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Tangella N, Cess CG, Ildefonso GV, Finley SD. Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions. APL Bioeng 2024; 8:036111. [PMID: 39175956 PMCID: PMC11341129 DOI: 10.1063/5.0205996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 08/21/2024] [Accepted: 07/25/2024] [Indexed: 08/24/2024] Open
Abstract
Interactions between cancer cells and immune cells in the tumor microenvironment influence tumor growth and can contribute to the response to cancer immunotherapies. It is difficult to gain mechanistic insights into the effects of cell-cell interactions in tumors using a purely experimental approach. However, computational modeling enables quantitative investigation of the tumor microenvironment, and agent-based modeling, in particular, provides relevant biological insights into the spatial and temporal evolution of tumors. Here, we develop a novel agent-based model (ABM) to predict the consequences of intercellular interactions. Furthermore, we leverage our prior work that predicts the transitions of CD8+ T cells from a naïve state to a terminally differentiated state using Boolean modeling. Given the details incorporated to predict T cell state, we apply the integrated Boolean-ABM framework to study how the properties of CD8+ T cells influence the composition and spatial organization of tumors and the efficacy of an immune checkpoint blockade. Overall, we present a mechanistic understanding of tumor evolution that can be leveraged to study targeted immunotherapeutic strategies.
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Affiliation(s)
- Neel Tangella
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089, USA
| | - Colin G. Cess
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California 90089, USA
| | - Geena V. Ildefonso
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California 90089, USA
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2
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Hamidi S, Iyer PC, Dadu R, Gule-Monroe MK, Maniakas A, Zafereo ME, Wang JR, Busaidy NL, Cabanillas ME. Checkpoint Inhibition in Addition to Dabrafenib/Trametinib for BRAF V600E-Mutated Anaplastic Thyroid Carcinoma. Thyroid 2024; 34:336-346. [PMID: 38226606 DOI: 10.1089/thy.2023.0573] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Background: The dabrafenib plus trametinib combination (DT) has revolutionized the treatment of BRAFV600E-mutated anaplastic thyroid carcinoma (BRAFm-ATC). However, patients eventually develop resistance and progress. Single-agent anti-PD-1 inhibitor spartalizumab has shown a median overall survival (mOS) of 5.9 months. Combination of immunotherapy with BRAF/MEK inhibitors (BRAF/MEKi) seems to improve outcomes compared with BRAF/MEKi alone, although no direct comparison is available. BRAF-targeted therapy before surgery (neoadjuvant approach) has also shown improvement in survival. We studied the efficacy and safety of DT plus pembrolizumab (DTP) compared with current standard-of-care DT alone as an initial treatment, as well as in the neoadjuvant setting. Methods: Retrospective single-center study of patients with BRAFm-ATC treated with first-line BRAF-directed therapy between January 2014 and March 2023. Three groups were evaluated: DT, DTP (pembrolizumab added upfront or at progression), and neoadjuvant (DT before surgery, and pembrolizumab added before or after surgery). The primary endpoint was mOS between DT and DTP. Secondary endpoints included median progression-free survival (mPFS) and response rate with DT versus DTP as initial treatments, and the exploratory endpoint was mOS in the neoadjuvant group. Results: Seventy-one patients were included in the primary analysis: n = 23 in DT and n = 48 in DTP. Baseline demographics were similar between groups, including the presence of metastatic disease at start of treatment (p = 0.427) and prior treatments with surgery (p = 0.864) and radiation (p = 0.678). mOS was significantly longer with DTP (17.0 months [confidence interval CI, 11.9-22.1]) compared with DT alone (9.0 months [CI, 4.5-13.5]), p = 0.037. mPFS was also significantly improved with DTP as the initial treatment (11.0 months [CI, 7.0-15.0]) compared with DT alone (4.0 months [CI, 0.7-7.3]), p = 0.049. Twenty-three patients were in the exploratory neoadjuvant group, where mOS was the longest (63.0 months [CI, 15.5-110.5]). No grade 5 adverse events (AEs) occurred in all three cohorts, and 32.4% had immune-related AEs, most frequently hepatitis and colitis. Conclusions: Our results show that in BRAFm-ATC, addition of pembrolizumab to dabrafenib/trametinib may significantly prolong survival. Surgical resection of the primary tumor after initial BRAF-targeted therapy in selected patients may provide further survival benefit. However, conclusions are limited by the retrospective nature of the study. Additional prospective data are needed to confirm this observation.
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Affiliation(s)
- Sarah Hamidi
- Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Priyanka C Iyer
- Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ramona Dadu
- Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Maria K Gule-Monroe
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Anastasios Maniakas
- Department of Head & Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark E Zafereo
- Department of Head & Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jennifer R Wang
- Department of Head & Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Naifa L Busaidy
- Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Maria E Cabanillas
- Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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3
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Arabameri A, Arab S. Understanding the Interplay of CAR-NK Cells and Triple-Negative Breast Cancer: Insights from Computational Modeling. Bull Math Biol 2024; 86:20. [PMID: 38240892 DOI: 10.1007/s11538-023-01247-z] [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: 07/22/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells have recently emerged as a promising and safe alternative to CAR-T cells for targeting solid tumors. In the case of triple-negative breast cancer (TNBC), traditional cancer treatments and common immunotherapies have shown limited effectiveness. However, CAR-NK cells have been successfully employed to target epidermal growth factor receptor (EGFR) on TNBC cells, thereby enhancing the efficacy of immunotherapy. The effectiveness of CAR-NK-based immunotherapy is influenced by various factors, including the vaccination dose, vaccination pattern, and tumor immunosuppressive factors in the microenvironment. To gain insights into the dynamics and effects of CAR-NK-based immunotherapy, we propose a computational model based on experimental data and immunological theories. This model integrates an individual-based model that describes the interplay between the tumor and the immune system, along with an ordinary differential equation model that captures the variation of inflammatory cytokines. Computational results obtained from the proposed model shed light on the conditions necessary for initiating an effective anti-tumor response. Furthermore, global sensitivity analysis highlights the issue of low persistence of CAR-NK cells in vivo, which poses a significant challenge for the successful clinical application of these cells. Leveraging the model, we identify the optimal vaccination time, vaccination dose, and time interval between injections for maximizing therapeutic outcomes.
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Affiliation(s)
- Abazar Arabameri
- Department of Electrical Engineering, University of Zanjan, Zanjan, Iran.
| | - Samaneh Arab
- Department of Tissue Engineering and Applied Cell Sciences, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
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4
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [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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5
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Liu J, Piranlioglu R, Ye F, Shu K, Lei T, Nakashima H. Immunosuppressive cells in oncolytic virotherapy for glioma: challenges and solutions. Front Cell Infect Microbiol 2023; 13:1141034. [PMID: 37234776 PMCID: PMC10206241 DOI: 10.3389/fcimb.2023.1141034] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
Glioblastoma is a highly aggressive form of brain cancer characterized by the abundance of myeloid lineage cells in the tumor microenvironment. Tumor-associated macrophages and microglia (TAM) and myeloid-derived suppressor cells (MDSCs), play a pivotal role in promoting immune suppression and tumor progression. Oncolytic viruses (OVs) are self-amplifying cytotoxic agents that can stimulate local anti-tumor immune responses and have the potential to suppress immunosuppressive myeloid cells and recruit tumor-infiltrating T lymphocytes (TILs) to the tumor site, leading to an adaptive immune response against tumors. However, the impact of OV therapy on the tumor-resident myeloid population and the subsequent immune responses are not yet fully understood. This review provides an overview of how TAM and MDSC respond to different types of OVs, and combination therapeutics that target the myeloid population to promote anti-tumor immune responses in the glioma microenvironment.
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Affiliation(s)
- Junfeng Liu
- Harvey Cushing Neuro-Oncology Laboratories, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Raziye Piranlioglu
- Harvey Cushing Neuro-Oncology Laboratories, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Fei Ye
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Shu
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Lei
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hiroshi Nakashima
- Harvey Cushing Neuro-Oncology Laboratories, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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6
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Tian Y, Younis MR, Zhao Y, Guo K, Wu J, Zhang L, Huang P, Wang Z. Precision Delivery of Dual Immune Inhibitors Loaded Nanomodulator to Reverse Immune Suppression for Combinational Photothermal-Immunotherapy. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2206441. [PMID: 36799196 DOI: 10.1002/smll.202206441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/30/2022] [Indexed: 05/25/2023]
Abstract
Although photothermal therapy (PTT) can noninvasively kill tumor cells and exert synergistic immunological effects, the immune responses are usually harmed due to the lack of cytotoxic T cells (CTLs) pre-infiltration and co-existing of intricate immunosuppressive tumor microenvironment (TME), including the programmed cell death ligand 1 (PD-L1)/cluster of differentiation 47 (CD47)/regulatory T cells (Tregs)/M2-macrophages overexpression. Indoleamine 2, 3-dioxygenase inhibitor (NLG919) or bromodomain extra-terminal inhibitor (OTX015) holds great promise to reprogram suppressive TME through different pathways, but their collaborative application remains a formidable challenge because of the poor water solubility and low tumor targeting. To address this challenge, a desirable nanomodulator based on dual immune inhibitors loaded mesoporous polydopamine nanoparticles is designed. This nanomodulator exhibits excellent biocompatibility and water solubility, PTT, and bimodal magnetic resonance/photoacoustic imaging abilities. Owing to enhanced permeability and retention effect and tumor acidic pH-responsiveness, both inhibitors are precisely delivered and locally released at tumor sites. Such a nanomodulator significantly reverses the immune suppression of PD-L1/CD47/Tregs, promotes the activation of CTLs, regulates M2-macrophages polarization, and further boosts combined therapeutic efficacy, inducing a strong immunological memory. Taken together, the nanomodulator provides a practical approach for combinational photothermal-immunotherapy, which may be further broadened to other "immune cold" tumors.
