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Xu J, Smith L. Curating models from BioModels: Developing a workflow for creating OMEX files. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585236. [PMID: 38559029 PMCID: PMC10979985 DOI: 10.1101/2024.03.15.585236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium. Once reproduced we manually created a new set of files, with the model information stored by the Systems Biology Markup Language (SBML), and simulation instructions stored by the Simulation Experiment Description Markup Language (SED-ML), and everything included in an Open Modeling EXchange (OMEX) file, which could be used with a variety of simulators to reproduce the same results. On the one hand, the reproducibility procedure of 50 models developed a manual workflow that we would use to build an automatic platform to help users more easily curate and verify models in the future. On the other hand, these exercises allowed us to find the limitations and possible enhancement of the current curation and tooling to verify and curate models.
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Affiliation(s)
- Jin Xu
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Lucian Smith
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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2
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Costin IC, Marcu LG. Affinity of PET-MRI Tracers for Hypoxic Cells in Breast Cancer: A Systematic Review. Cells 2024; 13:1048. [PMID: 38920676 PMCID: PMC11202228 DOI: 10.3390/cells13121048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/04/2024] [Accepted: 06/14/2024] [Indexed: 06/27/2024] Open
Abstract
Tumour hypoxia is a known microenvironmental culprit for treatment resistance, tumour recurrence and promotion of metastatic spread. Despite the long-known existence of this factor within the tumour milieu, hypoxia is still one of the greatest challenges in cancer management. The transition from invasive and less reliable detection methods to more accurate and non-invasive ways to identify and quantify hypoxia was a long process that eventually led to the promising results showed by functional imaging techniques. Hybrid imaging, such as PET-CT, has the great advantage of combining the structural or anatomical image (offered by CT) with the functional or metabolic one (offered by PET). However, in the context of hypoxia, it is only the PET image taken after appropriate radiotracer administration that would supply hypoxia-specific information. To overcome this limitation, the development of the latest hybrid imaging systems, such as PET-MRI, enables a synergistic approach towards hypoxia imaging, with both methods having the potential to provide functional information on the tumour microenvironment. This study is designed as a systematic review of the literature on the newest developments of PET-MRI for the imaging of hypoxic cells in breast cancer. The analysis includes the affinity of various PET-MRI tracers for hypoxia in this patient group as well as the correlations between PET-specific and MRI-specific parameters, to offer a broader view on the potential for the widespread clinical implementation of this hybrid imaging technique.
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Affiliation(s)
- Ioana-Claudia Costin
- Faculty of Physics, West University of Timisoara, 300223 Timisoara, Romania;
- Bihor County Emergency Clinical Hospital, 410167 Oradea, Romania
| | - Loredana G. Marcu
- Faculty of Informatics & Science, University of Oradea, 410087 Oradea, Romania
- UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA 5001, Australia
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3
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Akman T, Arendt LM, Geisler J, Kristensen VN, Frigessi A, Köhn-Luque A. Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent. Bull Math Biol 2024; 86:42. [PMID: 38498130 PMCID: PMC11310292 DOI: 10.1007/s11538-023-01253-1] [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: 07/17/2023] [Accepted: 12/30/2023] [Indexed: 03/20/2024]
Abstract
Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.
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Affiliation(s)
- Tuğba Akman
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway.
- Department of Computer Engineering, University of Turkish Aeronautical Association, 06790, Etimesgut, Ankara, Turkey.
| | - Lisa M Arendt
- Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Jürgen Geisler
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Campus AHUS, Oslo, Norway
| | - Vessela N Kristensen
- Department of Medical Genetics, Institute of Clinical Medicine, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway.
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.
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4
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Song PN, Lynch SE, DeMellier CT, Mansur A, Gallegos CA, Wright BD, Hartman YE, Minton LE, Lapi SE, Warram JM, Sorace AG. Dual anti-HER2/EGFR inhibition synergistically increases therapeutic effects and alters tumor oxygenation in HNSCC. Sci Rep 2024; 14:3771. [PMID: 38355949 PMCID: PMC10866896 DOI: 10.1038/s41598-024-52897-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), and hypoxia are associated with radioresistance. The goal of this study is to study the synergy of anti-HER2, trastuzumab, and anti-EGFR, cetuximab, and characterize the tumor microenvironment components that may lead to increased radiation sensitivity with dual anti-HER2/EGFR therapy in head and neck squamous cell carcinoma (HNSCC). Positron emission tomography (PET) imaging ([89Zr]-panitumumab and [89Zr]-pertuzumab) was used to characterize EGFR and HER2 in HNSCC cell line tumors. HNSCC cells were treated with trastuzumab, cetuximab, or combination followed by radiation to assess for viability and radiosensitivity (colony forming assay, immunofluorescence, and flow cytometry). In vivo, [18F]-FMISO-PET imaging was used to quantify changes in oxygenation during treatment. Bliss Test of Synergy was used to identify combination treatment synergy. Quantifying EGFR and HER2 receptor expression revealed a 50% increase in heterogeneity of HER2 relative to EGFR. In vitro, dual trastuzumab-cetuximab therapy shows significant decreases in DNA damage response and increased response to radiation therapy (p < 0.05). In vivo, tumors treated with dual anti-HER2/EGFR demonstrated decreased tumor hypoxia, when compared to single agent therapies. Dual trastuzumab-cetuximab demonstrates synergy and can affect tumor oxygenation in HNSCC. Combination trastuzumab-cetuximab modulates the tumor microenvironment through reductions in tumor hypoxia and induces sustained treatment synergy.
