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Nguyen T, Campbell A, Kumar A, Amponsah E, Fiterau M, Shahriyari L. Optimal fusion of genotype and drug embeddings in predicting cancer drug response. Brief Bioinform 2024; 25:bbae227. [PMID: 38754407 PMCID: PMC11097979 DOI: 10.1093/bib/bbae227] [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: 10/29/2023] [Revised: 04/14/2024] [Accepted: 04/25/2024] [Indexed: 05/18/2024] Open
Abstract
Predicting cancer drug response using both genomics and drug features has shown some success compared to using genomics features alone. However, there has been limited research done on how best to combine or fuse the two types of features. Using a visible neural network with two deep learning branches for genes and drug features as the base architecture, we experimented with different fusion functions and fusion points. Our experiments show that injecting multiplicative relationships between gene and drug latent features into the original concatenation-based architecture DrugCell significantly improved the overall predictive performance and outperformed other baseline models. We also show that different fusion methods respond differently to different fusion points, indicating that the relationship between drug features and different hierarchical biological level of gene features is optimally captured using different methods. Considering both predictive performance and runtime speed, tensor product partial is the best-performing fusion function to combine late-stage representations of drug and gene features to predict cancer drug response.
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Affiliation(s)
- Trang Nguyen
- Department of Computer Science, University of Massachusetts Amherst, Amherst 01002, MA, United States
| | - Anthony Campbell
- Department of Computer Science, University of Massachusetts Amherst, Amherst 01002, MA, United States
| | - Ankit Kumar
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst 01002, MA, United States
| | - Edwin Amponsah
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst 01002, MA, United States
| | - Madalina Fiterau
- Department of Computer Science, University of Massachusetts Amherst, Amherst 01002, MA, United States
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst 01002, MA, United States
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2
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Sofia D, Zhou Q, Shahriyari L. Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review. Bioengineering (Basel) 2023; 10:1320. [PMID: 38002445 PMCID: PMC10669004 DOI: 10.3390/bioengineering10111320] [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: 10/17/2023] [Revised: 11/08/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.
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Affiliation(s)
| | | | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (D.S.); (Q.Z.)
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3
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He X, Lan H, Jin K, Liu F. Can immunotherapy reinforce chemotherapy efficacy? a new perspective on colorectal cancer treatment. Front Immunol 2023; 14:1237764. [PMID: 37790928 PMCID: PMC10543914 DOI: 10.3389/fimmu.2023.1237764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/25/2023] [Indexed: 10/05/2023] Open
Abstract
As one of the main threats to human life (the fourth most dangerous and prevalent cancer), colorectal cancer affects many people yearly, decreases patients' quality of life, and causes irreparable financial and social damages. In addition, this type of cancer can metastasize and involve the liver in advanced stages. However, current treatments can't completely eradicate this disease. Chemotherapy and subsequent surgery can be mentioned among the current main treatments for this disease. Chemotherapy has many side effects, and regarding the treatment of this type of tumor, chemotherapy can lead to liver damage, such as steatohepatitis, steatosis, and sinus damage. These damages can eventually lead to liver failure and loss of its functions. Therefore, it seems that other treatments can be used in addition to chemotherapy to increase its efficiency and reduce its side effects. Biological therapies and immunotherapy are one of the leading suggestions for combined treatment. Antibodies (immune checkpoint blockers) and cell therapy (DC and CAR-T cells) are among the immune system-based treatments used to treat tumors. Immunotherapy targets various aspects of the tumor that may lead to 1) the recruitment of immune cells, 2) increasing the immunogenicity of tumor cells, and 3) leading to the elimination of inhibitory mechanisms established by the tumor. Therefore, immunotherapy can be used as a complementary treatment along with chemotherapy. This review will discuss different chemotherapy and immunotherapy methods for colorectal cancer. Then we will talk about the studies that have dealt with combined treatment.