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Affiliation(s)
- Ying Tian
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, P. R. China
| | - Muhammad Rizwan Younis
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, Laboratory of Evolutionary Theranostics (LET), School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, Guangdong, 518060, P. R. China
| | - Yatong Zhao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, P. R. China
| | - Kai Guo
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, P. R. China
| | - Jiayingzi Wu
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, Laboratory of Evolutionary Theranostics (LET), School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, Guangdong, 518060, P. R. China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, Jiangsu, 210002, P. R. China
| | - Peng Huang
- Marshall Laboratory of Biomedical Engineering, International Cancer Center, Laboratory of Evolutionary Theranostics (LET), School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, Guangdong, 518060, P. R. China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, P. R. China
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7
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Cess CG, Finley SD. Calibrating agent-based models to tumor images using representation learning. PLoS Comput Biol 2023; 19:e1011070. [PMID: 37083821 PMCID: PMC10156003 DOI: 10.1371/journal.pcbi.1011070] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/03/2023] [Accepted: 04/03/2023] [Indexed: 04/22/2023] Open
Abstract
Agent-based models (ABMs) have enabled great advances in the study of tumor development and therapeutic response, allowing researchers to explore the spatiotemporal evolution of the tumor and its microenvironment. However, these models face serious drawbacks in the realm of parameterization-ABM parameters are typically set individually based on various data and literature sources, rather than through a rigorous parameter estimation approach. While ABMs can be fit to simple time-course data (such as tumor volume), that type of data loses the spatial information that is a defining feature of ABMs. Tumor images provide spatial information; however, such images only represent individual timepoints, limiting their utility in calibrating the tumor dynamics predicted by ABMs. Furthermore, it is exceedingly difficult to compare tumor images to ABM simulations beyond a qualitative visual comparison. Without a quantitative method of comparing the similarity of tumor images to ABM simulations, a rigorous parameter fitting is not possible. Here, we present a novel approach that applies neural networks to represent both tumor images and ABM simulations as low dimensional points, with the distance between points acting as a quantitative measure of difference between the two. This enables a quantitative comparison of tumor images and ABM simulations, where the distance between simulated and experimental images can be minimized using standard parameter-fitting algorithms. Here, we describe this method and present two examples to demonstrate the application of the approach to estimate parameters for two distinct ABMs. Overall, we provide a novel method to robustly estimate ABM parameters.
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Affiliation(s)
- Colin G Cess
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
| | - Stacey D Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California, United States of America
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8
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Miller HA, Miller DM, van Berkel VH, Frieboes HB. Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling. Ann Biomed Eng 2023; 51:820-832. [PMID: 36224485 PMCID: PMC10023290 DOI: 10.1007/s10439-022-03096-8] [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: 06/24/2022] [Accepted: 10/02/2022] [Indexed: 11/28/2022]
Abstract
The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
| | - Donald M Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Victor H van Berkel
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA.
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9
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Amoddeo A. A mathematical model and numerical simulation for SARS-CoV-2 dynamics. Sci Rep 2023; 13:4575. [PMID: 36941368 PMCID: PMC10027279 DOI: 10.1038/s41598-023-31733-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 03/16/2023] [Indexed: 03/23/2023] Open
Abstract
Since its outbreak the corona virus-19 disease has been particularly aggressive for the lower respiratory tract, and lungs in particular. The dynamics of the abnormal immune response leading to lung damage with fatal outcomes is not yet fully understood. We present a mathematical model describing the dynamics of corona virus disease-19 starting from virus seeding inside the human respiratory tract, taking into account its interaction with the components of the innate immune system as classically and alternatively activated macrophages, interleukin-6 and -10. The numerical simulations have been performed for two different parameter values related to the pro-inflammatory interleukin, searching for a correlation among components dynamics during the early stage of infection, in particular pro- and anti-inflammatory polarizations of the immune response. We found that in the initial stage of infection the immune machinery is unable to stop or weaken the virus progression. Also an abnormal anti-inflammatory interleukin response is predicted, induced by the disease progression and clinically associated to tissue damages. The numerical results well reproduce experimental results found in literature.
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Affiliation(s)
- Antonino Amoddeo
- Department of Civil, Energy, Environment and Materials Engineering, Università 'Mediterranea' di Reggio Calabria, Via Graziella 1, Feo di Vito, 89122, Reggio Calabria, Italy.
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10
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Zhao X, Wang JR, Dadu R, Busaidy NL, Xu L, Learned KO, Chasen NN, Vu T, Maniakas A, Eguia AA, Diersing J, Gross ND, Goepfert R, Lai SY, Hofmann MC, Ferrarotto R, Lu C, Gunn GB, Spiotto MT, Subbiah V, Williams MD, Cabanillas ME, Zafereo ME. Surgery After BRAF-Directed Therapy Is Associated with Improved Survival in BRAF V600E Mutant Anaplastic Thyroid Cancer: A Single-Center Retrospective Cohort Study. Thyroid 2023; 33:484-491. [PMID: 36762947 PMCID: PMC10122263 DOI: 10.1089/thy.2022.0504] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Background: The aim of this study was to describe the oncologic outcomes of patients with BRAFV600E-mutated anaplastic thyroid cancer (ATC) who had neoadjuvant BRAF-directed therapy with subsequent surgery. For context, we also reviewed patients who received BRAF-directed therapy after surgery, and those who did not have surgery after BRAF-directed therapy. Methods: This was a single-center retrospective cohort study conducted at a tertiary care cancer center in Texas from 2017 to 2021. Fifty-seven consecutive patients with BRAFV600E-mutated ATC and at least 1 month of BRAF-directed therapy were included. Primary outcomes were overall survival (OS) and progression-free survival (PFS). Results: All patients had stage IVB (35%) or IVC (65%) ATC. Approximately 70% of patients treated with BRAF-directed therapy ultimately had surgical resection of residual disease. Patients who had neoadjuvant BRAF-directed therapy followed by surgery (n = 32) had 12-month OS of 93.6% [confidence interval (CI) 84.9-100] and PFS of 84.4% [CI 71.8-96.7]. Patients who had surgery before BRAF-directed therapy (n = 12) had 12-month OS of 74.1% [CI 48.7-99.5] and PFS of 50% [CI 21.7-78.3]. Finally, patients who did not receive surgery after BRAF-directed therapy (n = 13) had 12-month OS of 38.5% [CI 12.1-64.9] and PFS of 15.4% [CI 0-35.0]. Neoadjuvant BRAF-directed therapy reduced tumor size, extent of surgery, and surgical morbidity score. Subgroup analysis suggested that any residual ATC in the surgical specimen was associated with significantly worse 12-month OS and PFS (OS = 83.3% [CI 62.6-100], PFS = 61.5% [CI 35.1-88]) compared with patients with pathologic ATC complete response (OS = 100%, PFS = 100%). Conclusions: We observed that neoadjuvant BRAF-directed therapy reduced extent of surgery and surgical morbidity. While acknowledging potential selection bias, the 12-month OS rate appeared higher in patients who had BRAF-directed therapy followed by surgery as compared with BRAF-directed therapy without surgery; yet, it was not significantly different from surgery followed by BRAF-directed therapy. PFS appeared higher in patients treated with neoadjuvant BRAF-directed therapy relative to patients in the other groups. These promising results of neoadjuvant BRAF-directed therapy followed by surgery for BRAF-mutated ATC should be confirmed in prospective clinical trials.
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Affiliation(s)
- Xiao Zhao
- Department of Head and Neck Surgery; Houston, Texas, USA
| | | | - Ramona Dadu
- Endocrine Neoplasia and Hormonal Disorders; Houston, Texas, USA
| | | | - Lei Xu
- Department of Head and Neck Surgery; Houston, Texas, USA
| | | | | | - Thinh Vu
- Department of Neuroradiology; Houston, Texas, USA
| | | | - Arturo A Eguia
- Department of Head and Neck Surgery; Houston, Texas, USA
| | - Julia Diersing
- Department of Head and Neck Surgery; Houston, Texas, USA
| | - Neil D Gross
- Department of Head and Neck Surgery; Houston, Texas, USA
| | - Ryan Goepfert
- Department of Head and Neck Surgery; Houston, Texas, USA
| | - Stephen Y Lai
- Department of Head and Neck Surgery; Houston, Texas, USA
| | | | | | - Charles Lu
- Thoracic-Head and Neck Medical Oncology; Houston, Texas, USA
| | | | | | - Vivek Subbiah
- Investigational Cancer Therapeutics; Houston, Texas, USA
| | - Michelle D Williams
- Pathology; The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Mark E Zafereo
- Department of Head and Neck Surgery; Houston, Texas, USA
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11
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Bull JA, Byrne HM. Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions. PLoS Comput Biol 2023; 19:e1010994. [PMID: 36972297 PMCID: PMC10079237 DOI: 10.1371/journal.pcbi.1010994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 04/06/2023] [Accepted: 03/04/2023] [Indexed: 03/29/2023] Open
Abstract
We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the 'three Es of cancer immunoediting': Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a 'human readable' statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct 'PCF signature' that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples.
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Affiliation(s)
- Joshua A. Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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12
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Jørgensen ACS, Hill CS, Sturrock M, Tang W, Karamched SR, Gorup D, Lythgoe MF, Parrinello S, Marguerat S, Shahrezaei V. Data-driven spatio-temporal modelling of glioblastoma. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221444. [PMID: 36968241 PMCID: PMC10031411 DOI: 10.1098/rsos.221444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.
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Affiliation(s)
| | - Ciaran Scott Hill
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin D02 YN77, Ireland
| | - Wenhao Tang
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
| | - Saketh R. Karamched
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Dunja Gorup
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Mark F. Lythgoe
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Simona Parrinello
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Samuel Marguerat
- Genomics Translational Technology Platform, UCL Cancer Institute, University College London, London WC1E 6DD, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
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13
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Goodin DA, Frieboes HB. Evaluation of innate and adaptive immune system interactions in the tumor microenvironment via a 3D continuum model. J Theor Biol 2023; 559:111383. [PMID: 36539112 DOI: 10.1016/j.jtbi.2022.111383] [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: 05/06/2022] [Revised: 12/09/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
Immune cells in the tumor microenvironment (TME) are known to affect tumor growth, vascularization, and extracellular matrix (ECM) deposition. Marked interest in system-scale analysis of immune species interactions within the TME has encouraged progress in modeling tumor-immune interactions in silico. Due to the computational cost of simulating these intricate interactions, models have typically been constrained to representing a limited number of immune species. To expand the capability for system-scale analysis, this study develops a three-dimensional continuum mixture model of tumor-immune interactions to simulate multiple immune species in the TME. Building upon a recent distributed computing implementation that enables efficient solution of such mixture models, major immune species including monocytes, macrophages, natural killer cells, dendritic cells, neutrophils, myeloid-derived suppressor cells (MDSC), cytotoxic, helper, regulatory T-cells, and effector and regulatory B-cells and their interactions are represented in this novel implementation. Immune species extravasate from blood vasculature, undergo chemotaxis toward regions of high chemokine concentration, and influence the TME in proportion to locally defined levels of stimulation. The immune species contribute to the production of angiogenic and tumor growth factors, promotion of myofibroblast deposition of ECM, upregulation of angiogenesis, and elimination of living and dead tumor species. The results show that this modeling approach offers the capability for quantitative insight into the modulation of tumor growth by diverse immune-tumor interactions and immune-driven TME effects. In particular, MDSC-mediated effects on tumor-associated immune species' activation levels, volume fraction, and influence on the TME are explored. Longer term, linking of the model parameters to particular patient tumor information could simulate cancer-specific immune responses and move toward a more comprehensive evaluation of immunotherapeutic strategies.