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Affiliation(s)
- Patrick N Song
- Department of Radiology, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA
- Graduate Biomedical Sciences, The University of Alabama at Birmingham, Birmingham, USA
| | - Shannon E Lynch
- Department of Radiology, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA
- Graduate Biomedical Sciences, The University of Alabama at Birmingham, Birmingham, USA
| | - Chloe T DeMellier
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, USA
| | - Ameer Mansur
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, USA
| | - Carlos A Gallegos
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, USA
| | - Brian D Wright
- Department of Radiology, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA
| | - Yolanda E Hartman
- Department of Otolaryngology, The University of Alabama at Birmingham, Birmingham, USA
| | - Laura E Minton
- Department of Otolaryngology, The University of Alabama at Birmingham, Birmingham, USA
| | - Suzanne E Lapi
- Department of Radiology, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA
- O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, USA
| | - Jason M Warram
- Department of Radiology, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, USA
- Department of Otolaryngology, The University of Alabama at Birmingham, Birmingham, USA
- O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, USA
| | - Anna G Sorace
- Department of Radiology, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA.
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, USA.
- O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, USA.
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5
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Alnahdi AS, Idrees M. Nonlinear dynamics of estrogen receptor-positive breast cancer integrating experimental data: A novel spatial modeling approach. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21163-21185. [PMID: 38124592 DOI: 10.3934/mbe.2023936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Oncology research has focused extensively on estrogen hormones and their function in breast cancer proliferation. Mathematical modeling is essential for the analysis and simulation of breast cancers. This research presents a novel approach to examine the therapeutic and inhibitory effects of hormone and estrogen therapies on the onset of breast cancer. Our proposed mathematical model comprises a nonlinear coupled system of partial differential equations, capturing intricate interactions among estrogen, cytotoxic T lymphocytes, dormant cancer cells, and active cancer cells. The model's parameters are meticulously estimated through experimental studies, and we conduct a comprehensive global sensitivity analysis to assess the uncertainty of these parameter values. Remarkably, our findings underscore the pivotal role of hormone therapy in curtailing breast tumor growth by blocking estrogen's influence on cancer cells. Beyond this crucial insight, our proposed model offers an integrated framework to delve into the complexity of tumor progression and immune response under hormone therapy. We employ diverse experimental datasets encompassing gene expression profiles, spatial tumor morphology, and cellular interactions. Integrating multidimensional experimental data with mathematical models enhances our understanding of breast cancer dynamics and paves the way for personalized treatment strategies. Our study advances our comprehension of estrogen receptor-positive breast cancer and exemplifies a transformative approach that merges experimental data with cutting-edge mathematical modeling. This framework promises to illuminate the complexities of cancer progression and therapy, with broad implications for oncology.
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Affiliation(s)
- Abeer S Alnahdi
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Muhammad Idrees
- Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan
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Hu X, Ma Z, Xu B, Li S, Yao Z, Liang B, Wang J, Liao W, Lin L, Wang C, Zheng S, Wu Q, Huang Q, Yu L, Wang F, Shi M. Glutamine metabolic microenvironment drives M2 macrophage polarization to mediate trastuzumab resistance in HER2-positive gastric cancer. Cancer Commun (Lond) 2023; 43:909-937. [PMID: 37434399 PMCID: PMC10397568 DOI: 10.1002/cac2.12459] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/04/2023] [Accepted: 06/21/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND Trastuzumab is a first-line targeted therapy for human epidermal growth factor receptor-2 (HER2)-positive gastric cancer. However, the inevitable occurrence of acquired trastuzumab resistance limits the drug benefit, and there is currently no effective reversal measure. Existing researches on the mechanism of trastuzumab resistance mainly focused on tumor cells themselves, while the understanding of the mechanisms of environment-mediated drug resistance is relatively lacking. This study aimed to further explore the mechanisms of trastuzumab resistance to identify strategies to promote survival in these patients. METHODS Trastuzumab-sensitive and trastuzumab-resistant HER2-positive tumor tissues and cells were collected for transcriptome sequencing. Bioinformatics were used to analyze cell subtypes, metabolic pathways, and molecular signaling pathways. Changes in microenvironmental indicators (such as macrophage, angiogenesis, and metabolism) were verified by immunofluorescence (IF) and immunohistochemical (IHC) analyses. Finally, a multi-scale agent-based model (ABM) was constructed. The effects of combination treatment were further validated in nude mice to verify these effects predicted by the ABM. RESULTS Based on transcriptome sequencing, molecular biology, and in vivo experiments, we found that the level of glutamine metabolism in trastuzumab-resistant HER2-positive cells was increased, and glutaminase 1 (GLS1) was significantly overexpressed. Meanwhile, tumor-derived GLS1 microvesicles drove M2 macrophage polarization. Furthermore, angiogenesis promoted trastuzumab resistance. IHC showed high glutamine metabolism, M2 macrophage polarization, and angiogenesis in trastuzumab-resistant HER2-positive tumor tissues from patients and nude mice. Mechanistically, the cell division cycle 42 (CDC42) promoted GLS1 expression in tumor cells by activating nuclear factor kappa-B (NF-κB) p65 and drove GLS1 microvesicle secretion through IQ motif-containing GTPase-activating protein 1 (IQGAP1). Based on the ABM and in vivo experiments, we confirmed that the combination of anti-glutamine metabolism, anti-angiogenesis, and pro-M1 polarization therapy had the best effect in reversing trastuzumab resistance in HER2-positive gastric cancer. CONCLUSIONS This study revealed that tumor cells secrete GLS1 microvesicles via CDC42 to promote glutamine metabolism, M2 macrophage polarization, and pro-angiogenic function of macrophages, leading to acquired trastuzumab resistance in HER2-positive gastric cancer. A combination of anti-glutamine metabolism, anti-angiogenesis, and pro-M1 polarization therapy may provide a new insight into reversing trastuzumab resistance.
