1
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Singh D, Paquin D. Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:6659-6693. [PMID: 39176414 DOI: 10.3934/mbe.2024292] [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: 08/24/2024]
Abstract
Tumor growth dynamics serve as a critical aspect of understanding cancer progression and treatment response to mitigate one of the most pressing challenges in healthcare. The in silico approach to understanding tumor behavior computationally provides an efficient, cost-effective alternative to wet-lab examinations and are adaptable to different environmental conditions, time scales, and unique patient parameters. As a result, this paper explored modeling of free tumor growth in cancer, surveying contemporary literature on continuum, discrete, and hybrid approaches. Factors like predictive power and high-resolution simulation competed against drawbacks like simulation load and parameter feasibility in these models. Understanding tumor behavior in different scenarios and contexts became the first step in advancing cancer research and revolutionizing clinical outcomes.
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Affiliation(s)
- Dashmi Singh
- Stanford University Online High School, 415 Broadway Academy Hall, Floor 2, 8853,415 Broadway, Redwood City, CA 94063, USA
| | - Dana Paquin
- Stanford University Online High School, 415 Broadway Academy Hall, Floor 2, 8853,415 Broadway, Redwood City, CA 94063, USA
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2
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Nikmaneshi MR, Baish JW, Zhou H, Padera TP, Munn LL. Transport Barriers Influence the Activation of Anti-Tumor Immunity: A Systems Biology Analysis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304076. [PMID: 37949675 PMCID: PMC10754116 DOI: 10.1002/advs.202304076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/07/2023] [Indexed: 11/12/2023]
Abstract
Effective anti-cancer immune responses require activation of one or more naïve T cells. If the correct naïve T cell encounters its cognate antigen presented by an antigen presenting cell, then the T cell can activate and proliferate. Here, mathematical modeling is used to explore the possibility that immune activation in lymph nodes is a rate-limiting step in anti-cancer immunity and can affect response rates to immune checkpoint therapy. The model provides a mechanistic framework for optimizing cancer immunotherapy and developing testable solutions to unleash anti-tumor immune responses for more patients with cancer. The results show that antigen production rate and trafficking of naïve T cells into the lymph nodes are key parameters and that treatments designed to enhance tumor antigen production can improve immune checkpoint therapies. The model underscores the potential of radiation therapy in augmenting tumor immunogenicity and neoantigen production for improved ICB therapy, while emphasizing the need for careful consideration in cases where antigen levels are already sufficient to avoid compromising the immune response.
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Affiliation(s)
- Mohammad R. Nikmaneshi
- Department of Radiation OncologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA02114USA
| | - James W. Baish
- Biomedical EngineeringBucknell UniversityLewisburgPA17837USA
| | - Hengbo Zhou
- Department of Radiation OncologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA02114USA
| | - Timothy P. Padera
- Department of Radiation OncologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA02114USA
| | - Lance L. Munn
- Department of Radiation OncologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA02114USA
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3
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Du J, Zhou Y, Jin L, Sheng K. Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system. PLoS One 2023; 18:e0288721. [PMID: 37463167 DOI: 10.1371/journal.pone.0288721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop Gell (GPU Cell), a fast and memory-efficient open-source GPU-based hybrid computational modeling platform for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement.
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Affiliation(s)
- Jiayi Du
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Yu Zhou
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Lihua Jin
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Ke Sheng
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, California, United States of America
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4
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Syed M, Cagely M, Dogra P, Hollmer L, Butner JD, Cristini V, Koay EJ. Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2023; 15:e1855. [PMID: 36148978 DOI: 10.1002/wnan.1855] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022]
Abstract
The field of oncology has transformed with the advent of immunotherapies. The standard of care for multiple cancers now includes novel drugs that target key checkpoints that function to modulate immune responses, enabling the patient's immune system to elicit an effective anti-tumor response. While these immune-based approaches can have dramatic effects in terms of significantly reducing tumor burden and prolonging survival for patients, the therapeutic approach remains active only in a minority of patients and is often not durable. Multiple biological investigations have identified key markers that predict response to the most common form of immunotherapy-immune checkpoint inhibitors (ICI). These biomarkers help enrich patients for ICI but are not 100% predictive. Understanding the complex interactions of these biomarkers with other pathways and factors that lead to ICI resistance remains a major goal. Principles of oncophysics-the idea that cancer can be described as a multiscale physical aberration-have shown promise in recent years in terms of capturing the essence of the complexities of ICI interactions. Here, we review the biological knowledge of mechanisms of ICI action and how these are incorporated into modern oncophysics-based mathematical models. Building on the success of oncophysics-based mathematical models may help to discover new, rational methods to engineer immunotherapy for patients in the future. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.