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Affiliation(s)
- Dylan A Goodin
- Department of Bioengineering, University of Louisville, KY, USA
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, KY, USA; James Graham Brown Cancer Center, University of Louisville, KY, USA; Center for Predictive Medicine, University of Louisville, KY, USA.
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14
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Abstract
Cancer cells require higher oxygen levels and nutrition than normal cells. Cancer cells induce angiogenesis (the development of new blood vessels) from preexisting vessels. This biological process depends on the special, chemical, and physical properties of the microenvironment surrounding tumor tissues. The complexity of these properties hinders an understanding of their mechanisms. Various mathematical models have been developed to describe quantitative relationships related to angiogenesis. We developed a three-dimensional mathematical model that incorporates angiogenesis and tumor growth. We examined angiopoietin, which regulates the spouting and branching events in angiogenesis. The simulation successfully reproduced the transient decrease in new vessels during vascular network formation. This chapter describes the protocol used to perform the simulations.
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Affiliation(s)
- Masahiro Sugimoto
- Institute of Medical Science, Tokyo Medical University, Shinjuku, Tokyo, Japan.
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
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15
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Kim Y, Lee J, Lee C, Lawler S. Role of senescent tumor cells in building a cytokine shield in the tumor microenvironment: mathematical modeling. J Math Biol 2022; 86:14. [PMID: 36512100 DOI: 10.1007/s00285-022-01850-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 10/29/2022] [Accepted: 12/01/2022] [Indexed: 12/15/2022]
Abstract
Cellular senescence can induce dual effects (promotion or inhibition) on cancer progression. While immune cells naturally respond and migrate toward various chemotactic sources from the tumor mass, various factors including senescent tumor cells (STCs) in the tumor microenvironment may affect this chemotactic movement. In this work, we investigate the mutual interactions between the tumor cells and the immune cells that either inhibit or facilitate tumor growth by developing a mathematical model that consists of taxis-reaction-diffusion equations and receptor kinetics for the key players in the interaction network. We apply a mathematical model to a transwell Boyden chamber invasion assay used in the experiments to illustrate that STCs can play a pivotal role in negating immune attack through tight regulation of intra- and extra-cellular signaling molecules. In particular, we show that senescent tumor cells in cell cycle arrest can block intratumoral infiltration of CD8+ T cells by secreting a high level of CXCL12, which leads to significant reduction its receptors, CXCR4, on T cells, and thus impaired chemotaxis. The predictions of nonlinear responses to CXCL12 were in good agreement with experimental data. We tested several hypotheses on immune-tumor interactions under various biochemical conditions in the tumor microenvironment and developed new concepts for anti-tumor strategies targeting senescence induced immune impairment.
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Affiliation(s)
- Yangjin Kim
- Department of Mathematics, Konkuk University, Seoul, 05029, Republic of Korea.
| | - Junho Lee
- Department of Mathematics, Konkuk University, Seoul, 05029, Republic of Korea
| | - Chaeyoung Lee
- Department of Mathematics, Korea University, Seoul, Republic of Korea
| | - Sean Lawler
- Department of Pathology and Laboratory Medicine, Brown Cancer Center, Brown University, Providence, RI, USA
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16
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Almásy MG, Hörömpő A, Kiss D, Kertész G. A review on modeling tumor dynamics and agent reward functions in reinforcement learning based therapy optimization. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Revolutionary changes of deep reinforcement learning are leading to high-performing intelligent solutions in multiple fields, including healthcare. At the moment, chemotherapy and radiotherapy are common types of treatments for cancer, however, both therapies are usually radical procedures with undesirable side effects. There is an increasing number of evidence that patient-based optimal schedule has a significant impact in increasing efficiency and survival, and reducing side effects during both therapies. To apply artificial intelligence in therapy optimization, an adequate model of tumor growth incorporating the effect of the treatment is mandatory. A method on training a controller for dosage and scheduling, reinforcement learning can be applied, where a well-chosen agent rewarding function is key to achieve optimal behavior. In this survey paper, some selected tumor growth models, reinforcement learning based solutions and especially agent reward functions are reviewed and compared, providing a summary on state of the art approaches.
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Affiliation(s)
| | - András Hörömpő
- Obuda University John von Neumann Faculty of Informatics, Budapest, Hungary
| | - Dániel Kiss
- Obuda University John von Neumann Faculty of Informatics, Budapest, Hungary
| | - Gábor Kertész
- Obuda University John von Neumann Faculty of Informatics, Budapest, Hungary
- Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Budapest, Hungary
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17
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Shojaee P, Mornata F, Deutsch A, Locati M, Hatzikirou H. The impact of tumor associated macrophages on tumor biology under the lens of mathematical modelling: A review. Front Immunol 2022; 13:1050067. [PMID: 36439180 PMCID: PMC9685623 DOI: 10.3389/fimmu.2022.1050067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 10/18/2022] [Indexed: 09/10/2023] Open
Abstract
In this article, we review the role of mathematical modelling to elucidate the impact of tumor-associated macrophages (TAMs) in tumor progression and therapy design. We first outline the biology of TAMs, and its current application in tumor therapies, and their experimental methods that provide insights into tumor cell-macrophage interactions. We then focus on the mechanistic mathematical models describing the role of macrophages as drug carriers, the impact of macrophage polarized activation on tumor growth, and the role of tumor microenvironment (TME) parameters on the tumor-macrophage interactions. This review aims to identify the synergies between biological and mathematical approaches that allow us to translate knowledge on fundamental TAMs biology in addressing current clinical challenges.
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Affiliation(s)
- Pejman Shojaee
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
| | - Federica Mornata
- Leukocyte Biology Lab, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Andreas Deutsch
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
| | - Massimo Locati
- Leukocyte Biology Lab, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Medical Biotechnologies and Translational Medicine, Universitàdegli Studi di Milano, Milan, Italy
| | - Haralampos Hatzikirou
- Centre for Information Services and High Performance Computing, Technische Universität (TU) Dresden, Dresden, Germany
- Mathematics Department, Khalifa University, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Centre (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
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18
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Todosenko N, Yurova K, Khaziakhmatova O, Malashchenko V, Khlusov I, Litvinova L. Heparin and Heparin-Based Drug Delivery Systems: Pleiotropic Molecular Effects at Multiple Drug Resistance of Osteosarcoma and Immune Cells. Pharmaceutics 2022; 14:pharmaceutics14102181. [PMID: 36297616 PMCID: PMC9612132 DOI: 10.3390/pharmaceutics14102181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/29/2022] [Accepted: 10/10/2022] [Indexed: 11/23/2022] Open
Abstract
One of the main problems of modern health care is the growing number of oncological diseases both in the elderly and young population. Inadequately effective chemotherapy, which remains the main method of cancer control, is largely associated with the emergence of multidrug resistance in tumor cells. The search for new solutions to overcome the resistance of malignant cells to pharmacological agents is being actively pursued. Another serious problem is immunosuppression caused both by the tumor cells themselves and by antitumor drugs. Of great interest in this context is heparin, a biomolecule belonging to the class of glycosaminoglycans and possessing a broad spectrum of biological activity, including immunomodulatory and antitumor properties. In the context of the rapid development of the new field of “osteoimmunology,” which focuses on the collaboration of bone and immune cells, heparin and delivery systems based on it may be of intriguing importance for the oncotherapy of malignant bone tumors. Osteosarcoma is a rare but highly aggressive, chemoresistant malignant tumor that affects young adults and is characterized by constant recurrence and metastasis. This review describes the direct and immune-mediated regulatory effects of heparin and drug delivery systems based on it on the molecular mechanisms of (multiple) drug resistance in (onco) pathological conditions of bone tissue, especially osteosarcoma.
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Affiliation(s)
- Natalia Todosenko
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
| | - Kristina Yurova
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
| | - Olga Khaziakhmatova
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
| | - Vladimir Malashchenko
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
| | - Igor Khlusov
- Department of Morphology and General Pathology, Siberian State Medical University, 634050 Tomsk, Russia
| | - Larisa Litvinova
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
- Correspondence:
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19
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Cheng K, Cai N, Zhu J, Yang X, Liang H, Zhang W. Tumor-associated macrophages in liver cancer: From mechanisms to therapy. CANCER COMMUNICATIONS (LONDON, ENGLAND) 2022; 42:1112-1140. [PMID: 36069342 DOI: 10.1002/cac2.12345] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 06/28/2022] [Accepted: 07/26/2022] [Indexed: 12/19/2022]
Abstract
Multidimensional analyses have demonstrated the presence of a unique tumor microenvironment (TME) in liver cancer. Tumor-associated macrophages (TAMs) are among the most abundant immune cells infiltrating the TME and are present at all stages of liver cancer progression, and targeting TAMs has become one of the most favored immunotherapy strategies. In addition, macrophages and liver cancer cells have distinct origins. At the early stage of liver cancer, macrophages can provide a niche for the maintenance of liver cancer stem cells. In contrast, cancer stem cells (CSCs) or poorly differentiated tumor cells are key factors modulating macrophage activation. In the present review, we first propose the origin connection between precursor macrophages and liver cancer cells. Macrophages undergo dynamic phenotypic transition during carcinogenesis. In this course of such transition, it is critical to determine the appropriate timing for therapy and block specific markers to suppress pro-tumoral TAMs. The present review provides a more detailed discussion of transition trends of such surface markers than previous reviews. Complex crosstalk occurs between TAMs and liver cancer cells. TAMs play indispensable roles in tumor progression, angiogenesis, and autophagy due to their heterogeneity and robust plasticity. In addition, macrophages in the TME interact with other immune cells by directing cell-to-cell contact or secreting various effector molecules. Similarly, tumor cells combined with other immune cells can drive macrophage recruitment and polarization. Despite the latest achievements and the advancements in treatment strategies following TAMs studies, comprehensive discussions on the communication between macrophages and cancer cells or immune cells in liver cancer are currently lacking. In this review, we discussed the interactions between TAMs and liver cancer cells (from cell origin to maturation), the latest therapeutic strategies (including chimeric antigen receptor macrophages), and critical clinical trials for hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) to provide a rationale for further clinical investigation of TAMs as a potential target for treating patients with liver cancer.
