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Lu MJ, Hao W, Hu B, Li S. Bifurcation analysis of a free boundary model of vascular tumor growth with a necrotic core and chemotaxis. J Math Biol 2023; 86:19. [PMID: 36609586 DOI: 10.1007/s00285-022-01862-9] [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: 06/01/2022] [Revised: 12/13/2022] [Accepted: 12/27/2022] [Indexed: 01/09/2023]
Abstract
A considerable number of research works has been devoted to the study of tumor models. Several biophysical factors, such as cell proliferation, apoptosis, chemotaxis, angiogenesis and necrosis, have been discovered to have an impact on the complicated biological system of tumors. An indicator of the aggressiveness of tumor development is the instability of the shape of the tumor boundary. Complex patterns of tumor morphology have been explored in Lu et al. (J Comput Phys 459:111153, 2022). In this paper, we continue to carry out a bifurcation analysis on such a vascular tumor model with a controlled necrotic core and chemotaxis. This bifurcation analysis, to the parameter of cell proliferation, is built on the explicit formulas of radially symmetric steady-state solutions. By perturbing the tumor free boundary and establishing rigorous estimates of the free boundary system, we prove the existence of the bifurcation branches with Crandall-Rabinowitz theorem. The parameter of chemotaxis is found to influence the monotonicity of the bifurcation point as the mode l increases both theoretically and numerically.
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Affiliation(s)
- Min-Jhe Lu
- Department of Mathematics, University of California at Irvine, Irvine, CA, 92617, USA
| | - Wenrui Hao
- Department of Mathematics, Pennsylvania State University, University Park, PA, 16802, USA.
| | - Bei Hu
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Shuwang Li
- Department of Applied Mathematics, Illinois Institute of Technology, Chicago, IL, 60616, USA
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2
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Hu Y, Mohammad Mirzaei N, Shahriyari L. Bio-Mechanical Model of Osteosarcoma Tumor Microenvironment: A Porous Media Approach. Cancers (Basel) 2022; 14:cancers14246143. [PMID: 36551627 PMCID: PMC9777270 DOI: 10.3390/cancers14246143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/05/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Osteosarcoma is the most common malignant bone tumor in children and adolescents with a poor prognosis. To describe the progression of osteosarcoma, we expanded a system of data-driven ODE from a previous study into a system of Reaction-Diffusion-Advection (RDA) equations and coupled it with Biot equations of poroelasticity to form a bio-mechanical model. The RDA system includes the spatio-temporal information of the key components of the tumor microenvironment. The Biot equations are comprised of an equation for the solid phase, which governs the movement of the solid tumor, and an equation for the fluid phase, which relates to the motion of cells. The model predicts the total number of cells and cytokines of the tumor microenvironment and simulates the tumor's size growth. We simulated different scenarios using this model to investigate the impact of several biomedical settings on tumors' growth. The results indicate the importance of macrophages in tumors' growth. Particularly, we have observed a high co-localization of macrophages and cancer cells, and the concentration of tumor cells increases as the number of macrophages increases.
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3
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Mohammad Mirzaei N, Tatarova Z, Hao W, Changizi N, Asadpoure A, Zervantonakis IK, Hu Y, Chang YH, Shahriyari L. A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice. J Pers Med 2022; 12:807. [PMID: 35629230 PMCID: PMC9145520 DOI: 10.3390/jpm12050807] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 02/04/2023] Open
Abstract
The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells' location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells' diffusion and immune cells' influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes' diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer.
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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.); (Y.H.)
| | - Zuzana Tatarova
- Department of Radiology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Wenrui Hao
- Department of Mathematics, The Pennsylvania State University, University Park, PA 16802, USA;
| | - Navid Changizi
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, MA 02747, USA; (N.C.); (A.A.)
| | - Alireza Asadpoure
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, MA 02747, USA; (N.C.); (A.A.)
| | - Ioannis K. Zervantonakis
- Department of Bioengineering, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15219, USA;
| | - Yu Hu
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (Y.H.)
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (Y.H.)
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Mohammad Mirzaei N, Changizi N, Asadpoure A, Su S, Sofia D, Tatarova Z, Zervantonakis IK, Chang YH, Shahriyari L. Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model. PLoS Comput Biol 2022; 18:e1009953. [PMID: 35294447 PMCID: PMC8959189 DOI: 10.1371/journal.pcbi.1009953] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/28/2022] [Accepted: 02/22/2022] [Indexed: 02/07/2023] Open
Abstract
The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model's parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.