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Affiliation(s)
- Xingbin Hu
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Zhenfeng Ma
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Beibei Xu
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Shulong Li
- School of Biomedical EngineeringSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Zhiqi Yao
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Bishan Liang
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Jiao Wang
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Wangjun Liao
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Li Lin
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Chunling Wang
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Siting Zheng
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Qijing Wu
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Qiong Huang
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Le Yu
- School of Pharmaceutical SciencesSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Fenghua Wang
- Department of Medical OncologySun Yat‐sen University Cancer CenterState Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer MedicineGuangzhouGuangdongP. R. China
| | - Min Shi
- Department of OncologyNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
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7
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Davenport AA, Lu Y, Gallegos CA, Massicano AVF, Heinzman KA, Song PN, Sorace AG, Cogan NG. Mathematical Model of Triple-Negative Breast Cancer in Response to Combination Chemotherapies. Bull Math Biol 2022; 85:7. [PMID: 36542180 DOI: 10.1007/s11538-022-01108-1] [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: 04/27/2022] [Accepted: 11/14/2022] [Indexed: 12/24/2022]
Abstract
Triple-negative breast cancer (TNBC) is a heterogenous disease that is defined by its lack of targetable receptors, thus limiting treatment options and resulting in higher rates of metastasis and recurrence. Combination chemotherapy treatments, which inhibit tumor cell proliferation and regeneration, are a major component of standard-of-care treatment of TNBC. In this manuscript, we build a coupled ordinary differential equation model of TNBC with compartments that represent tumor proliferation, necrosis, apoptosis, and immune response to computationally describe the biological tumor affect to a combination of chemotherapies, doxorubicin (DRB) and paclitaxel (PTX). This model is parameterized using longitudinal [18F]-fluorothymidine positron emission tomography (FLT-PET) imaging data which allows for a noninvasive molecular imaging approach to quantify the tumor proliferation and tumor volume measurements for two murine models of TNBC. Animal models include a human cell line xenograft model, MDA-MB-231, and a syngeneic 4T1 mammary carcinoma model. The mathematical models are parameterized and the percent necrosis at the end time point is predicted and validated using histological hematoxylin and eosin (H&E) data. Global Sobol' sensitivity analysis is conducted to further understand the role each parameter plays in the model's goodness of fit to the data. In both the MDA-MB-231 and the 4T1 tumor models, the designed mathematical model can accurately describe both tumor volume changes and final necrosis volume. This can give insight into the ordering, dosing, and timing of DRB and PTX treatment. More importantly, this model can also give insight into future novel combinations of therapies and how the immune system plays a role in therapeutic response to TNBC, due to its calibration to two types of TNBC murine models.
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Affiliation(s)
- Angelica A Davenport
- Department of Mathematics, Florida State University, 1017 Academic Way, Tallahassee, FL, 32304, USA.
| | - Yun Lu
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Carlos A Gallegos
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Katherine A Heinzman
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Patrick N Song
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
| | - N G Cogan
- Department of Mathematics, Florida State University, 1017 Academic Way, Tallahassee, FL, 32304, USA
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Lima EA, Wyde RA, Sorace AG, Yankeelov TE. Optimizing combination therapy in a murine model of HER2+ breast cancer. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022; 402:115484. [PMID: 37800167 PMCID: PMC10552906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Human epidermal growth factor receptor 2 positive (HER2+) breast cancer is frequently treated with drugs that target the HER2 receptor, such as trastuzumab, in combination with chemotherapy, such as doxorubicin. However, an open problem in treatment design is to determine the therapeutic regimen that optimally combines these two treatments to yield optimal tumor control. Working with data quantifying temporal changes in tumor volume due to different trastuzumab and doxorubicin treatment protocols in a murine model of human HER2+ breast cancer, we propose a complete framework for model development, calibration, selection, and treatment optimization to find the optimal treatment protocol. Through different assumptions for the drug-tumor interactions, we propose ten different models to characterize the dynamic relationship between tumor volume and drug availability, as well as the drug-drug interaction. Using a Bayesian framework, each of these models are calibrated to the dataset and the model with the highest Bayesian information criterion weight is selected to represent the biological system. The selected model captures the inhibition of trastuzumab due to pre-treatment with doxorubicin, as well as the increase in doxorubicin efficacy due to pre-treatment with trastuzumab. We then apply optimal control theory (OCT) to this model to identify two optimal treatment protocols. In the first optimized protocol, we fix the maximum dosage for doxorubicin and trastuzumab to be the same as the maximum dose delivered experimentally, while trying to minimize tumor burden. Within this constraint, optimal control theory indicates the optimal regimen is to first deliver two doses of trastuzumab on days 35 and 36, followed by two doses of doxorubicin on days 37 and 38. This protocol predicts an additional 45% reduction in tumor burden compared to that achieved with the experimentally delivered regimen. In the second optimized protocol we fix the tumor control to be the same as that obtained experimentally, and attempt to reduce the doxorubicin dose. Within this constraint, the optimal regimen is the same as the first optimized protocol but uses only 43% of the doxorubicin dose used experimentally. This protocol predicts tumor control equivalent to that achieved experimentally. These results strongly suggest the utility of mathematical modeling and optimal control theory for identifying therapeutic regimens maximizing efficacy and minimizing toxicity.