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Affiliation(s)
- Xing He
- Department of Gastroenterology, Jinhua Wenrong Hospital, Jinhua, Zhejiang, China
| | - Huanrong Lan
- Department of Surgical Oncology, Hangzhou Cancer Hospital, Hangzhou, Zhejiang, China
| | - Ketao Jin
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, China
| | - Fanlong Liu
- Department of Colorectal Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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4
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Zhang T, Liu Z, Lin Q. Clinical effect and safety of targeted therapy combined with chemotherapy in the treatment of patients with advanced colon cancer. Pak J Med Sci 2023; 39:1074-1079. [PMID: 37492316 PMCID: PMC10364270 DOI: 10.12669/pjms.39.4.7105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 02/21/2023] [Accepted: 02/27/2023] [Indexed: 07/27/2023] Open
Abstract
Objective To evaluate the clinical effect and safety of immunotherapy combined with chemotherapy in patients with advanced colon cancer. Methods This is a retrospective study. The subjects of this study were 120 patients with advanced colon cancer who were admitted to The No.2 Hospital of Baoding from November 30, 2019 to November 30, 2021. The enrolled patients were randomly divided into two groups, with 60 cases in each group. Patients in the control group were given F0LF0X4 regimen, while those in the study group were provided with Bevacizumab therapy on the basis of the method in the control group. All patients were evaluated after two cycles of treatment. The comparison of outcome measures included the curative effects, adverse drug reactions, improvement of quality-of-life scores and changes in tumor markers between the two groups. Results The total effective rate of the study group was significantly better than that of the control group. There was no significant difference in the incidence of adverse drug reactions between the two groups. After treatment, the study group had a significantly higher rate of improved quality of life score, while the obviously lower rate of the aggravated score than those in the control group. The levels of CEA, CA19-9 and CA125 in the study group were significantly lower than those in the control group after treatment. Conclusion Targeted therapy combined with chemotherapy is a safe and effective therapeutic option that has a definite curative effect in the treatment of patients with advanced colon cancer.
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Affiliation(s)
- Tao Zhang
- Tao Zhang, Department of General Surgery, The No.2 Hospital of Baoding, Baoding 071051, Hebei, P.R. China
| | - Zhi Liu
- Zhi Liu, Department of General Surgery, The No.2 Hospital of Baoding, Baoding 071051, Hebei, P.R. China
| | - Qian Lin
- Qian Lin, Department of Nursing, The No.2 Hospital of Baoding, Baoding 071051, Hebei, P.R. China
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5
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Mohammad Mirzaei N, Hao W, Shahriyari L. Investigating the spatial interaction of immune cells in colon cancer. iScience 2023; 26:106596. [PMID: 37168560 PMCID: PMC10165418 DOI: 10.1016/j.isci.2023.106596] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/28/2023] [Accepted: 04/03/2023] [Indexed: 05/13/2023] Open
Abstract
The intricate network of interactions between cells and molecules in the tumor microenvironment creates a heterogeneous ecosystem. The proximity of the cells and molecules to their activators and inhibitors is essential in the progression of tumors. Here, we develop a system of partial differential equations coupled with linear elasticity to investigate the effects of spatial interactions on the tumor microenvironment. We observe interesting cell and cytokine distribution patterns, which are heavily affected by macrophages. We also see that cytotoxic T cells get recruited and suppressed at the site of macrophages. Moreover, we observe that anti-tumor macrophages reorganize the patterns in favor of a more spatially restricted cancer and necrotic core. Furthermore, the adjoint-based sensitivity analysis indicates that the most sensitive model's parameters are directly related to macrophages. The results emphasize the widely acknowledged effect of macrophages in controlling cancer cells population and spatially arranging cells in the tumor microenvironment.
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Affiliation(s)
- Navid Mohammad Mirzaei
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, 01003 MA, USA
| | - Wenrui Hao
- Department of Mathematics, Pennsylvania State University, University Park, 16802 PA, USA
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, 01003 MA, USA
- Corresponding author
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6
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Patient-Specific Mathematical Model of the Clear Cell Renal Cell Carcinoma Microenvironment. J Pers Med 2022; 12:jpm12101681. [PMID: 36294824 PMCID: PMC9605269 DOI: 10.3390/jpm12101681] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/21/2022] [Accepted: 09/30/2022] [Indexed: 11/04/2022] Open
Abstract
The interactions between cells and molecules in the tumor microenvironment can give insight into the initiation and progression of tumors and their optimal treatment options. In this paper, we developed an ordinary differential equation (ODE) mathematical model of the interaction network of key players in the clear cell renal cell carcinoma (ccRCC) microenvironment. We then performed a global gradient-based sensitivity analysis to investigate the effects of the most sensitive parameters of the model on the number of cancer cells. The results indicate that parameters related to IL-6 have high a impact on cancer cell growth, such that decreasing the level of IL-6 can remarkably slow the tumor's growth.
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7
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Shariatifar H, Ranjbarian F, Hajiahmadi F, Farasat A. A comprehensive review on methotrexate containing nanoparticles; an appropriate tool for cancer treatment. Mol Biol Rep 2022; 49:11049-11060. [PMID: 36097117 DOI: 10.1007/s11033-022-07782-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 07/07/2022] [Indexed: 11/30/2022]
Abstract
For more than seven decades, methotrexate has been used all over the world for treatment of different diseases such as: cancer, autoimmune diseases, and rheumatoid arthritis. Several studies have addressed its formula, efficacy, and delivery methods in recent years. These studies have been focused on the effectiveness of different nanoparticles on drug delivery, delivery of the drug to the target cells, and attenuation of harm to the host cell. Whereas, the main usages of methotrexate are in cancer treatment field, this review provided a brief perspective into using different nanoparticles and their role in the treatment of different cancers.