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Affiliation(s)
- Navid Mohammad Mirzaei
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Navid Changizi
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, Massachusetts, United States of America
| | - Alireza Asadpoure
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, Massachusetts, United States of America
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Dilruba Sofia
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Zuzana Tatarova
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon, United States of America
| | - Ioannis K. Zervantonakis
- Department of Bioengineering, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Young Hwan Chang
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon, United States of America
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
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Fu X, Yang Y, Zhang D. Molecular mechanism of albumin in suppressing invasion and metastasis of hepatocellular carcinoma. Liver Int 2022; 42:696-709. [PMID: 34854209 PMCID: PMC9299813 DOI: 10.1111/liv.15115] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 11/22/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND & AIMS Worldwide, hepatocellular carcinoma (HCC) is one of the most common causes of death in people. Albumin (ALB) is considered as an important indicator for HCC prognosis, and evidence has shown HCC cell growth can be regulated by ALB. However, the role of ALB in hepatocarcinogenesis and the mechanism of action is still unknown. METHODS The expression of ALB was determined by clinical profiles, immunohistochemistry, and western blot. Wound healing and Transwell assays were conducted to evaluate the effects of ALB during migration and invasion in HCC. We used mass spectrometry coupled isobaric tags for relative and absolute quantitation (iTRAQ)-technology to identify secretory differentially expressed proteins (DEPs) in ALB knockdown HepG2 cells. Western blot, reverse transcription-quantitative polymerase chain reaction and enzyme-linked immunosorbent assay techniques were used for verification. RESULTS We suggested that ALB was associated with aggressive metastasis and depleting ALB significantly promoted invasion and migration of HCC. A total of 210 DEPs were identified after silencing of ALB. We observed that a negative correlation between ALB and urokinase plasminogen activator surface receptor (uPAR) expression levels. CONCLUSIONS ALB acts as a tumour suppressor and plays a key role in HCC progression, particularly in invasion and metastasis. Suppression of ALB promoted migration and invasion of HCC cells by increasing uPAR, matrix metalloproteinase (MMP2), and MMP9.
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Affiliation(s)
- Xiao Fu
- Department of Infectious Diseases, Key Laboratory of Molecular Biology for Infectious Diseases, Institute for Viral Hepatitis, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Yixuan Yang
- Department of Infectious Diseases, Key Laboratory of Molecular Biology for Infectious Diseases, Institute for Viral Hepatitis, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Dazhi Zhang
- Department of Infectious Diseases, Key Laboratory of Molecular Biology for Infectious Diseases, Institute for Viral Hepatitis, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
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Lv T, Zhao Y, Jiang X, Yuan H, Wang H, Cui X, Xu J, Zhao J, Wang J. uPAR: An Essential Factor for Tumor Development. J Cancer 2021; 12:7026-7040. [PMID: 34729105 PMCID: PMC8558663 DOI: 10.7150/jca.62281] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/02/2021] [Indexed: 02/06/2023] Open
Abstract
Tumorigenesis is closely related to the loss of control of many genes. Urokinase-type plasminogen activator receptor (uPAR), a glycolipid-anchored protein on the cell surface, is controlled by many factors in tumorigenesis and is expressed in many tumor tissues. In this review, we summarize the regulatory effects of the uPAR signaling pathway on processes and factors related to tumor progression, such as tumor cell proliferation, adhesion, metastasis, glycolysis, tumor microenvironment and angiogenesis. Overall, the evidence accumulated to date suggests that uPAR induction by tumor progression may be one of the most important factors affecting therapeutic efficacy. An improved understanding of the interactions between uPAR and its coreceptors in cancer will provide critical biomolecular information that may help to better predict the disease course and response to therapy.