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Affiliation(s)
- Mustafa Syed
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Matthew Cagely
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA
| | - Lauren Hollmer
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Eugene J Koay
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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5
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Rebbeck CA, Xian J, Bornelöv S, Geradts J, Hobeika A, Geiger H, Alvarez JF, Rozhkova E, Nicholls A, Robine N, Lyerly HK, Hannon GJ. Gene expression signatures of individual ductal carcinoma in situ lesions identify processes and biomarkers associated with progression towards invasive ductal carcinoma. Nat Commun 2022; 13:3399. [PMID: 35697697 PMCID: PMC9192778 DOI: 10.1038/s41467-022-30573-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/06/2022] [Indexed: 12/27/2022] Open
Abstract
Ductal carcinoma in situ (DCIS) is considered a non-invasive precursor to breast cancer, and although associated with an increased risk of developing invasive disease, many women with DCIS will never progress beyond their in situ diagnosis. The path from normal duct to invasive ductal carcinoma (IDC) is not well understood, and efforts to do so are hampered by the substantial heterogeneity that exists between patients, and even within patients. Here we show gene expression analysis from > 2,000 individually micro-dissected ductal lesions representing 145 patients. Combining all samples into one continuous trajectory we show there is a progressive loss in basal layer integrity heading towards IDC, coupled with two epithelial to mesenchymal transitions, one early and a second coinciding with the convergence of DCIS and IDC expression profiles. We identify early processes and potential biomarkers, including CAMK2N1, MNX1, ADCY5, HOXC11 and ANKRD22, whose reduced expression is associated with the progression of DCIS to invasive breast cancer.
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Affiliation(s)
- Clare A Rebbeck
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
| | - Jian Xian
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Susanne Bornelöv
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Joseph Geradts
- Department of Pathology & Laboratory Medicine, East Carolina University Brody School of Medicine, Greenville, NC, USA
| | - Amy Hobeika
- Department of Surgery, Duke University Medical Center, Durham, NC, USA
| | | | - Jose Franco Alvarez
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Elena Rozhkova
- Department of Dermatology, Boston University School of Medicine, Boston, MA, USA
| | - Ashley Nicholls
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Herbert K Lyerly
- Department of Surgery, Duke University Medical Center, Durham, NC, USA.
| | - Gregory J Hannon
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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6
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Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study. Cell Death Dis 2022; 13:485. [PMID: 35597788 PMCID: PMC9124196 DOI: 10.1038/s41419-022-04939-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/11/2022] [Accepted: 05/11/2022] [Indexed: 12/14/2022]
Abstract
We present a multiscale agent-based model of ductal carcinoma in situ (DCIS) to study how key phenotypic and signaling pathways are involved in the early stages of disease progression. The model includes a phenotypic hierarchy, and key endocrine and paracrine signaling pathways, and simulates cancer ductal growth in a 3D lattice-free domain. In particular, by considering stochastic cell dedifferentiation plasticity, the model allows for study of how dedifferentiation to a more stem-like phenotype plays key roles in the maintenance of cancer stem cell populations and disease progression. Through extensive parameter perturbation studies, we have quantified and ranked how DCIS is sensitive to perturbations in several key mechanisms that are instrumental to early disease development. Our studies reveal that long-term maintenance of multipotent stem-like cell niches within the tumor are dependent on cell dedifferentiation plasticity, and that disease progression will become arrested due to dilution of the multipotent stem-like population in the absence of dedifferentiation. We have identified dedifferentiation rates necessary to maintain biologically relevant multipotent cell populations, and also explored quantitative relationships between dedifferentiation rates and disease progression rates, which may potentially help to optimize the efficacy of emerging anti-cancer stem cell therapeutics.