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Affiliation(s)
- Kun Cheng
- Hepatic Surgery Centre, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P. R. China
| | - Ning Cai
- Hepatic Surgery Centre, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P. R. China
| | - Jinghan Zhu
- Hepatic Surgery Centre, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P. R. China
| | - Xing Yang
- Hepatic Surgery Centre, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P. R. China
| | - Huifang Liang
- Hepatic Surgery Centre, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P. R. China
| | - Wanguang Zhang
- Hepatic Surgery Centre, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P. R. China
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20
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Ruiz-Martinez A, Gong C, Wang H, Sové RJ, Mi H, Kimko H, Popel AS. Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model. PLoS Comput Biol 2022; 18:e1010254. [PMID: 35867773 PMCID: PMC9348712 DOI: 10.1371/journal.pcbi.1010254] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 08/03/2022] [Accepted: 05/26/2022] [Indexed: 12/23/2022] Open
Abstract
Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.
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Affiliation(s)
- Alvaro Ruiz-Martinez
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Chang Gong
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Richard J. Sové
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, Maryland, United States of America
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
- Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, United States of America
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21
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Tajaldini M, Saeedi M, Amiriani T, Amiriani AH, Sedighi S, Mohammad Zadeh F, Dehghan M, Jahanshahi M, Zanjan Ghandian M, Khalili P, Poorkhani AH, Alizadeh AM, Khori V. Cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs); where do they stand in tumorigenesis and how they can change the face of cancer therapy? Eur J Pharmacol 2022; 928:175087. [PMID: 35679891 DOI: 10.1016/j.ejphar.2022.175087] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/18/2022] [Accepted: 06/03/2022] [Indexed: 11/03/2022]
Abstract
The tumor microenvironment (TME) and its components have recently attracted tremendous attention in cancer treatment strategies, as alongside the genetic and epigenetic alterations in tumor cells, TME could also provide a fertile background for malignant cells to survive and proliferate. Interestingly, TME plays a vital role in the mediation of cancer metastasis and drug resistance even against immunotherapeutic agents. Among different cells that are presenting in TME, tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) have shown to have significant value in the regulation of angiogenesis, tumor metastasis, and drug-resistance through manipulating the composition as well as the organization of extracellular matrix (ECM). Evidence has shown that the presence of both TAMs and CAFs in TME is associated with poor prognosis and failure of chemotherapeutic agents. It seems that these cells together with ECM form a shield around tumor cells to protect them from the toxic agents and even the adaptive arm of the immune system, which is responsible for tumor surveillance. Given this, targeting TAMs and CAFs seems to be an essential approach to potentiate the cytotoxic effects of anti-cancer agents, either conventional chemotherapeutic drugs or immunotherapies. In the present review, we aimed to take a deep look at the mechanobiology of CAFs and TAMs in tumor progression and to discuss the available therapeutic approaches for harnessing these cells in TME.
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Affiliation(s)
- Mahboubeh Tajaldini
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Mohsen Saeedi
- Stem Cell Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Taghi Amiriani
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Amir Hossein Amiriani
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Sima Sedighi
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Fatemeh Mohammad Zadeh
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Mohammad Dehghan
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Mehrdad Jahanshahi
- Neuroscience Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Maziar Zanjan Ghandian
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Pedram Khalili
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | | | - Ali Mohammad Alizadeh
- Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Vahid Khori
- Ischemic Disorder Research Center, Golestan University of Medical Sciences, Gorgan, Iran.
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22
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Rojas-Domínguez A, Arroyo-Duarte R, Rincón-Vieyra F, Alvarado-Mentado M. Modeling cancer immunoediting in tumor microenvironment with system characterization through the ising-model Hamiltonian. BMC Bioinformatics 2022; 23:200. [PMID: 35637445 PMCID: PMC9150349 DOI: 10.1186/s12859-022-04731-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 05/11/2022] [Indexed: 12/02/2022] Open
Abstract
Background and objective Cancer Immunoediting (CI) describes the cellular-level interaction between tumor cells and the Immune System (IS) that takes place in the Tumor Micro-Environment (TME). CI is a highly dynamic and complex process comprising three distinct phases (Elimination, Equilibrium and Escape) wherein the IS can both protect against cancer development as well as, over time, promote the appearance of tumors with reduced immunogenicity. Herein we present an agent-based model for the simulation of CI in the TME, with the objective of promoting the understanding of this process. Methods Our model includes agents for tumor cells and for elements of the IS. The actions of these agents are governed by probabilistic rules, and agent recruitment (including cancer growth) is modeled via logistic functions. The system is formalized as an analogue of the Ising model from statistical mechanics to facilitate its analysis. The model was implemented in the Netlogo modeling environment and simulations were performed to verify, illustrate and characterize its operation. Results A main result from our simulations is the generation of emergent behavior in silico that is very difficult to observe directly in vivo or even in vitro. Our model is capable of generating the three phases of CI; it requires only a couple of control parameters and is robust to these. We demonstrate how our simulated system can be characterized through the Ising-model energy function, or Hamiltonian, which captures the “energy” involved in the interaction between agents and presents it in clear and distinct patterns for the different phases of CI. Conclusions The presented model is very flexible and robust, captures well the behaviors of the target system and can be easily extended to incorporate more variables such as those pertaining to different anti-cancer therapies. System characterization via the Ising-model Hamiltonian is a novel and powerful tool for a better understanding of CI and the development of more effective treatments. Since data of CI at the cellular level is very hard to procure, our hope is that tools such as this may be adopted to shed light on CI and related developing theories. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04731-w.
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Affiliation(s)
- Alfonso Rojas-Domínguez
- Postgraduate Studies and Research Division, Tecnológico Nacional de México - IT de León, León, Mexico
| | | | - Fernando Rincón-Vieyra
- Depto. de Computación, CINVESTAV-IPN, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, GAM, 07360, Mexico City, CDMX, Mexico
| | - Matías Alvarado-Mentado
- Depto. de Computación, CINVESTAV-IPN, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, GAM, 07360, Mexico City, CDMX, Mexico.
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Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data. Ann Biomed Eng 2022; 50:314-329. [PMID: 35083584 PMCID: PMC9743982 DOI: 10.1007/s10439-022-02904-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 01/01/2022] [Indexed: 12/15/2022]
Abstract
Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.
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24
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Comprehensive characterization of the alternative splicing landscape in ovarian cancer reveals novel events associated with tumor-immune microenvironment. Biosci Rep 2022; 42:230626. [PMID: 35137909 PMCID: PMC8829021 DOI: 10.1042/bsr20212090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/09/2021] [Accepted: 01/07/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Ovarian cancer (OV) is a serious threat to women’s health. Immunotherapy is a new approach. Alternative splicing (AS) of messenger RNA (mRNA) and its regulation are highly relevant for understanding every cancer hallmark and may offer a broadened target space. Methods: We downloaded the clinical information and mRNA expression profiles of 587 tumor tissues from The Cancer Genome Atlas (TCGA) database. We constructed a risk score model to predict the prognosis of OV patients. The association between AS-based clusters and tumor-immune microenvironment features was further explored. The ESTIMATE algorithm was also carried out on each OV sample depending on the risk score groups. A total of three immune checkpoint genes that have a significant correlation with risk scores were screened. Results: The AS-events were a reliable and stable independent risk predictor in the OV cohort. Patients in the high-risk score group had a poor prognosis (P<0.001). Mast cells activated, NK cells resting, and Neutrophils positively correlated with the risk score. The number of Macrophages M1 was also more numerous in the low-risk score group (P<0.05). Checkpoint genes CD274, CTLA-4, and PDCD1LG2, showed a negative correlation with the risk score of AS in OV. Conclusions: The proposed AS signature is a promising biomarker for estimating overall survival (OS) in OV. The AS-events signature combined with tumor-immune microenvironment enabled a deeper understanding of the immune status of OV patients, and also provided new insights for exploring novel prognostic predictors and precise therapy methods.
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25
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Zhou L, Fu F, Wang Y, Yang L. Interlocked feedback loops balance the adaptive immune response. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:4084-4100. [PMID: 35341288 DOI: 10.3934/mbe.2022188] [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: 06/14/2023]
Abstract
Adaptive immune responses can be activated by harmful stimuli. Upon activation, a cascade of biochemical events ensues the proliferation and the differentiation of T cells, which can remove the stimuli and undergo cell death to maintain immune cell homeostasis. However, normal immune processes can be disrupted by certain dysregulations, leading to pathological responses, such as cytokine storms and immune escape. In this paper, a qualitative mathematical model, composed of key feedback loops within the immune system, was developed to study the dynamics of various response behaviors. First, simulation results of the model well reproduce the results of several immune response processes, particularly pathological immune responses. Next, we demonstrated how the interaction of positive and negative feedback loops leads to irreversible bistable, reversible bistable and monostable, which characterize different immune response processes: cytokine storm, normal immune response, immune escape. The stability analyses suggest that the switch-like behavior is the basis of rapid activation of the immune system, and a balance between positive and negative regulation loops is necessary to prevent pathological responses. Furthermore, we have shown how the treatment moves the system back to a healthy state from the pathological immune response. The bistable mechanism that revealed in this work is helpful to understand the dynamics of different immune response processes.