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Affiliation(s)
- Tao Lv
- College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan, China 655011.,Key Laboratory of Yunnan Province Universities of the Diversity and Ecological Adaptive Evolution for Animals and Plants on YunGui Plateau, Qujing Normal University, Qujing, China 655011
| | - Ying Zhao
- College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan, China 655011
| | - Xinni Jiang
- School of Biological Sciences and Technology, Chengdu Medical College, Chengdu, Sichuan, China 610500
| | - Hemei Yuan
- College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan, China 655011
| | - Haibo Wang
- College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan, China 655011.,Key Laboratory of Yunnan Province Universities of the Diversity and Ecological Adaptive Evolution for Animals and Plants on YunGui Plateau, Qujing Normal University, Qujing, China 655011
| | - Xuelin Cui
- College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan, China 655011
| | - Jiashun Xu
- College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan, China 655011
| | - Jingye Zhao
- College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan, China 655011
| | - Jianlin Wang
- College of Chemistry and Environmental Science, Qujing Normal University, Qujing, Yunnan, China 655011
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Le T, Su S, Kirshtein A, Shahriyari L. Data-Driven Mathematical Model of Osteosarcoma. Cancers (Basel) 2021; 13:cancers13102367. [PMID: 34068946 PMCID: PMC8156666 DOI: 10.3390/cancers13102367] [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: 04/16/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/22/2022] Open
Abstract
As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-γ, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.
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Affiliation(s)
- Trang Le
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (T.L.); (S.S.)
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (T.L.); (S.S.)
| | - Arkadz Kirshtein
- Department of Mathematics, Tufts University, Medford, MA 02155, USA;
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (T.L.); (S.S.)
- Correspondence:
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Kirshtein A, Akbarinejad S, Hao W, Le T, Su S, Aronow RA, Shahriyari L. Data Driven Mathematical Model of Colon Cancer Progression. J Clin Med 2020; 9:E3947. [PMID: 33291412 PMCID: PMC7762015 DOI: 10.3390/jcm9123947] [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: 10/27/2020] [Revised: 11/28/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients the size of the tumor is sensitive to the parameters related to macrophages, such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.
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Affiliation(s)
- Arkadz Kirshtein
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Shaya Akbarinejad
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Wenrui Hao
- Department of Mathematics, Pennsylvania State University, University Park, State College, PA 16802, USA;
| | - Trang Le
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Rachel A. Aronow
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
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Lu H, Shi C, Liu X, Liang C, Yang C, Wan X, Li L, Liu Y. Identification of ZG16B as a prognostic biomarker in breast cancer. Open Med (Wars) 2020; 16:1-13. [PMID: 33336077 PMCID: PMC7718615 DOI: 10.1515/med-2021-0004] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/01/2020] [Accepted: 10/14/2020] [Indexed: 02/06/2023] Open
Abstract
Zymogen granule protein 16B (ZG16B) has been identified in various cancers, while so far the association between ZG16B and breast cancer hasn’t been explored. Our aim is to confirm whether it can serve as a prognostic biomarker in breast cancer. In this study, Oncomine, Cancer Cell Line Encyclopedia (CCLE), Ualcan, and STRING database analyses were conducted to detect the expression level of ZG16B in breast cancer with different types. Kaplan–Meier plotter was used to analyze the prognosis of patients with high or low expression of ZG16B. We found that ZG16B was significantly upregulated in breast cancer. Moreover, ZG16B was closely associated with foregone biomarkers and crucial factors in breast cancer. In the survival analysis, high expression of ZG16B represents a favorable prognosis in patients. Our work demonstrates the latent capacity of ZG16B to be a biomarker for prognosis of breast cancer.
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Affiliation(s)
- Haotian Lu
- School of Basic Medicine, College of Medicine, Qingdao University, Qingdao, 266071, China
| | - Chunying Shi
- Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, College of Medicine, Qingdao University, Qingdao, 266071, China
| | - Xinyu Liu
- School of Basic Medicine, College of Medicine, Qingdao University, Qingdao, 266071, China
| | - Chen Liang
- School of Basic Medicine, College of Medicine, Qingdao University, Qingdao, 266071, China
| | - Chaochao Yang
- School of Basic Medicine, College of Medicine, Qingdao University, Qingdao, 266071, China
| | - Xueqi Wan
- School of Basic Medicine, College of Medicine, Qingdao University, Qingdao, 266071, China
| | - Ling Li
- Department of Human Anatomy, Histology and Embryology, School of Basic Medicine, College of Medicine, Qingdao University, Qingdao, 266071, China
| | - Ying Liu
- School of Basic Medicine, College of Medicine, Qingdao University, Qingdao, 266071, China.,Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, 266071, China
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Lai X, Friedman A. How to schedule VEGF and PD-1 inhibitors in combination cancer therapy? BMC SYSTEMS BIOLOGY 2019; 13:30. [PMID: 30894166 PMCID: PMC6427900 DOI: 10.1186/s12918-019-0706-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 02/19/2019] [Indexed: 11/23/2022]
Abstract
BACKGROUND One of the questions in the design of cancer clinical trials with combination of two drugs is in which order to administer the drugs. This is an important question, especially in the case where one agent may interfere with the effectiveness of the other agent. RESULTS In the present paper we develop a mathematical model to address this scheduling question in a specific case where one of the drugs is anti-VEGF, which is known to affect the perfusion of other drugs. As a second drug we take anti-PD-1. Both drugs are known to increase the activation of anticancer T cells. Our simulations show that in the case where anti-VEGF reduces the perfusion, a non-overlapping schedule is significantly more effective than a simultaneous injection of the two drugs, and it is somewhat more beneficial to inject anti-PD-1 first. CONCLUSION The method and results of the paper can be extended to other combinations, and they could play an important role in the design of clinical trials with combination therapy, where scheduling strategies may significantly affect the outcome.