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Affiliation(s)
- Ernesto A.B.F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
- Texas Advanced Computing Center, The University of Texas at Austin, United States of America
| | - Reid A.F. Wyde
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
| | - Anna G. Sorace
- Department of Radiology, The University of Alabama at Birmingham, United States of America
- Department of Biomedical Engineering, The University of Alabama at Birmingham, United States of America
- O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, United States of America
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, United States of America
- Department of Biomedical Engineering, The University of Texas at Austin, United States of America
- Department of Diagnostic Medicine, The University of Texas at Austin, United States of America
- Department of Oncology, The University of Texas at Austin, United States of America
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, United States of America
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9
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Wu C, Hormuth DA, Lorenzo G, Jarrett AM, Pineda F, Howard FM, Karczmar GS, Yankeelov TE. Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics. IEEE Trans Biomed Eng 2022; 69:3334-3344. [PMID: 35439121 PMCID: PMC9640301 DOI: 10.1109/tbme.2022.3168402] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers. METHODS Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity. RESULTS With an individually optimized dose (range = 12.11-15.11 mg/m2 per injection), a 3-week regimen consisting of a uniform daily injection significantly outperforms all other scheduling strategies (P < 0.001). In particular, the optimal protocol is predicted to significantly outperform the standard protocol (P < 0.001), improving the MSTD by an average factor of 9.93 (range = 6.63 to 14.17). CONCLUSION A clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis. SIGNIFICANCE Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, the University of Texas at Austin, Austin TX 78712 USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, and Livestrong Cancer Institutes, The University of Texas at Austin, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, the University of Texas at Austin; Department of Civil Engineering and Architecture, University of Pavia, Italy
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, and Livestrong Cancer Institutes, The University of Texas at Austin, USA
| | | | - Frederick M. Howard
- Section of Hematology/Oncology - Department of Medicine, The University of Chicago, USA
| | | | - Thomas E. Yankeelov
- Department of Biomedical Engineering, Department of Diagnostic Medicine, Department of Oncology, Oden Institute for Computational Engineering and Sciences, and Livestrong Cancer Institutes, The University of Texas at Austin; Department of Imaging Physics, MD Anderson Cancer Center, USA
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10
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Wu C, Jarrett AM, Zhou Z, Elshafeey N, Adrada BE, Candelaria RP, Mohamed RM, Boge M, Huo L, White JB, Tripathy D, Valero V, Litton JK, Yam C, Son JB, Ma J, Rauch GM, Yankeelov TE. MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Cancer Res 2022; 82:3394-3404. [PMID: 35914239 PMCID: PMC9481712 DOI: 10.1158/0008-5472.can-22-1329] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/14/2022] [Accepted: 07/26/2022] [Indexed: 02/07/2023]
Abstract
Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 patients with TNBC enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response. SIGNIFICANCE Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin. Austin, Texas 78712
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin. Austin, Texas 78712
- Livestrong Cancer Institutes, The University of Texas at Austin. Austin, Texas 78712
| | - Zijian Zhou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Nabil Elshafeey
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Beatriz E. Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Rosalind P. Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Rania M.M. Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Jason B. White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Jennifer K. Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Gaiane M. Rauch
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin. Austin, Texas 78712
- Livestrong Cancer Institutes, The University of Texas at Austin. Austin, Texas 78712
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
- Department of Biomedical Engineering, The University of Texas at Austin. Austin, Texas 78712
- Department of Diagnostic Medicine, The University of Texas at Austin. Austin, Texas 78712
- Department of Oncology, The University of Texas at Austin. Austin, Texas 78712
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11
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Wu C, Lorenzo G, Hormuth DA, Lima EABF, Slavkova KP, DiCarlo JC, Virostko J, Phillips CM, Patt D, Chung C, Yankeelov TE. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. BIOPHYSICS REVIEWS 2022; 3:021304. [PMID: 35602761 PMCID: PMC9119003 DOI: 10.1063/5.0086789] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/29/2022] [Indexed: 12/11/2022]
Abstract
Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | | | - Kalina P. Slavkova
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Debra Patt
- Texas Oncology, Austin, Texas 78731, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas 77030, USA
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12
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Kazerouni AS, Hormuth DA, Davis T, Bloom MJ, Mounho S, Rahman G, Virostko J, Yankeelov TE, Sorace AG. Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer. Cancers (Basel) 2022; 14:1837. [PMID: 35406609 PMCID: PMC8997932 DOI: 10.3390/cancers14071837] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/02/2022] [Accepted: 04/02/2022] [Indexed: 01/27/2023] Open
Abstract
This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two "tumor imaging phenotypes" (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.