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Affiliation(s)
- Lingli Zhou
- School of Mathematical Sciences, Soochow University, Suzhou 215006, China
- Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Fengqing Fu
- Jiangsu Institute of Clinical Immunology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou 215123, China
| | - Yao Wang
- School of Mathematical Sciences, Soochow University, Suzhou 215006, China
- Center for Systems Biology, Soochow University, Suzhou 215006, China
| | - Ling Yang
- School of Mathematical Sciences, Soochow University, Suzhou 215006, China
- Center for Systems Biology, Soochow University, Suzhou 215006, China
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26
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Mbuguiro W, Gonzalez AN, Mac Gabhann F. Computational Models for Diagnosing and Treating Endometriosis. FRONTIERS IN REPRODUCTIVE HEALTH 2021; 3:699133. [DOI: 10.3389/frph.2021.699133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 11/23/2021] [Indexed: 11/13/2022] Open
Abstract
Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endometrial-like tissue (including epithelial cells, stromal fibroblasts, vascular cells, and immune cells) outside of the uterus. Computational models can aid in understanding the mechanisms by which immune, hormone, and vascular disruptions manifest in endometriosis and complicate treatment. In this review, we illustrate how three computational modeling approaches (regression, pharmacokinetics/pharmacodynamics, and quantitative systems pharmacology) have been used to improve the diagnosis and treatment of endometriosis. As we explore these approaches and their differing detail of biological mechanisms, we consider how each approach can answer different questions about endometriosis. We summarize the mathematics involved, and we use published examples of each approach to compare how researchers: (1) shape the scope of each model, (2) incorporate experimental and clinical data, and (3) generate clinically useful predictions and insight. Lastly, we discuss the benefits and limitations of each modeling approach and how we can combine these approaches to further understand, diagnose, and treat endometriosis.
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27
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He Y, de Araújo Júnior RF, Cruz LJ, Eich C. Functionalized Nanoparticles Targeting Tumor-Associated Macrophages as Cancer Therapy. Pharmaceutics 2021; 13:1670. [PMID: 34683963 PMCID: PMC8540805 DOI: 10.3390/pharmaceutics13101670] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/02/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
The tumor microenvironment (TME) plays a central role in regulating antitumor immune responses. As an important part of the TME, alternatively activated type 2 (M2) macrophages drive the development of primary and secondary tumors by promoting tumor cell proliferation, tumor angiogenesis, extracellular matrix remodeling and overall immunosuppression. Immunotherapy approaches targeting tumor-associated macrophages (TAMs) in order to reduce the immunosuppressive state in the TME have received great attention. Although these methods hold great potential for the treatment of several cancers, they also face some limitations, such as the fast degradation rate of drugs and drug-induced cytotoxicity of organs and tissues. Nanomedicine formulations that prevent TAM signaling and recruitment to the TME or deplete M2 TAMs to reduce tumor growth and metastasis represent encouraging novel strategies in cancer therapy. They allow the specific delivery of antitumor drugs to the tumor area, thereby reducing side effects associated with systemic application. In this review, we give an overview of TAM biology and the current state of nanomedicines that target M2 macrophages in the course of cancer immunotherapy, with a specific focus on nanoparticles (NPs). We summarize how different types of NPs target M2 TAMs, and how the physicochemical properties of NPs (size, shape, charge and targeting ligands) influence NP uptake by TAMs in vitro and in vivo in the TME. Furthermore, we provide a comparative analysis of passive and active NP-based TAM-targeting strategies and discuss their therapeutic potential.
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Affiliation(s)
- Yuanyuan He
- Translational Nanobiomaterials and Imaging (TNI) Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Y.H.); (R.F.d.A.J.)
| | - Raimundo Fernandes de Araújo Júnior
- Translational Nanobiomaterials and Imaging (TNI) Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Y.H.); (R.F.d.A.J.)
- Postgraduate Program in Health Science, Federal University of Rio Grande do Norte (UFRN), Natal 59064-720, Brazil
- Cancer and Inflammation Research Laboratory (LAICI), Postgraduate Program in Functional and Structural Biology, Department of Morphology, Federal University of Rio Grande do Norte (UFRN), Natal 59064-720, Brazil
- Percuros B.V., 2333 CL Leiden, The Netherlands
| | - Luis J. Cruz
- Translational Nanobiomaterials and Imaging (TNI) Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Y.H.); (R.F.d.A.J.)
| | - Christina Eich
- Translational Nanobiomaterials and Imaging (TNI) Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Y.H.); (R.F.d.A.J.)
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28
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Modeling codelivery of CD73 inhibitor and dendritic cell-based vaccines in cancer immunotherapy. Comput Biol Chem 2021; 95:107585. [PMID: 34610532 DOI: 10.1016/j.compbiolchem.2021.107585] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/16/2021] [Accepted: 09/23/2021] [Indexed: 11/21/2022]
Abstract
Dendritic cells (DCs) are the dominant class of antigen-presenting cells in humans; therefore, a range of DC-based approaches have been established to promote an immune response against cancer cells. The efficacy of DC-based immunotherapeutic approaches is markedly affected by the immunosuppressive factors related to the tumor microenvironment, such as adenosine. In this paper, based on immunological theories and experimental data, a hybrid model is designed that offers some insights into the effects of DC-based immunotherapy combined with adenosine inhibition. The model combines an individual-based model for describing tumor-immune system interactions with a set of ordinary differential equations for adenosine modeling. Computational simulations of the proposed model clarify the conditions for the onset of a successful immune response against cancer cells. Global and local sensitivity analysis of the model highlights the importance of adenosine blockage for strengthening effector cells. The model is used to determine the most effective suppressive mechanism caused by adenosine, proper vaccination time, and the appropriate time interval between injections.
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29
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Yang Y, Guo J, Huang L. Tackling TAMs for Cancer Immunotherapy: It's Nano Time. Trends Pharmacol Sci 2021; 41:701-714. [PMID: 32946772 DOI: 10.1016/j.tips.2020.08.003] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 07/13/2020] [Accepted: 08/04/2020] [Indexed: 12/14/2022]
Abstract
The tumor microenvironment (TME) is a highly complex environment that surrounds tumors. Interactions between cancer cells/non-cancerous cells and cells/non-cell components in the TME support tumor initiation, development, and metastasis. Of the cell types in the TME, tumor-associated macrophages (TAMs) have gained attention owing to their crucial roles in supporting tumors and conferring therapy resistance. Recent developments in nanotechnology raise opportunities for the application of nano targeted drug-delivery systems (Nano-TDDS) in cancer therapy. We focus our discussion on current knowledge of TAMs, and describe recent examples of Nano-TDDS-based TAM modulation, highlighting strategies to overcome in vivo delivery barriers associated with the TME and their potential for clinical translation.
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Affiliation(s)
- Yishun Yang
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Experiment Centre of Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jianfeng Guo
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; School of Pharmaceutical Sciences, Jilin University, Changchun 130021, China.
| | - Leaf Huang
- Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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30
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The Heterogeneity of Infiltrating Macrophages in Metastatic Osteosarcoma and Its Correlation with Immunotherapy. JOURNAL OF ONCOLOGY 2021; 2021:4836292. [PMID: 34335756 PMCID: PMC8321719 DOI: 10.1155/2021/4836292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/28/2021] [Indexed: 12/23/2022]
Abstract
Background Metastatic osteosarcoma is a common and fatal bone tumor. Several studies have found that tumor-infiltrating immune cells play pivotal roles in the progression of metastatic osteosarcoma. However, the heterogeneity of infiltrating immune cells across metastatic and primary osteosarcoma remains unclear. Methods Immune infiltration analysis was carried out via the “ESTIMATE” and “xCell” algorithms in primary and metastatic osteosarcoma. Then, we evaluated the prognostic value of infiltrating immune cells in 85 osteosarcomas through the Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curve. Infiltrations of macrophage M1 and M2 were evaluated in metastatic osteosarcoma, as well as their correlation with immune checkpoints. Macrophage-related prognostic genes were identified through Weighted Gene Coexpression Network Analysis (WGCNA), Lasso analysis, and Random Forest algorithm. Finally, a macrophage-related risk model had been constructed and validated. Results Macrophages, especially the macrophage M1, sparingly infiltrated in metastatic compared with the primary osteosarcoma and predicted the worse overall survival (OS) and disease-free survival (DFS). Macrophage M1 was positively correlated with immune checkpoints PDCD1, CD274 (PD-L1), PDCD1LG2, CTLA4, and TIGIT. In addition, four macrophage-related prognostic genes (IL10, VAV1, CD14, and CCL2) had been identified, and the macrophage-related risk model had been validated to be reliable for evaluating prognosis in osteosarcoma. Simultaneously, the risk score showed a strong correlation with several immune checkpoints. Conclusion Macrophages potentially contribute to the regulation of osteosarcoma metastasis. It can be used as a candidate marker for metastatic osteosarcoma' prognosis and immune checkpoints blockades (ICBs) therapy. We constructed a macrophage-related risk model through machine-learning, which might help us evaluate patients' prognosis and response to ICBs therapy.
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31
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Curtis LT, Sebens S, Frieboes HB. Modeling of tumor response to macrophage and T lymphocyte interactions in the liver metastatic microenvironment. Cancer Immunol Immunother 2021; 70:1475-1488. [PMID: 33180183 PMCID: PMC10992133 DOI: 10.1007/s00262-020-02785-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/26/2020] [Indexed: 12/12/2022]
Abstract
The dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment exert both antagonistic and synergistic functions affecting tumor growth. Extensive experimental effort has been expended to investigate immunotherapeutic strategies targeting macrophage polarization as well as T-cell activation with the goal to promote tumor cell killing and cancer elimination. However, these interactions remain poorly understood, and cancer immunotherapeutic strategies are often disappointing. The complex system encompassing innate and adaptive immune cell activity in response to tumor growth could benefit from a systems perspective built upon mathematical modeling. This study develops a modeling system to help evaluate the effects of macrophage and T-lymphocyte interactions on tumor growth. The system enables simulating the combined cytotoxic and tumor-promoting interactions of these two immune cell populations in a vascularized organ microenvironment, such as in liver metastases. A hypothetical immunotherapeutic strategy is simulated to increase the number of tumor-suppressive (M1-phenotype) vs. tumor-promoting (M2-phenotype) macrophages to gauge their effects on CD8+ T-cells and CD4+ T-helper cells, which in turn affect the macrophage functions. The results highlight the dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment and show that with the chosen set of parameter values, the overall cytotoxic effect from macrophages and T-lymphocytes obtained by driving the M1:M2 ratio higher could saturate and fail to achieve tumor regression. Further expansion of this modeling platform to include additional tumor-immune cell interactions, coupled with parameters representing particular tumor characteristics, could enable systematic evaluation of immunotherapeutic strategies tailored to patient-tumor specific conditions, including metastatic disease.