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7
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Dogra P, Ramírez JR, Butner JD, Peláez MJ, Chung C, Hooda-Nehra A, Pasqualini R, Arap W, Cristini V, Calin GA, Ozpolat B, Wang Z. Translational Modeling Identifies Synergy between Nanoparticle-Delivered miRNA-22 and Standard-of-Care Drugs in Triple-Negative Breast Cancer. Pharm Res 2022; 39:511-528. [PMID: 35294699 PMCID: PMC8986735 DOI: 10.1007/s11095-022-03176-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/21/2022] [Indexed: 12/29/2022]
Abstract
Purpose Downregulation of miRNA-22 in triple-negative breast cancer (TNBC) is associated with upregulation of eukaryotic elongation 2 factor kinase (eEF2K) protein, which regulates tumor growth, chemoresistance, and tumor immunosurveillance. Moreover, exogenous administration of miRNA-22, loaded in nanoparticles to prevent degradation and improve tumor delivery (termed miRNA-22 nanotherapy), to suppress eEF2K production has shown potential as an investigational therapeutic agent in vivo. Methods To evaluate the translational potential of miRNA-22 nanotherapy, we developed a multiscale mechanistic model, calibrated to published in vivo data and extrapolated to the human scale, to describe and quantify the pharmacokinetics and pharmacodynamics of miRNA-22 in virtual patient populations. Results Our analysis revealed the dose-response relationship, suggested optimal treatment frequency for miRNA-22 nanotherapy, and highlighted key determinants of therapy response, from which combination with immune checkpoint inhibitors was identified as a candidate strategy for improving treatment outcomes. More importantly, drug synergy was identified between miRNA-22 and standard-of-care drugs against TNBC, providing a basis for rational therapeutic combinations for improved response Conclusions The present study highlights the translational potential of miRNA-22 nanotherapy for TNBC in combination with standard-of-care drugs. Supplementary Information The online version contains supplementary material available at 10.1007/s11095-022-03176-3.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, 10065, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Maria J Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Anupama Hooda-Nehra
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77230, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York, 10065, USA
| | - George A Calin
- Department of Translational Molecular Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Bulent Ozpolat
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA.
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, 10065, USA.
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8
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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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9
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Reye G, Huang X, Haupt LM, Murphy RJ, Northey JJ, Thompson EW, Momot KI, Hugo HJ. Mechanical Pressure Driving Proteoglycan Expression in Mammographic Density: a Self-perpetuating Cycle? J Mammary Gland Biol Neoplasia 2021; 26:277-296. [PMID: 34449016 PMCID: PMC8566410 DOI: 10.1007/s10911-021-09494-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/05/2021] [Indexed: 12/23/2022] Open
Abstract
Regions of high mammographic density (MD) in the breast are characterised by a proteoglycan (PG)-rich fibrous stroma, where PGs mediate aligned collagen fibrils to control tissue stiffness and hence the response to mechanical forces. Literature is accumulating to support the notion that mechanical stiffness may drive PG synthesis in the breast contributing to MD. We review emerging patterns in MD and other biological settings, of a positive feedback cycle of force promoting PG synthesis, such as in articular cartilage, due to increased pressure on weight bearing joints. Furthermore, we present evidence to suggest a pro-tumorigenic effect of increased mechanical force on epithelial cells in contexts where PG-mediated, aligned collagen fibrous tissue abounds, with implications for breast cancer development attributable to high MD. Finally, we summarise means through which this positive feedback mechanism of PG synthesis may be intercepted to reduce mechanical force within tissues and thus reduce disease burden.
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Affiliation(s)
- Gina Reye
- School of Biomedical Sciences, Gardens Point, Queensland University of Technology (QUT), Kelvin Grove, QLD, 4059, Australia
- Translational Research Institute, Woolloongabba, QLD, Australia
| | - Xuan Huang
- School of Biomedical Sciences, Gardens Point, Queensland University of Technology (QUT), Kelvin Grove, QLD, 4059, Australia
- Translational Research Institute, Woolloongabba, QLD, Australia
| | - Larisa M Haupt
- Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia
| | - Ryan J Murphy
- School of Mathematical Sciences, Gardens Point, Queensland University of Technology (QUT), Kelvin Grove, QLD, Australia
| | - Jason J Northey
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Erik W Thompson
- School of Biomedical Sciences, Gardens Point, Queensland University of Technology (QUT), Kelvin Grove, QLD, 4059, Australia
- Translational Research Institute, Woolloongabba, QLD, Australia
| | - Konstantin I Momot
- School of Chemistry and Physics, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Honor J Hugo
- School of Biomedical Sciences, Gardens Point, Queensland University of Technology (QUT), Kelvin Grove, QLD, 4059, Australia.
- Translational Research Institute, Woolloongabba, QLD, Australia.