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Affiliation(s)
- Anum S. Kazerouni
- Department of Radiology, The University of Washington, Seattle, WA 98104, USA;
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA;
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA;
| | - Tessa Davis
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; (T.D.); (M.J.B.); (S.M.); (G.R.)
| | - Meghan J. Bloom
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; (T.D.); (M.J.B.); (S.M.); (G.R.)
| | - Sarah Mounho
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; (T.D.); (M.J.B.); (S.M.); (G.R.)
| | - Gibraan Rahman
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; (T.D.); (M.J.B.); (S.M.); (G.R.)
| | - John Virostko
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA;
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; (T.D.); (M.J.B.); (S.M.); (G.R.)
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, TX 77030, USA
| | - Anna G. Sorace
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
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13
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Song PN, Mansur A, Lu Y, Della Manna D, Burns A, Samuel S, Heinzman K, Lapi SE, Yang ES, Sorace AG. Modulation of the Tumor Microenvironment with Trastuzumab Enables Radiosensitization in HER2+ Breast Cancer. Cancers (Basel) 2022; 14:cancers14041015. [PMID: 35205763 PMCID: PMC8869800 DOI: 10.3390/cancers14041015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/04/2022] [Accepted: 02/09/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Trastuzumab and radiation are used clinically to treat HER2-overexpressing breast cancers; however, the mechanistic synergy of anti-HER2 and radiation therapy has not been investigated. In this study, we identify that a subtherapeutic dose of trastuzumab sensitizes the tumor microenvironment to fractionated radiation. This results in longitudinal sustained response by triggering a state of innate immune activation through reduced DNA damage repair and increased tumor oxygenation. As positron emission tomography imaging can be used to longitudinally evaluate changes in tumor hypoxia, synergy of combination therapies is the result of both cellular and molecular changes in the tumor microenvironment. Abstract DNA damage repair and tumor hypoxia contribute to intratumoral cellular and molecular heterogeneity and affect radiation response. The goal of this study is to investigate anti-HER2-induced radiosensitization of the tumor microenvironment to enhance fractionated radiotherapy in models of HER2+ breast cancer. This is monitored through in vitro and in vivo studies of phosphorylated γ-H2AX, [18F]-fluoromisonidazole (FMISO)-PET, and transcriptomic analysis. In vitro, HER2+ breast cancer cell lines were treated with trastuzumab prior to radiation and DNA double-strand breaks (DSB) were quantified. In vivo, HER2+ human cell line or patient-derived xenograft models were treated with trastuzumab, fractionated radiation, or a combination and monitored longitudinally with [18F]-FMISO-PET. In vitro DSB analysis revealed that trastuzumab administered prior to fractionated radiation increased DSB. In vivo, trastuzumab prior to fractionated radiation significantly reduced hypoxia, as detected through decreased [18F]-FMISO SUV, synergistically improving long-term tumor response. Significant changes in IL-2, IFN-gamma, and THBS-4 were observed in combination-treated tumors. Trastuzumab prior to fractionated radiation synergistically increases radiotherapy in vitro and in vivo in HER2+ breast cancer which is independent of anti-HER2 response alone. Modulation of the tumor microenvironment, through increased tumor oxygenation and decreased DNA damage response, can be translated to other cancers with first-line radiation therapy.
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Affiliation(s)
- Patrick N. Song
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (P.N.S.); (Y.L.); (S.S.); (S.E.L.)
- Graduate Biomedical Sciences, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ameer Mansur
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (A.M.); (A.B.); (K.H.)
| | - Yun Lu
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (P.N.S.); (Y.L.); (S.S.); (S.E.L.)
- Graduate Biomedical Sciences, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Deborah Della Manna
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (D.D.M.); (E.S.Y.)
| | - Andrew Burns
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (A.M.); (A.B.); (K.H.)
| | - Sharon Samuel
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (P.N.S.); (Y.L.); (S.S.); (S.E.L.)
| | - Katherine Heinzman
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (A.M.); (A.B.); (K.H.)
| | - Suzanne E. Lapi
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (P.N.S.); (Y.L.); (S.S.); (S.E.L.)
- O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Eddy S. Yang
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (D.D.M.); (E.S.Y.)
- O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Anna G. Sorace
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (P.N.S.); (Y.L.); (S.S.); (S.E.L.)
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; (A.M.); (A.B.); (K.H.)
- O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Correspondence:
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14
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Mohammad Mirzaei N, Su S, Sofia D, Hegarty M, Abdel-Rahman MH, Asadpoure A, Cebulla CM, Chang YH, Hao W, Jackson PR, Lee AV, Stover DG, Tatarova Z, Zervantonakis IK, Shahriyari L. A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration. J Pers Med 2021; 11:jpm11101031. [PMID: 34683171 PMCID: PMC8540934 DOI: 10.3390/jpm11101031] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023] Open
Abstract
Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.