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Affiliation(s)
- Louis T Curtis
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA
| | - Susanne Sebens
- Institute for Experimental Cancer Research, Christian-Albrechts-University Kiel (CAU), Kiel, Germany
- University Medical Center Schleswig-Holstein (UK-SH), Campus Kiel, Kiel, Germany
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
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32
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Wang Y, Brodin E, Nishii K, Frieboes HB, Mumenthaler SM, Sparks JL, Macklin P. Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach. Sci Rep 2021; 11:1710. [PMID: 33462259 PMCID: PMC7813881 DOI: 10.1038/s41598-020-78780-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 11/24/2020] [Indexed: 12/17/2022] Open
Abstract
Colorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma (normal liver tissue) are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales (adhesion, repulsion, and elastic tissue deformation over minutes) and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could "reawaken" after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer.
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Affiliation(s)
- Yafei Wang
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Erik Brodin
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA
| | - Kenichiro Nishii
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica L Sparks
- Department of Chemical, Paper and Biomedical Engineering, Miami University, Oxford, OH, USA.
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
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33
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Mathematical simulation of tumour angiogenesis: angiopoietin balance is a key factor in vessel growth and regression. Sci Rep 2021; 11:419. [PMID: 33432093 PMCID: PMC7801613 DOI: 10.1038/s41598-020-79824-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 12/10/2020] [Indexed: 12/12/2022] Open
Abstract
Excessive tumour growth results in a hypoxic environment around cancer cells, thus inducing tumour angiogenesis, which refers to the generation of new blood vessels from pre-existing vessels. This mechanism is biologically and physically complex, with various mathematical simulation models proposing to reproduce its formation. However, although temporary vessel regression is clinically known, few models succeed in reproducing this phenomenon. Here, we developed a three-dimensional simulation model encompassing both angiogenesis and tumour growth, specifically including angiopoietin. Angiopoietin regulates both adhesion and migration between vascular endothelial cells and wall cells, thus inhibiting the cell-to-cell adhesion required for angiogenesis initiation. Simulation results showed a regression, i.e. transient decrease, in the overall length of new vessels during vascular network formation. Using our model, we also evaluated the efficacy of administering the drug bevacizumab. The results highlighted differences in treatment efficacy: (1) earlier administration showed higher efficacy in inhibiting tumour growth, and (2) efficacy depended on the treatment interval even with the administration of the same dose. After thorough validation in the future, these results will contribute to the design of angiogenesis treatment protocols.
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34
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Zhu S, Luo Z, Li X, Han X, Shi S, Zhang T. Tumor-associated macrophages: role in tumorigenesis and immunotherapy implications. J Cancer 2021; 12:54-64. [PMID: 33391402 PMCID: PMC7738842 DOI: 10.7150/jca.49692] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/24/2020] [Indexed: 12/12/2022] Open
Abstract
Tumor-associated macrophages (TAMs) occupy an important position in the tumor microenvironment (TME), they are a highly plastic heterogeneous population with complex effects on tumorigenesis and development. TAMs secrete a variety of cytokines, chemokines, and proteases, which promote the remodeling of extracellular matrix, tumor cell growth and metastasis, tumor vessel and lymphangiogenesis, and immunosuppression. TAMs with different phenotypes have different effects on tumor proliferation and metastasis. TAMs act a pivotal part in occurrence and development of tumors, and are very attractive target to inhibit tumor growth and metastasis in tumor immunotherapy. This article reviews the interrelationship between TAMs and tumor microenvironment and its related applications in tumor therapy.
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Affiliation(s)
- Shunyao Zhu
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Ziyi Luo
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Xixi Li
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Xi Han
- Xiaoshan Hosptital of Traditional Chinese Medicine, Hangzhou 311201, China
| | - Senlin Shi
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Ting Zhang
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
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35
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Abstract
The tumor microenvironment contains many cellular components influencing tumor behaviors, such as metastasis, angiogenesis and chemo-resistance. Tumor-associated macrophages (TAMs) are one of such components that can also manipulate the overall prognosis and patient survival. Analysis of tumor-macrophage crosstalk is crucial as tumor cells can polarize circulatory monocytes into TAMs. Such trans-polarization of macrophages support tumor mediated evasion and suppression of immune response. Additionally, such TAMs significantly influence tumor growth and proliferation, making them a potential candidate for precision therapeutics. However, the failure of macrophage-dependent therapies at clinical trials emphasizes the fault in current perception and research modality. This review discussed this field's progress regarding emerging model systems with a focused view on the in vitro platforms. The inadequacy of currently available models and their implications on existing studies also analyzed. The need for a conceptual and experimental leap toward a human-relevant in vitro custom-built platform for studying tumor-macrophage crosstalk is acknowledged.
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Affiliation(s)
- Tuli Dey
- Institute of Bioinformatics and Biotechnology, Savitribai Phule Pune University, Pune, India
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36
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Cess CG, Finley SD. Multi-scale modeling of macrophage-T cell interactions within the tumor microenvironment. PLoS Comput Biol 2020; 16:e1008519. [PMID: 33362239 PMCID: PMC7790427 DOI: 10.1371/journal.pcbi.1008519] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 01/07/2021] [Accepted: 11/11/2020] [Indexed: 02/06/2023] Open
Abstract
Within the tumor microenvironment, macrophages exist in an immunosuppressive state, preventing T cells from eliminating the tumor. Due to this, research is focusing on immunotherapies that specifically target macrophages in order to reduce their immunosuppressive capabilities and promote T cell function. In this study, we develop an agent-based model consisting of the interactions between macrophages, T cells, and tumor cells to determine how the immune response changes due to three macrophage-based immunotherapeutic strategies: macrophage depletion, recruitment inhibition, and macrophage reeducation. We find that reeducation, which converts the macrophages into an immune-promoting phenotype, is the most effective strategy and that the macrophage recruitment rate and tumor proliferation rate (tumor-specific properties) have large impacts on therapy efficacy. We also employ a novel method of using a neural network to reduce the computational complexity of an intracellular signaling mechanistic model.
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Affiliation(s)
- Colin G. Cess
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
| | - Stacey D. Finley
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
- Department of Quantitative and Biological Sciences, University of Southern California, Los Angeles, California, United States of America
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California, United States of America
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37
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Directionality of Macrophages Movement in Tumour Invasion: A Multiscale Moving-Boundary Approach. Bull Math Biol 2020; 82:148. [PMID: 33211193 PMCID: PMC7677171 DOI: 10.1007/s11538-020-00819-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 10/07/2020] [Indexed: 12/11/2022]
Abstract
Invasion of the surrounding tissue is one of the recognised hallmarks of cancer (Hanahan and Weinberg in Cell 100: 57–70, 2000. 10.1016/S0092-8674(00)81683-9), which is accomplished through a complex heterotypic multiscale dynamics involving tissue-scale random and directed movement of the population of both cancer cells and other accompanying cells (including here, the family of tumour-associated macrophages) as well as the emerging cell-scale activity of both the matrix-degrading enzymes and the rearrangement of the cell-scale constituents of the extracellular matrix (ECM) fibres. The involved processes include not only the presence of cell proliferation and cell adhesion (to other cells and to the extracellular matrix), but also the secretion of matrix-degrading enzymes. This is as a result of cancer cells as well as macrophages, which are one of the most abundant types of immune cells in the tumour micro-environment. In large tumours, these tumour-associated macrophages (TAMs) have a tumour-promoting phenotype, contributing to tumour proliferation and spread. In this paper, we extend a previous multiscale moving-boundary mathematical model for cancer invasion, by considering also the multiscale effects of TAMs, with special focus on the influence that their directional movement exerts on the overall tumour progression. Numerical investigation of this new model shows the importance of the interactions between pro-tumour TAMs and the fibrous ECM, highlighting the impact of the fibres on the spatial structure of solid tumour.
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38
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Frieboes HB, Raghavan S, Godin B. Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis. Front Bioeng Biotechnol 2020; 8:1011. [PMID: 32974325 PMCID: PMC7466654 DOI: 10.3389/fbioe.2020.01011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 08/03/2020] [Indexed: 12/13/2022] Open
Abstract
The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.
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Affiliation(s)
- Hermann B. Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, United States
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, United States
- Center for Predictive Medicine, University of Louisville, Louisville, KY, United States
| | - Shreya Raghavan
- Department of Biomedical Engineering, College of Engineering, Texas A&M University, College Station, TX, United States
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
| | - Biana Godin
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
- Department of Obstetrics and Gynecology, Houston Methodist Hospital, Houston, TX, United States
- Developmental Therapeutics Program, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, United States
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Chelliah V, Lazarou G, Bhatnagar S, Gibbs JP, Nijsen M, Ray A, Stoll B, Thompson RA, Gulati A, Soukharev S, Yamada A, Weddell J, Sayama H, Oishi M, Wittemer-Rump S, Patel C, Niederalt C, Burghaus R, Scheerans C, Lippert J, Kabilan S, Kareva I, Belousova N, Rolfe A, Zutshi A, Chenel M, Venezia F, Fouliard S, Oberwittler H, Scholer-Dahirel A, Lelievre H, Bottino D, Collins SC, Nguyen HQ, Wang H, Yoneyama T, Zhu AZX, van der Graaf PH, Kierzek AM. Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm. Clin Pharmacol Ther 2020; 109:605-618. [PMID: 32686076 PMCID: PMC7983940 DOI: 10.1002/cpt.1987] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/06/2020] [Indexed: 12/12/2022]
Abstract
Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.