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Affiliation(s)
- Hanifeh Shariatifar
- Health Products Safety Research Center, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Fateme Ranjbarian
- Student Research Committee, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Fahimeh Hajiahmadi
- Department of Medical Imaging Technology (Molecular Imaging), School of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Alireza Farasat
- Cellular and Molecular Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran.
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8
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Zheng H, Petrella JR, Doraiswamy PM, Lin G, Hao W. Data-driven causal model discovery and personalized prediction in Alzheimer's disease. NPJ Digit Med 2022; 5:137. [PMID: 36076010 PMCID: PMC9458727 DOI: 10.1038/s41746-022-00632-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 06/16/2022] [Indexed: 12/03/2022] Open
Abstract
With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.
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Affiliation(s)
- Haoyang Zheng
- School of Mechanical Engineering, Purdue University, West Lafayette, 47907, IN, USA
| | - Jeffrey R Petrella
- Department of Radiology, Duke University Health System, Durham, 27710, NC, USA
| | - P Murali Doraiswamy
- Departments of Psychiatry and Medicine, Duke University School of Medicine and Duke Institute for Brain Sciences, Durham, 27710, NC, USA
| | - Guang Lin
- School of Mechanical Engineering, Purdue University, West Lafayette, 47907, IN, USA. .,Department of Mathematics, Purdue University, West Lafayette, 47907, IN, USA.
| | - Wenrui Hao
- Department of Mathematics, Penn State University, University Park, 16802, PA, USA
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Mohammad Mirzaei N, Tatarova Z, Hao W, Changizi N, Asadpoure A, Zervantonakis IK, Hu Y, Chang YH, Shahriyari L. A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice. J Pers Med 2022; 12:807. [PMID: 35629230 PMCID: PMC9145520 DOI: 10.3390/jpm12050807] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 02/04/2023] Open
Abstract
The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells' location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells' diffusion and immune cells' influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes' diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer.
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Affiliation(s)
- Navid Mohammad Mirzaei
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (Y.H.)
| | - Zuzana Tatarova
- Department of Radiology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Wenrui Hao
- Department of Mathematics, The Pennsylvania State University, University Park, PA 16802, USA;
| | - Navid Changizi
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, MA 02747, USA; (N.C.); (A.A.)
| | - Alireza Asadpoure
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, MA 02747, USA; (N.C.); (A.A.)
| | - Ioannis K. Zervantonakis
- Department of Bioengineering, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15219, USA;
| | - Yu Hu
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (Y.H.)
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA;
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (Y.H.)
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Mohammad Mirzaei N, Changizi N, Asadpoure A, Su S, Sofia D, Tatarova Z, Zervantonakis IK, Chang YH, Shahriyari L. Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model. PLoS Comput Biol 2022; 18:e1009953. [PMID: 35294447 PMCID: PMC8959189 DOI: 10.1371/journal.pcbi.1009953] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/28/2022] [Accepted: 02/22/2022] [Indexed: 02/07/2023] Open
Abstract
The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model's parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.
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Affiliation(s)
- Navid Mohammad Mirzaei
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Navid Changizi
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, Massachusetts, United States of America
| | - Alireza Asadpoure
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, Massachusetts, United States of America
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Dilruba Sofia
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Zuzana Tatarova
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon, United States of America
| | - Ioannis K. Zervantonakis
- Department of Bioengineering, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Young Hwan Chang
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon, United States of America
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
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11
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Gao T, Yuan D, He B, Gao Y, Liu C, Sun H, Nie J, Wang S, Nie Z. Identification of autophagy related genes in predicting the prognosis and aiding 5- fluorouracil therapy of colorectal cancer. Heliyon 2022; 8:e09033. [PMID: 35284678 PMCID: PMC8904229 DOI: 10.1016/j.heliyon.2022.e09033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/03/2021] [Accepted: 02/24/2022] [Indexed: 11/30/2022] Open
Abstract
The emergence of 5-Fluorouracil (5-FU) resistance is the barrier to effective clinical outcomes for colorectal cancer (CRC) patients. Autophagy was found to be involved in protecting tumor cells from 5-FU. However, the specific role of autophagy-related genes in CRC 5-FU resistance remains unclear. In this study, HSPB8 among 34 differentially expressed ARGs in CRC was identified to be the hub ARGs in 5-FU resistant which was down-regulated in CRC samples when compared with normal samples but up-regulated in CRC samples with relatively higher lymphatic invasion, later stages and poor prognosis of CRC. Mechanistic analysis demonstrated that due to the recruitment of CAFs, HSPB8 expression was enhanced in CRC cells so that HSPB8 could act together with its co-chaperone BAG3 in autophagy drived 5-FU resistance. Furthermore, the augmented expression level of HSPB8 was found to be significantly correlated to the immune cell infiltration such as Treg cells, macrophages, monocyte and dendritic cells and so on. Our results suggested CAFs driving HSPB8 induced CRC 5-FU resistance by promoting tumor autophagy would provide a new strategy in seeking potential CRC therapeutic target.