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Affiliation(s)
- Xiulan Lai
- Institute for Mathematical Sciences, Renmin University of China, Beijing, People’s Republic of China
| | - Avner Friedman
- Mathematical Bioscience Institute & Department of Mathematics, Ohio State University, Columbus, OH USA
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11
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Mathematical modeling in scheduling cancer treatment with combination of VEGF inhibitor and chemotherapy drugs. J Theor Biol 2019; 462:490-498. [DOI: 10.1016/j.jtbi.2018.11.018] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 11/15/2018] [Accepted: 11/19/2018] [Indexed: 11/20/2022]
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12
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Lai X, Stiff A, Duggan M, Wesolowski R, Carson WE, Friedman A. Modeling combination therapy for breast cancer with BET and immune checkpoint inhibitors. Proc Natl Acad Sci U S A 2018; 115:5534-5539. [PMID: 29735668 PMCID: PMC6003484 DOI: 10.1073/pnas.1721559115] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
CTLA-4 is an immune checkpoint expressed on active anticancer T cells. When it combines with its ligand B7 on dendritic cells, it inhibits the activity of the T cells. The Bromo- and Extra-Terminal (BET) protein family includes proteins that regulate the expression of key oncogenes and antiapoptotic proteins. BET inhibitor (BETi) has been shown to reduce the expression of MYC by suppressing its transcription factors and to down-regulate the hypoxic transcriptome response to VEGF-A. This paper develops a mathematical model of the treatment of cancer by combination therapy of BETi and CTLA-4 inhibitor. The model shows that the two drugs are positively correlated in the sense that the tumor volume decreases as the dose of each of the drugs is increased. The model also considers the effect of the combined therapy on levels of myeloid-derived suppressor cells (MDSCs) and the overexpression of TNF-α, which may predict gastrointestinal side effects of the combination.
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Affiliation(s)
- Xiulan Lai
- Institute for Mathematical Sciences, Renmin University of China, 100872 Beijing, P. R. China
| | - Andrew Stiff
- Medical Scientist Training Program, The Ohio State University, Columbus, OH 43210
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210
| | - Megan Duggan
- Department of Surgery, The Ohio State University, Columbus, OH 43210
| | - Robert Wesolowski
- Stefanie Spielman Comprehensive Breast Center, Wexner Medical Center, The Ohio State University, Columbus, OH 43212
| | - William E Carson
- Division of Surgical Oncology, Department of Surgery, The Ohio State University, Columbus, OH 43210
- Division of Surgical Oncology, Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210
| | - Avner Friedman
- Mathematical Bioscience Institute, The Ohio State University, Columbus, OH 43210;
- Department of Mathematics, The Ohio State University, Columbus, OH 43210
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13
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Eftimie R, Hassanein E. Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach. J Transl Med 2018; 16:73. [PMID: 29554938 PMCID: PMC5859525 DOI: 10.1186/s12967-018-1432-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 03/02/2018] [Indexed: 01/12/2023] Open
Abstract
Background Early cancer diagnosis is one of the most important challenges of cancer research, since in many cancers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers. Methods Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection. Results We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solution diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolution of cancer biomarkers, thus allowing us to make predictions on cancer detection times. Conclusions Combining cancer and immune biomarkers could improve cancer detection times, and any predictions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.
