and what they infer about the structure of optimal treatment regimens.
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Affiliation(s)
- Urszula Ledzewicz
- Dept. of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, IL, 62026-1653, USA; Institute of Mathematics, Lodz University of Technology, 90-924, Lodz, Poland.
| | - Heinz Schättler
- Dept. of Electrical and Systems Engineering Washington University, St. Louis, MO, 63130, USA.
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16
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Pérez-Velázquez J, Gevertz JL, Karolak A, Rejniak KA. Microenvironmental Niches and Sanctuaries: A Route to Acquired Resistance. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 936:149-164. [PMID: 27739047 DOI: 10.1007/978-3-319-42023-3_8] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
A tumor vasculature that is functionally abnormal results in irregular gradients of metabolites and drugs within the tumor tissue. Recently, significant efforts have been committed to experimentally examine how cellular response to anti-cancer treatments varies based on the environment in which the cells are grown. In vitro studies point to specific conditions in which tumor cells can remain dormant and survive the treatment. In vivo results suggest that cells can escape the effects of drug therapy in tissue regions that are poorly penetrated by the drugs. Better understanding how the tumor microenvironments influence the emergence of drug resistance in both primary and metastatic tumors may improve drug development and the design of more effective therapeutic protocols. This chapter presents a hybrid agent-based model of the growth of tumor micrometastases and explores how microenvironmental factors can contribute to the development of acquired resistance in response to a DNA damaging drug. The specific microenvironments of interest in this work are tumor hypoxic niches and tumor normoxic sanctuaries with poor drug penetration. We aim to quantify how spatial constraints of limited drug transport and quiescent cell survival contribute to the development of drug resistant tumors.
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Affiliation(s)
- Judith Pérez-Velázquez
- Mathematical Modeling of Biological Systems, Centre for Mathematical Science, Technical University of Munich, Garching, Germany.
| | - Jana L Gevertz
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA
| | - Aleksandra Karolak
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.,Department of Oncologic Sciences, College of Medicine, University of South Florida, Tampa, FL, USA
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