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Affiliation(s)
| | | | | | | | | | - Avijit Ray
- Abbvie Inc., North Chicago, Illinois, USA
| | | | | | - Abhishek Gulati
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Serguei Soukharev
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Akihiro Yamada
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Jared Weddell
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Hiroyuki Sayama
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | - Masayo Oishi
- Astellas Pharma Global Development Inc./Astellas Pharma Inc., Northbrook, Illinois, USA.,Astellas Pharma Global Development Inc./Astellas Pharma Inc., Tokyo or Tsukuba-shi, Japan
| | | | | | | | | | | | | | | | - Irina Kareva
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | | | - Alex Rolfe
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | - Anup Zutshi
- EMD Serono, Merck KGaA, Billerica, Massachusetts, USA
| | | | | | | | | | | | | | - Dean Bottino
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Sabrina C Collins
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Hoa Q Nguyen
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Haiqing Wang
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Tomoki Yoneyama
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
| | - Andy Z X Zhu
- Millennium Pharmaceuticals Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Ltd., Cambridge, Massachusetts, USA
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40
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Cassidy T, Humphries AR, Craig M, Mackey MC. Characterizing Chemotherapy-Induced Neutropenia and Monocytopenia Through Mathematical Modelling. Bull Math Biol 2020; 82:104. [PMID: 32737602 DOI: 10.1007/s11538-020-00777-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/11/2020] [Indexed: 12/18/2022]
Abstract
In spite of the recent focus on the development of novel targeted drugs to treat cancer, cytotoxic chemotherapy remains the standard treatment for the vast majority of patients. Unfortunately, chemotherapy is associated with high hematopoietic toxicity that may limit its efficacy. We have previously established potential strategies to mitigate chemotherapy-induced neutropenia (a lack of circulating neutrophils) using a mechanistic model of granulopoiesis to predict the interactions defining the neutrophil response to chemotherapy and to define optimal strategies for concurrent chemotherapy/prophylactic granulocyte colony-stimulating factor (G-CSF). Here, we extend our analyses to include monocyte production by constructing and parameterizing a model of monocytopoiesis. Using data for neutrophil and monocyte concentrations during chemotherapy in a large cohort of childhood acute lymphoblastic leukemia patients, we leveraged our model to determine the relationship between the monocyte and neutrophil nadirs during cyclic chemotherapy. We show that monocytopenia precedes neutropenia by 3 days, and rationalize the use of G-CSF during chemotherapy by establishing that the onset of monocytopenia can be used as a clinical marker for G-CSF dosing post-chemotherapy. This work therefore has important clinical applications as a comprehensive approach to understanding the relationship between monocyte and neutrophils after cyclic chemotherapy with or without G-CSF support.
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Affiliation(s)
- Tyler Cassidy
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Antony R Humphries
- Department of Mathematics and Statistics, McGill University, Montréal, QC, H3A 0B9, Canada.,Department of Physiology, McGill University, Montréal, QC, H3A 0B9, Canada
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada. .,CHU Sainte-Justine Research Centre, University of Montreal, Montréal, Canada.
| | - Michael C Mackey
- Department of Physiology, McGill University, 3655 Drummond, Montréal, QC, H3G 1Y6, Canada.,Department of Mathematics and Statistics, McGill University, 3655 Drummond, Montréal, QC, H3G 1Y6, Canada.,Department of Physics, McGill University, 3655 Drummond, Montréal, QC, H3G 1Y6, Canada
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Leonard F, Curtis LT, Hamed AR, Zhang C, Chau E, Sieving D, Godin B, Frieboes HB. Nonlinear response to cancer nanotherapy due to macrophage interactions revealed by mathematical modeling and evaluated in a murine model via CRISPR-modulated macrophage polarization. Cancer Immunol Immunother 2020; 69:731-744. [PMID: 32036448 PMCID: PMC7186159 DOI: 10.1007/s00262-020-02504-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 01/24/2020] [Indexed: 12/11/2022]
Abstract
Tumor-associated macrophages (TAMs) have been shown to both aid and hinder tumor growth, with patient outcomes potentially hinging on the proportion of M1, pro-inflammatory/growth-inhibiting, to M2, growth-supporting, phenotypes. Strategies to stimulate tumor regression by promoting polarization to M1 are a novel approach that harnesses the immune system to enhance therapeutic outcomes, including chemotherapy. We recently found that nanotherapy with mesoporous particles loaded with albumin-bound paclitaxel (MSV-nab-PTX) promotes macrophage polarization towards M1 in breast cancer liver metastases (BCLM). However, it remains unclear to what extent tumor regression can be maximized based on modulation of the macrophage phenotype, especially for poorly perfused tumors such as BCLM. Here, for the first time, a CRISPR system is employed to permanently modulate macrophage polarization in a controlled in vitro setting. This enables the design of 3D co-culture experiments mimicking the BCLM hypovascularized environment with various ratios of polarized macrophages. We implement a mathematical framework to evaluate nanoparticle-mediated chemotherapy in conjunction with TAM polarization. The response is predicted to be not linearly dependent on the M1:M2 ratio. To investigate this phenomenon, the response is simulated via the model for a variety of M1:M2 ratios. The modeling indicates that polarization to an all-M1 population may be less effective than a combination of both M1 and M2. Experimental results with the CRISPR system confirm this model-driven hypothesis. Altogether, this study indicates that response to nanoparticle-mediated chemotherapy targeting poorly perfused tumors may benefit from a fine-tuned M1:M2 ratio that maintains both phenotypes in the tumor microenvironment during treatment.
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Affiliation(s)
- Fransisca Leonard
- Department of Nanomedicine, Houston Methodist Research Institute, R8-213, 6670 Bertner St., Houston, TX, 77030, USA
| | - Louis T Curtis
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | - Ahmed R Hamed
- Department of Nanomedicine, Houston Methodist Research Institute, R8-213, 6670 Bertner St., Houston, TX, 77030, USA
- Pharmaceutical and Drug Industries Research Division, National Research Centre, Giza, Egypt
| | - Carolyn Zhang
- Department of Nanomedicine, Houston Methodist Research Institute, R8-213, 6670 Bertner St., Houston, TX, 77030, USA
| | - Eric Chau
- Department of Nanomedicine, Houston Methodist Research Institute, R8-213, 6670 Bertner St., Houston, TX, 77030, USA
| | - Devon Sieving
- Department of Nanomedicine, Houston Methodist Research Institute, R8-213, 6670 Bertner St., Houston, TX, 77030, USA
| | - Biana Godin
- Department of Nanomedicine, Houston Methodist Research Institute, R8-213, 6670 Bertner St., Houston, TX, 77030, USA.
- Department of Obstetrics and Gynecology, Houston Methodist Hospital, Houston, TX, USA.
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA.
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42
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Tao S, Zhao Z, Zhang X, Guan X, Wei J, Yuan B, He S, Zhao D, Zhang J, Liu Q, Ding Y. The role of macrophages during breast cancer development and response to chemotherapy. Clin Transl Oncol 2020; 22:1938-1951. [PMID: 32279178 DOI: 10.1007/s12094-020-02348-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 03/21/2020] [Indexed: 12/14/2022]
Abstract
Macrophages play an important role in the immune system as a key host defense against pathogens. Non-polarized macrophages can differentiate into pro-inflammatory classical pathway-activated macrophages or anti-inflammatory alternative pathway-activated macrophages, both of which play central roles in breast cancer growth and progression in a process called polarization of macrophages. Classical pathway-activated and alternative pathway-activated macrophages can transform into each other and their transformational properties and orientation are determined by cytokines in the tumor microenvironment. Tumor-associated macrophages display many functions, such as tissue reforming, participating in inflammation and tumor growth in breast cancer progression. Some cytokines, such as interleukins and transcriptional activators, reside in the tumor microenvironment and influence tumor-associated macrophages. Chemotherapy is a common treatment for breast cancer and macrophages play an important role in mammary tumor cell migration, cancer invasion, and angiogenesis. This review summarizes the activities of tumor-associated macrophages in the mammary tumor, chemotherapeutic processes and some potential strategies for breast cancer therapy.
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Affiliation(s)
- S Tao
- Department of Laboratory Animals, College of Animal Sciences, Jilin University, Changchun, 130062, China
| | - Z Zhao
- The Second Clinical School of Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine-Zhuhai Hospital, Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China.,The 2nd Clinical School of Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.,The 85th Hospital of CPLA, Shanghai, 200040, China.,Guangdong Provincial Hospital of Chinese Medicine-Zhuhai Hospital, Zhuhai, 519015, China
| | - X Zhang
- College of Animal Science and Technology, Jilin Agricultural University, Changchun, 130118, China
| | - X Guan
- Department of Laboratory Animals, College of Animal Sciences, Jilin University, Changchun, 130062, China
| | - J Wei
- Department of Laboratory Animals, College of Animal Sciences, Jilin University, Changchun, 130062, China
| | - B Yuan
- Department of Laboratory Animals, College of Animal Sciences, Jilin University, Changchun, 130062, China
| | - S He
- Department of Laboratory Animals, College of Animal Sciences, Jilin University, Changchun, 130062, China
| | - D Zhao
- Department of Laboratory Animals, College of Animal Sciences, Jilin University, Changchun, 130062, China
| | - J Zhang
- Department of Laboratory Animals, College of Animal Sciences, Jilin University, Changchun, 130062, China.
| | - Q Liu
- The Second Clinical School of Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine-Zhuhai Hospital, Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China. .,Department of Immunology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China. .,The 2nd Clinical School of Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, 510120, China. .,Guangdong Provincial Hospital of Chinese Medicine-Zhuhai Hospital, Zhuhai, 519015, China.
| | - Y Ding
- Department of Laboratory Animals, College of Animal Sciences, Jilin University, Changchun, 130062, China.