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10
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Dogra P, Ruiz-Ramírez J, Sinha K, Butner JD, Peláez MJ, Rawat M, Yellepeddi VK, Pasqualini R, Arap W, Sostman HD, Cristini V, Wang Z. Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model. ACS Pharmacol Transl Sci 2021; 4:248-265. [PMID: 33615177 PMCID: PMC7805603 DOI: 10.1021/acsptsci.0c00183] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Indexed: 12/18/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following parameter quantification with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we ranked model parameters through sensitivity analysis for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination were simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the importance of timing when initiating therapy after infection.
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Affiliation(s)
- Prashant Dogra
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
| | - Javier Ruiz-Ramírez
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
| | - Kavya Sinha
- DeBakey
Heart and Vascular Center, Houston Methodist
Hospital, Houston, Texas 77030, United States
| | - Joseph D. Butner
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
| | - Maria J. Peláez
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
| | - Manmeet Rawat
- Department
of Internal Medicine, University of New
Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Venkata K. Yellepeddi
- Division
of Clinical Pharmacology, Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah 84132, United States
- Department
of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, University of Utah, Salt Lake City, Utah 84112, United States
| | - Renata Pasqualini
- Rutgers
Cancer Institute of New Jersey, Newark, New Jersey 07101, United States
- Department
of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Wadih Arap
- Rutgers
Cancer Institute of New Jersey, Newark, New Jersey 07101, United States
- Department
of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, New Jersey 07103, United States
| | - H. Dirk Sostman
- Weill
Cornell Medicine, New York, New York 10065, United States
- Houston
Methodist Research Institute, Houston, Texas 77030, United States
- Houston
Methodist Academic Institute, Houston, Texas 77030, United States
| | - Vittorio Cristini
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
- Weill
Cornell Medicine, New York, New York 10065, United States
| | - Zhihui Wang
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
- Weill
Cornell Medicine, New York, New York 10065, United States
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11
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Butner JD, Fuentes D, Ozpolat B, Calin GA, Zhou X, Lowengrub J, Cristini V, Wang Z. Corrections to "A multiscale Agent-Based Model of Ductal Carcinoma in Situ". IEEE Trans Biomed Eng 2021; 68:736. [PMID: 33476261 DOI: 10.1109/tbme.2020.3044508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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12
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Butner JD, Wang Z, Elganainy D, Al Feghali KA, Plodinec M, Calin GA, Dogra P, Nizzero S, Ruiz-Ramírez J, Martin GV, Tawbi HA, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V. A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden. Nat Biomed Eng 2021; 5:297-308. [PMID: 33398132 PMCID: PMC8669771 DOI: 10.1038/s41551-020-00662-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/14/2020] [Indexed: 02/06/2023]
Abstract
A large proportion of patients with cancer are unresponsive to treatment with immune checkpoint blockade and other immunotherapies. Here, we report a mathematical model of the time-course of tumour responses to immune-checkpoint inhibitors. The model takes into account intrinsic tumour-growth rates, the rates of immune activation and of tumour–immune-cell interactions, and the efficacy of immune-mediated tumour killing. For 124 patients, four cancer types and two immunotherapy agents, the model reliably described the immune responses and final tumour burden across all different cancers and drug combinations examined. In validation cohorts from four clinical trials of checkpoint inhibitors (with a total of 177 patients), the model accurately stratified the patients according to reduced or increased long-term tumour burden. We also provide model-derived quantitative measures of treatment sensitivity for specific drug–cancer combinations. The model can be used to predict responses to therapy and to quantify specific drug–cancer sensitivities in individual patients.
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Affiliation(s)
- Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. .,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Dalia Elganainy
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Karine A Al Feghali
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marija Plodinec
- Biozentrum and the Swiss Nanoscience Institute, University of Basel, Basel, Switzerland
| | - George A Calin
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Javier Ruiz-Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Geoffrey V Martin
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hussein A Tawbi
- Department of Melanoma Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James W Welsh
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David S Hong
- Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. .,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA. .,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA.
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13
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Lin X, Li X, Lin X. A Review on Applications of Computational Methods in Drug Screening and Design. Molecules 2020; 25:E1375. [PMID: 32197324 PMCID: PMC7144386 DOI: 10.3390/molecules25061375] [Citation(s) in RCA: 233] [Impact Index Per Article: 58.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/16/2020] [Accepted: 03/16/2020] [Indexed: 12/27/2022] Open
Abstract
Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.
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Affiliation(s)
- Xiaoqian Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiu Li
- School of Chemistry and Material Science, Shanxi Normal University, Linfen 041004, China;
| | - Xubo Lin
- Institute of Single Cell Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China;
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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