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Affiliation(s)
- Navid Mohammad Mirzaei
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
| | - Dilruba Sofia
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
| | - Maura Hegarty
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
| | - Mohamed H. Abdel-Rahman
- Department of Ophthalmology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; (M.H.A.-R.); (C.M.C.); (D.G.S.)
| | - Alireza Asadpoure
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, MA 02747, USA;
| | - Colleen M. Cebulla
- Department of Ophthalmology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; (M.H.A.-R.); (C.M.C.); (D.G.S.)
| | - Young Hwan Chang
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, OR 97239, USA; (Y.H.C.); (Z.T.)
| | - Wenrui Hao
- Department of Mathematics, The Pennsylvania State University, University Park, PA 16802, USA;
| | - Pamela R. Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, Phoenix, AZ 85054, USA;
| | - Adrian V. Lee
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA 15219, USA;
| | - Daniel G. Stover
- Department of Ophthalmology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; (M.H.A.-R.); (C.M.C.); (D.G.S.)
| | - Zuzana Tatarova
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, OR 97239, USA; (Y.H.C.); (Z.T.)
| | - Ioannis K. Zervantonakis
- Department of Bioengineering, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15219, USA;
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
- Correspondence:
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15
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Idrees M, Sohail A. Bio-algorithms for the modeling and simulation of cancer cells and the immune response. BIO-ALGORITHMS AND MED-SYSTEMS 2021. [DOI: 10.1515/bams-2020-0054] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Abstract
There have been significant developments in clinical, experimental, and theoretical approaches to understand the biomechanics of tumor cells and immune cells. Cytotoxic T lymphocytes (CTLs) are regarded as a major antitumor mechanism of immune cells. Mathematical modeling of tumor growth is an important and useful tool to observe and understand clinical phenomena analytically. This work develops a novel two-variable mathematical model to describe the interaction of tumor cells and CTLs. The designed model is providing an integrated framework to investigate the complexity of tumor progression and answer clinical questions that cannot always be reached with experimental tools. The parameters of the model are estimated from experimental study and stability analysis of the model is performed through nullclines. A global sensitivity analysis is also performed to check the uncertainty of the parameters. The results of numerical simulations of the model support the importance of the CTLs and demonstrate that CTLs can eliminate small tumors. The proposed model provides efficacious information to study and demonstrate the complex dynamics of breast cancer.
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Affiliation(s)
- Muhammad Idrees
- Department of Mathematics , COMSATS University Islamabad , Lahore , Pakistan
| | - Ayesha Sohail
- Department of Mathematics , COMSATS University Islamabad , Lahore , Pakistan
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16
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Kazerouni AS, Gadde M, Gardner A, Hormuth DA, Jarrett AM, Johnson KE, Lima EAF, Lorenzo G, Phillips C, Brock A, Yankeelov TE. Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology. iScience 2020; 23:101807. [PMID: 33299976 PMCID: PMC7704401 DOI: 10.1016/j.isci.2020.101807] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.
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Affiliation(s)
- Anum S. Kazerouni
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Manasa Gadde
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
| | - Andrea Gardner
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kaitlyn E. Johnson
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ernesto A.B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78712, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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17
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Johnson KE, Howard GR, Morgan D, Brenner EA, Gardner AL, Durrett RE, Mo W, Al’Khafaji A, Sontag ED, Jarrett AM, Yankeelov TE, Brock A. Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer. Phys Biol 2020; 18:016001. [PMID: 33215611 PMCID: PMC8156495 DOI: 10.1088/1478-3975/abb09c] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.