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43
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Eftimie R. Investigation into the role of macrophages heterogeneity on solid tumour aggregations. Math Biosci 2020; 322:108325. [DOI: 10.1016/j.mbs.2020.108325] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 02/03/2020] [Accepted: 02/16/2020] [Indexed: 01/01/2023]
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44
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Makaryan SZ, Cess CG, Finley SD. Modeling immune cell behavior across scales in cancer. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1484. [PMID: 32129950 PMCID: PMC7317398 DOI: 10.1002/wsbm.1484] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/07/2020] [Accepted: 02/04/2020] [Indexed: 12/17/2022]
Abstract
Detailed, mechanistic models of immune cell behavior across multiple scales in the context of cancer provide clinically relevant insights needed to understand existing immunotherapies and develop more optimal treatment strategies. We highlight mechanistic models of immune cells and their ability to become activated and promote tumor cell killing. These models capture various aspects of immune cells: (a) single‐cell behavior by predicting the dynamics of intracellular signaling networks in individual immune cells, (b) multicellular interactions between tumor and immune cells, and (c) multiscale dynamics across space and different levels of biological organization. Computational modeling is shown to provide detailed quantitative insight into immune cell behavior and immunotherapeutic strategies. However, there are gaps in the literature, and we suggest areas where additional modeling efforts should be focused to more prominently impact our understanding of the complexities of the immune system in the context of cancer. This article is categorized under:Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Cellular Models
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Affiliation(s)
- Sahak Z Makaryan
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
| | - Colin G Cess
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
| | - Stacey D Finley
- Department of Biomedical Engineering, Mork Family Department of Chemical Engineering and Materials Science, Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
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45
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Wang H, Sové RJ, Jafarnejad M, Rahmeh S, Jaffee EM, Stearns V, Torres ETR, Connolly RM, Popel AS. Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors. Front Bioeng Biotechnol 2020; 8:141. [PMID: 32158754 PMCID: PMC7051945 DOI: 10.3389/fbioe.2020.00141] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 02/11/2020] [Indexed: 12/16/2022] Open
Abstract
The survival rate of patients with breast cancer has been improved by immune checkpoint blockade therapies, and the efficacy of their combinations with epigenetic modulators has shown promising results in preclinical studies. In this prospective study, we propose an ordinary differential equation (ODE)-based quantitative systems pharmacology (QSP) model to conduct an in silico virtual clinical trial and analyze potential predictive biomarkers to improve the anti-tumor response in HER2-negative breast cancer. The model is comprised of four compartments: central, peripheral, tumor, and tumor-draining lymph node, and describes immune activation, suppression, T cell trafficking, and pharmacokinetics and pharmacodynamics (PK/PD) of the therapeutic agents. We implement theoretical mechanisms of action for checkpoint inhibitors and the epigenetic modulator based on preclinical studies to investigate their effects on anti-tumor response. According to model-based simulations, we confirm the synergistic effect of the epigenetic modulator and that pre-treatment tumor mutational burden, tumor-infiltrating effector T cell (Teff) density, and Teff to regulatory T cell (Treg) ratio are significantly higher in responders, which can be potential biomarkers to be considered in clinical trials. Overall, we present a readily reproducible modular model to conduct in silico virtual clinical trials on patient cohorts of interest, which is a step toward personalized medicine in cancer immunotherapy.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Richard J. Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mohammad Jafarnejad
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Sondra Rahmeh
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Elizabeth M. Jaffee
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Viragh Center for Pancreatic Clinical Research and Care, Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Vered Stearns
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Evanthia T. Roussos Torres
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Roisin M. Connolly
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Viragh Center for Pancreatic Clinical Research and Care, Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Curtis LT, Frieboes HB. Modeling of Combination Chemotherapy and Immunotherapy for Lung Cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:273-276. [PMID: 31945894 DOI: 10.1109/embc.2019.8857566] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Cancer has traditionally been studied from a basic science perspective, focusing on the underlying biology, physiology, and biochemistry. Engineering has supplemented this effort via the development of technology, e.g., microscopy. In recent times, engineering and the physical sciences have positioned themselves as approaches on par with the traditional basic sciences to tackle the study of cancer. Mathematical modeling and computational simulation have become key elements of this engineering-focused effort, evaluating the growth of tumors and their response to therapy as problems that could benefit from a systems analysis perspective. Building upon previous work in this field, here is developed a modeling framework to help evaluate the response of tumors to the combination of chemotherapy and immunotherapy, focusing on non-small cell lung cancer (NSCLC). With system parameters set with patient tumor-specific parameters, the longer term goal of this work is to advance personalized cancer treatment.
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47
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Mpekris F, Voutouri C, Baish JW, Duda DG, Munn LL, Stylianopoulos T, Jain RK. Combining microenvironment normalization strategies to improve cancer immunotherapy. Proc Natl Acad Sci U S A 2020; 117:3728-3737. [PMID: 32015113 PMCID: PMC7035612 DOI: 10.1073/pnas.1919764117] [Citation(s) in RCA: 159] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Advances in immunotherapy have revolutionized the treatment of multiple cancers. Unfortunately, tumors usually have impaired blood perfusion, which limits the delivery of therapeutics and cytotoxic immune cells to tumors and also results in hypoxia-a hallmark of the abnormal tumor microenvironment (TME)-that causes immunosuppression. We proposed that normalization of TME using antiangiogenic drugs and/or mechanotherapeutics can overcome these challenges. Recently, immunotherapy with checkpoint blockers was shown to effectively induce vascular normalization in some types of cancer. Although these therapeutic approaches have been used in combination in preclinical and clinical studies, their combined effects on TME are not fully understood. To identify strategies for improved immunotherapy, we have developed a mathematical framework that incorporates complex interactions among various types of cancer cells, immune cells, stroma, angiogenic molecules, and the vasculature. Model predictions were compared with the data from five previously reported experimental studies. We found that low doses of antiangiogenic treatment improve immunotherapy when the two treatments are administered sequentially, but that high doses are less efficacious because of excessive vessel pruning and hypoxia. Stroma normalization can further increase the efficacy of immunotherapy, and the benefit is additive when combined with vascular normalization. We conclude that vessel functionality dictates the efficacy of immunotherapy, and thus increased tumor perfusion should be investigated as a predictive biomarker of response to immunotherapy.
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Affiliation(s)
- Fotios Mpekris
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, 1678 Nicosia, Cyprus
| | - Chrysovalantis Voutouri
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, 1678 Nicosia, Cyprus
| | - James W Baish
- Department of Biomedical Engineering, Bucknell University, Lewisburg, PA 17837
| | - Dan G Duda
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Lance L Munn
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, 1678 Nicosia, Cyprus;
| | - Rakesh K Jain
- Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
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48
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Spatio-temporal aspects of the interplay of cancer and the immune system. J Biol Phys 2019; 45:395-400. [PMID: 31773382 PMCID: PMC6917631 DOI: 10.1007/s10867-019-09535-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 11/07/2019] [Indexed: 10/26/2022] Open
Abstract
The conventional mean-field kinetic models describing the interplay of cancer and the immune system are temporal and predict exponential growth or elimination of the population of tumour cells provided their number is small and their effect on the immune system is negligible. More complex kinetics are associated with non-linear features of the response of the immune system. The generic model presented in this communication takes into account that the rates of the birth and death of tumour cells inside a tumour spheroid can significantly depend on the radial coordinate due to diffusion limitations in the supply of nutrients and/or transport of the species (cells and proteins) belonging to the immune system. In this case, non-trivial kinetic regimes are shown to be possible even without appreciable perturbation of the immune system.
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Zhao C, Mirando AC, Sové RJ, Medeiros TX, Annex BH, Popel AS. A mechanistic integrative computational model of macrophage polarization: Implications in human pathophysiology. PLoS Comput Biol 2019; 15:e1007468. [PMID: 31738746 PMCID: PMC6860420 DOI: 10.1371/journal.pcbi.1007468] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 10/08/2019] [Indexed: 12/24/2022] Open
Abstract
Macrophages respond to signals in the microenvironment by changing their functional phenotypes, a process known as polarization. Depending on the context, they acquire different patterns of transcriptional activation, cytokine expression and cellular metabolism which collectively constitute a continuous spectrum of phenotypes, of which the two extremes are denoted as classical (M1) and alternative (M2) activation. To quantitatively decode the underlying principles governing macrophage phenotypic polarization and thereby harness its therapeutic potential in human diseases, a systems-level approach is needed given the multitude of signaling pathways and intracellular regulation involved. Here we develop the first mechanism-based, multi-pathway computational model that describes the integrated signal transduction and macrophage programming under M1 (IFN-γ), M2 (IL-4) and cell stress (hypoxia) stimulation. Our model was calibrated extensively against experimental data, and we mechanistically elucidated several signature feedbacks behind the M1-M2 antagonism and investigated the dynamical shaping of macrophage phenotypes within the M1-M2 spectrum. Model sensitivity analysis also revealed key molecular nodes and interactions as targets with potential therapeutic values for the pathophysiology of peripheral arterial disease and cancer. Through simulations that dynamically capture the signal integration and phenotypic marker expression in the differential macrophage polarization responses, our model provides an important computational basis toward a more quantitative and network-centric understanding of the complex physiology and versatile functions of macrophages in human diseases.
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Affiliation(s)
- Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
| | - Adam C. Mirando
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Richard J. Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Thalyta X. Medeiros
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America
- Divison of Cardiovascular Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
| | - Brian H. Annex
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America
- Divison of Cardiovascular Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
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Hudson SV, Miller HA, Mahlbacher GE, Saforo D, Beverly LJ, Arteel GE, Frieboes HB. Computational/experimental evaluation of liver metastasis post hepatic injury: interactions with macrophages and transitional ECM. Sci Rep 2019; 9:15077. [PMID: 31636296 PMCID: PMC6803648 DOI: 10.1038/s41598-019-51249-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 09/27/2019] [Indexed: 12/19/2022] Open
Abstract
The complex interactions between subclinical changes to hepatic extracellular matrix (ECM) in response to injury and tumor-associated macrophage microenvironmental cues facilitating metastatic cell seeding remain poorly understood. This study implements a combined computational modeling and experimental approach to evaluate tumor growth following hepatic injury, focusing on ECM remodeling and interactions with local macrophages. Experiments were performed to determine ECM density and macrophage-associated cytokine levels. Effects of ECM remodeling along with macrophage polarization on tumor growth were evaluated via computational modeling. For primary or metastatic cells in co-culture with macrophages, TNF-α levels were 5× higher with M1 vs. M2 macrophages. Metastatic cell co-culture exhibited 10× higher TNF-α induction than with primary tumor cells. Although TGFβ1 induction was similar between both co-cultures, levels were slightly higher with primary cells in the presence of M1. Simulated metastatic tumors exhibited decreased growth compared to primary tumors, due to high local M1-induced cytotoxicity, even in a highly vascularized microenvironment. Experimental analysis combined with computational modeling may provide insight into interactions between ECM remodeling, macrophage polarization, and liver tumor growth.
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Affiliation(s)
- Shanice V Hudson
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, 40292, USA
- Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, 40292, USA
| | - Grace E Mahlbacher
- Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Douglas Saforo
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, 40292, USA
| | - Levi J Beverly
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, 40292, USA
- Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40292, USA
| | - Gavin E Arteel
- Department of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- University of Louisville Alcohol Research Center, University of Louisville, Louisville, KY, 40292, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, 40292, USA.
- Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40292, USA.
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