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Affiliation(s)
- Raluca Eftimie
- Division of Mathematics, University of Dundee, Dundee, DD1 4HN, UK.
| | - Esraa Hassanein
- Biophysics Department, Faculty of Science, Cairo University, 12613, Giza, Egypt
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Miller-Kleinhenz J, Guo X, Qian W, Zhou H, Bozeman EN, Zhu L, Ji X, Wang YA, Styblo T, O'Regan R, Mao H, Yang L. Dual-targeting Wnt and uPA receptors using peptide conjugated ultra-small nanoparticle drug carriers inhibited cancer stem-cell phenotype in chemo-resistant breast cancer. Biomaterials 2017; 152:47-62. [PMID: 29107218 DOI: 10.1016/j.biomaterials.2017.10.035] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 10/15/2017] [Accepted: 10/18/2017] [Indexed: 12/27/2022]
Abstract
Heterogeneous tumor cells, high incidence of tumor recurrence, and decrease in overall survival are the major challenges for the treatment of chemo-resistant breast cancer. Results of our study showed differential chemotherapeutic responses among breast cancer patient derived xenograft (PDX) tumors established from the same patients. All doxorubicin (Dox)-resistant tumors expressed higher levels of cancer stem-like cell biomarkers, including CD44, Wnt and its receptor LRP5/6, relative to Dox-sensitive tumors. To effectively treat resistant tumors, we developed an ultra-small magnetic iron oxide nanoparticle (IONP) drug carrier conjugated with peptides that are dually targeted to Wnt/LRP5/6 and urokinase plasminogen activator receptor (uPAR). Our results showed that simultaneous binding to LRP5/6 and uPAR by the dual receptor targeted IONPs was required to inhibit breast cancer cell invasion. Molecular analysis revealed that the dual receptor targeted IONPs significantly inhibited Wnt/β-catenin signaling and cancer stem-like phenotype of tumor cells, with marked reduction of Wnt ligand, CD44 and uPAR. Systemic administration of the dual targeted IONPs led to nanoparticle-drug delivery into PDX tumors, resulting in stronger tumor growth inhibition compared to non-targeted or single-targeted IONP-Dox in a human breast cancer PDX model. Therefore, co-targeting Wnt/LRP and uPAR using IONP drug carriers is a promising therapeutic approach for effective drug delivery to chemo-resistant breast cancer.
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Affiliation(s)
- Jasmine Miller-Kleinhenz
- Winship Cancer Institute, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Xiangxue Guo
- Winship Cancer Institute, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Weiping Qian
- Winship Cancer Institute, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Hongyu Zhou
- Winship Cancer Institute, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Erica N Bozeman
- Winship Cancer Institute, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Lei Zhu
- Winship Cancer Institute, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Xin Ji
- Ocean Nanotech, San Diego, CA, USA
| | | | - Toncred Styblo
- Winship Cancer Institute, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Ruth O'Regan
- University of Wisconsin Carbone Cancer Center, Madison, WI, USA
| | - Hui Mao
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Lily Yang
- Winship Cancer Institute, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA.
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Targeting pediatric sarcoma with a bispecific ligand immunotoxin targeting urokinase and epidermal growth factor receptors. Oncotarget 2017; 9:11938-11947. [PMID: 29552283 PMCID: PMC5844719 DOI: 10.18632/oncotarget.21187] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 08/07/2017] [Indexed: 12/11/2022] Open
Abstract
Children with high risk sarcoma have a poor prognosis despite surgical resection, irradiation and chemotherapy. Alternative therapies are urgently needed. Urokinase-type plasminogen activator receptor (uPAR) and epidermal growth factor receptor (EGFR) are surface proteins expressed by some pediatric sarcomas. We show for the first time that a de-immunized bispecific ligand toxin, EGFATFKDEL, directed against EGFR and uPAR, successfully targets pediatric sarcoma. Using flow cytometry, we identified a rhabdomyosarcoma (RMS) cell line, RH30, that expresses both uPAR and EGFR, and a Ewing sarcoma (EWS) cell line, TC-71, that expresses only uPAR. We tested the differential sensitivity of these two sarcoma cell lines to toxin-induced killing, using both in vitro assays and an in vivo murine model. We show that pediatric sarcomas are highly sensitive to EGFATFKDEL (at subnanomolar concentrations) in vitro. In vivo, tumor growth was significantly attenuated after treatment with EGFTFKDEL, compared to untreated controls, in both RH30 and TC-71 tumor bearing mice. In addition, we found that simultaneously targeting both receptors in a dual positive cell line was more effective than targeting a single receptor or antigen, resulting in a greater tumor response, including complete tumor regression in an animal model of bulky disease. Our findings provide support for further exploration of bispecific targeting of pediatric sarcomas with bispecific ligand toxins, such as EGFATFKDEL.
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