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Affiliation(s)
- Kaitlyn E Johnson
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
| | - Grant R Howard
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
| | - Daylin Morgan
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
| | - Eric A Brenner
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
- Institute for Cellular and Molecular Biology, The
University of Texas at Austin, Austin, TX, 78712, United States of America
| | - Andrea L Gardner
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
| | - Russell E Durrett
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
- Institute for Cellular and Molecular Biology, The
University of Texas at Austin, Austin, TX, 78712, United States of America
| | - William Mo
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
| | - Aziz Al’Khafaji
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
- Institute for Cellular and Molecular Biology, The
University of Texas at Austin, Austin, TX, 78712, United States of America
| | - Eduardo D Sontag
- Department of Electrical and Computer Engineering,
Northeastern University, Boston, MA, 02115, United States of America
- Department of Bioengineering, Northeastern University,
Boston, MA, 02115, United States of America
- Laboratory of Systems Pharmacology, Program in Therapeutics
Science, Harvard Medical School, Boston, MA, 02115, United States of America
| | - Angela M Jarrett
- Livestrong Cancer Institutes, Dell Medical School, The
University of Texas at Austin, Austin, TX, 78712, United States of America
- Oden Institute for Computational Engineering and Sciences,
The University of Texas at Austin
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
- Livestrong Cancer Institutes, Dell Medical School, The
University of Texas at Austin, Austin, TX, 78712, United States of America
- Oden Institute for Computational Engineering and Sciences,
The University of Texas at Austin
- Department of Diagnostic Medicine, The University of Texas
at Austin, Austin, TX, 78712, United States of America
- Department of Oncology, The University of Texas at Austin,
Austin, TX, 78712, United States of America
- Department of Imaging Physics, The MD Anderson Cancer
Center Houston, TX, 77030, United States of America
| | - Amy Brock
- Department of Biomedical Engineering, The University of
Texas at Austin, Austin, TX, 78712, United States of America
- Institute for Cellular and Molecular Biology, The
University of Texas at Austin, Austin, TX, 78712, United States of America
- Livestrong Cancer Institutes, Dell Medical School, The
University of Texas at Austin, Austin, TX, 78712, United States of America
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18
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Song PN, Mansur A, Dugger KJ, Davis TR, Howard G, Yankeelov TE, Sorace AG. CD4 T-cell immune stimulation of HER2 + breast cancer cells alters response to trastuzumab in vitro. Cancer Cell Int 2020; 20:544. [PMID: 33292267 PMCID: PMC7654187 DOI: 10.1186/s12935-020-01625-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/26/2020] [Indexed: 12/20/2022] Open
Abstract
Introduction The HER2 + tumor immune microenvironment is composed of macrophages, natural killer cells, and tumor infiltrating lymphocytes, which produce pro-inflammatory cytokines. Determining the effect of T-cells on HER2 + cancer cells during therapy could guide immunogenic therapies that trigger antibody-dependent cellular cytotoxicity. This study utilized longitudinal in vitro time-resolved microscopy to measure T-cell influence on trastuzumab in HER2 + breast cancer. Methods Fluorescently-labeled breast cancer cells (BT474, SKBR3, MDA-MB-453, and MDA-MB-231) were co-cultured with CD4 + T-cells (Jurkat cell line) and longitudinally imaged to quantify cancer cell viability when treated with or without trastuzumab (10, 25, 50 and 100 μg/mL). The presence and timing of T-cell co-culturing was manipulated to determine immune stimulation of trastuzumab-treated HER2 + breast cancer. HER2 and TNF-α expression were evaluated with western blot and ELISA, respectively. Significance was calculated using a two-tailed parametric t-test. Results The viability of HER2 + cancer cells significantly decreased when exposed to 25 μg/mL trastuzumab and T-cells, compared to cancer cells exposed to trastuzumab without T-cells (p = 0.01). The presence of T-cells significantly increased TNF-α expression in trastuzumab-treated cancer cells (p = 0.02). Conversely, cancer cells treated with TNF-α and trastuzumab had a similar decrease in viability as trastuzumab-treated cancer cells co-cultured with T-cells (p = 0.32). Conclusions The presence of T-cells significantly increases the efficacy of targeted therapies and suggests trastuzumab may trigger immune mediated cytotoxicity. Increased TNF-α receptor expression suggest cytokines may interact with trastuzumab to create a state of enhanced response to therapy in HER2 + breast cancer, which has potential to reducing tumor burden.
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Affiliation(s)
- Patrick N Song
- Department of Radiology, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA
| | - Ameer Mansur
- Department of Biomedical Engineering, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA
| | - Kari J Dugger
- Department of Clinical and Diagnostic Sciences, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Tessa R Davis
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Grant Howard
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.,Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA.,Department of Oncology, The University of Texas at Austin, Austin, TX, USA.,Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA.,Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA. .,Department of Biomedical Engineering, The University of Alabama at Birmingham, 1670 University Blvd, Birmingham, AL, 35233, USA. .,O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL, USA.
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19
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Bloom MJ, Jarrett AM, Triplett TA, Syed AK, Davis T, Yankeelov TE, Sorace AG. Anti-HER2 induced myeloid cell alterations correspond with increasing vascular maturation in a murine model of HER2+ breast cancer. BMC Cancer 2020; 20:359. [PMID: 32345237 PMCID: PMC7189470 DOI: 10.1186/s12885-020-06868-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 04/14/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Therapy targeted to the human epidermal growth factor receptor type 2 (HER2) is used in combination with cytotoxic therapy in treatment of HER2+ breast cancer. Trastuzumab, a monoclonal antibody that targets HER2, has been shown pre-clinically to induce vascular changes that can increase delivery of chemotherapy. To quantify the role of immune modulation in treatment-induced vascular changes, this study identifies temporal changes in myeloid cell infiltration with corresponding vascular alterations in a preclinical model of HER2+ breast cancer following trastuzumab treatment. METHODS HER2+ tumor-bearing mice (N = 46) were treated with trastuzumab or saline. After extraction, half of each tumor was analyzed by immunophenotyping using flow cytometry. The other half was quantified by immunohistochemistry to characterize macrophage infiltration (F4/80), vascularity (CD31 and α-SMA), proliferation (Ki67) and cellularity (H&E). Additional mice (N = 10) were used to quantify differences in tumor cytokines between control and treated groups. RESULTS Immunophenotyping showed an increase in macrophage infiltration 24 h after trastuzumab treatment (P ≤ 0.05). With continued trastuzumab treatment, the M1 macrophage population increased (P = 0.02). Increases in vessel maturation index (i.e., the ratio of α-SMA to CD31) positively correlated with increases in tumor infiltrating M1 macrophages (R = 0.33, P = 0.04). Decreases in VEGF-A and increases in inflammatory cytokines (TNF-α, IL-1β, CCL21, CCL7, and CXCL10) were observed with continued trastuzumab treatment (P ≤ 0.05). CONCLUSIONS Preliminary results from this study in a murine model of HER2+ breast cancer show correlations between immune modulation and vascular changes, and reveals the potential for anti-HER2 therapy to reprogram immunosuppressive components of the tumor microenvironment. The quantification of immune modulation in HER2+ breast cancer, as well as the mechanistic insight of vascular alterations after anti-HER2 treatment, represent novel contributions and warrant further assessment for potential clinical translation.