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Affiliation(s)
- Tianyi Gao
- Department of Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
| | - Dan Yuan
- Department of Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
| | - Bangshun He
- Department of Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
| | - Yingdong Gao
- Department of Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
| | - Caidong Liu
- Department of Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
| | - Huilin Sun
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
| | - Junjie Nie
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
| | - Shukui Wang
- Department of Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
- Central Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
- Jiangsu Collaborative Innovation Center on Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Zhenlin Nie
- Department of Clinical Laboratory, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing 210006, Jiangsu, China
- Corresponding author.
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12
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Mohammad Mirzaei N, Su S, Sofia D, Hegarty M, Abdel-Rahman MH, Asadpoure A, Cebulla CM, Chang YH, Hao W, Jackson PR, Lee AV, Stover DG, Tatarova Z, Zervantonakis IK, Shahriyari L. A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration. J Pers Med 2021; 11:jpm11101031. [PMID: 34683171 PMCID: PMC8540934 DOI: 10.3390/jpm11101031] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023] Open
Abstract
Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.
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Affiliation(s)
- Navid Mohammad Mirzaei
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
| | - Dilruba Sofia
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
| | - Maura Hegarty
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
| | - Mohamed H. Abdel-Rahman
- Department of Ophthalmology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; (M.H.A.-R.); (C.M.C.); (D.G.S.)
| | - Alireza Asadpoure
- Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, MA 02747, USA;
| | - Colleen M. Cebulla
- Department of Ophthalmology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; (M.H.A.-R.); (C.M.C.); (D.G.S.)
| | - Young Hwan Chang
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, OR 97239, USA; (Y.H.C.); (Z.T.)
| | - Wenrui Hao
- Department of Mathematics, The Pennsylvania State University, University Park, PA 16802, USA;
| | - Pamela R. Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, Phoenix, AZ 85054, USA;
| | - Adrian V. Lee
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA 15219, USA;
| | - Daniel G. Stover
- Department of Ophthalmology, Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; (M.H.A.-R.); (C.M.C.); (D.G.S.)
| | - Zuzana Tatarova
- Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, OR 97239, USA; (Y.H.C.); (Z.T.)
| | - Ioannis K. Zervantonakis
- Department of Bioengineering, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15219, USA;
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA; (N.M.M.); (S.S.); (D.S.); (M.H.)
- Correspondence:
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Investigating Optimal Chemotherapy Options for Osteosarcoma Patients through a Mathematical Model. Cells 2021; 10:cells10082009. [PMID: 34440778 PMCID: PMC8394778 DOI: 10.3390/cells10082009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary Osteosarcoma is a rare type of cancer with poor prognoses. However, to the best of our knowledge, there are no mathematical models that study the impact of chemotherapy treatments on the osteosarcoma microenvironment. In this study, we developed a data driven mathematical model to analyze the dynamics of the important players in three groups of osteosarcoma tumors with distinct immune patterns in the presence of the most common chemotherapy drugs. The results indicate that the treatments’ start times and optimal dosages depend on the unique growth rate of the tumor, which implies the necessity of personalized medicine. Furthermore, the developed model can be extended by others to build models that can recommend individual-specific optimal dosages. Abstract Since all tumors are unique, they may respond differently to the same treatments. Therefore, it is necessary to study their characteristics individually to find their best treatment options. We built a mathematical model for the interactions between the most common chemotherapy drugs and the osteosarcoma microenvironments of three clusters of tumors with unique immune profiles. We then investigated the effects of chemotherapy with different treatment regimens and various treatment start times on the behaviors of immune and cancer cells in each cluster. Saliently, we suggest the optimal drug dosages for the tumors in each cluster. The results show that abundances of dendritic cells and HMGB1 increase when drugs are given and decrease when drugs are absent. Populations of helper T cells, cytotoxic cells, and IFN-γ grow, and populations of cancer cells and other immune cells shrink during treatment. According to the model, the MAP regimen does a good job at killing cancer, and is more effective than doxorubicin and cisplatin combined or methotrexate alone. The results also indicate that it is important to consider the tumor’s unique growth rate when deciding the treatment details, as fast growing tumors need early treatment start times and high dosages.
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