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Affiliation(s)
- Meghan J Bloom
- Department of Biomedical Engineering, The University of Texas, Austin, TX, USA
| | - Angela M Jarrett
- LiveSTRONG Cancer Institutes, The University of Texas, Austin, TX, USA
| | - Todd A Triplett
- LiveSTRONG Cancer Institutes, The University of Texas, Austin, TX, USA.,Department of Oncology, The University of Texas Dell Medical School, Austin, TX, USA
| | - Anum K Syed
- Department of Biomedical Engineering, The University of Texas, Austin, TX, USA
| | - Tessa Davis
- Department of Biomedical Engineering, The University of Texas, Austin, TX, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas, Austin, TX, USA.,LiveSTRONG Cancer Institutes, The University of Texas, Austin, TX, USA.,Department of Oncology, The University of Texas Dell Medical School, Austin, TX, USA.,Diagnostic Medicine, The University of Texas, Austin, TX, USA.,Oden Institute for Computational and Engineering Sciences, The University of Texas, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, The University of Alabama, Birmingham, AL, USA. .,Department of Biomedical Engineering, The University of Alabama, Birmingham, AL, USA. .,O'Neal Comprehensive Cancer Center, The University of Alabama, Birmingham, AL, USA.
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20
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Padmanabhan R, Kheraldine HS, Meskin N, Vranic S, Al Moustafa AE. Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models. Cancers (Basel) 2020; 12:E636. [PMID: 32164163 PMCID: PMC7139939 DOI: 10.3390/cancers12030636] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 12/12/2022] Open
Abstract
Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2+) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2+ breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2+ breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2+ breast cancer management, we also review mathematical models pertaining to the dynamics of HER2+ breast cancer and immune checkpoint inhibitors.
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Affiliation(s)
- Regina Padmanabhan
- Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar;
- Biomedical Research Centre, Qatar University, 2713 Doha, Qatar;
| | - Hadeel Shafeeq Kheraldine
- Biomedical Research Centre, Qatar University, 2713 Doha, Qatar;
- College of Pharmacy, QU Health, Qatar University, 2713 Doha, Qatar
| | - Nader Meskin
- Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar;
| | - Semir Vranic
- College of Medicine, QU Health, Qatar University, 2713 Doha, Qatar;
| | - Ala-Eddin Al Moustafa
- Biomedical Research Centre, Qatar University, 2713 Doha, Qatar;
- College of Medicine, QU Health, Qatar University, 2713 Doha, Qatar;
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21
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Jarrett AM, Shah A, Bloom MJ, McKenna MT, Hormuth DA, Yankeelov TE, Sorace AG. Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer. Sci Rep 2019; 9:12830. [PMID: 31492947 PMCID: PMC6731321 DOI: 10.1038/s41598-019-49073-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 08/19/2019] [Indexed: 12/14/2022] Open
Abstract
The goal of this study is to experimentally and computationally investigate combination trastuzumab-paclitaxel therapies and identify potential synergistic effects due to sequencing of the therapies with in vitro imaging and mathematical modeling. Longitudinal alterations in cell confluence are reported for an in vitro model of BT474 HER2+ breast cancer cells following various dosages and timings of paclitaxel and trastuzumab combination regimens. Results of combination drug regimens are evaluated for drug interaction relationships based on order, timing, and quantity of dose of the drugs. Altering the order of treatments, with the same total therapeutic dose, provided significant changes in overall cell confluence (p < 0.001). Two mathematical models are introduced that are constrained by the in vitro data to simulate the tumor cell response to the individual therapies. A collective model merging the two individual drug response models was designed to investigate the potential mechanisms of synergy for paclitaxel-trastuzumab combinations. This collective model shows increased synergy for regimens where trastuzumab is administered prior to paclitaxel and suggests trastuzumab accelerates the cytotoxic effects of paclitaxel. The synergy derived from the model is found to be in agreement with the combination index, where both indicate a spectrum of additive and synergistic interactions between the two drugs dependent on their dose order. The combined in vitro results and development of a mathematical model of drug synergy has potential to evaluate and improve standard-of-care combination therapies in cancer.
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Affiliation(s)
- Angela M Jarrett
- 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
| | - Alay Shah
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA
| | - Meghan J Bloom
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA
| | - Matthew T McKenna
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, 37232, USA
| | - David A Hormuth
- 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
| | - 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 Diagnostic Medicine, The University of Texas at Austin, Austin, Texas, USA.
- Department of Oncology, The University of Texas at Austin, Austin, Texas, USA.
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, 35209, USA.
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35209, USA.
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, 35209, USA.
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