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Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [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: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
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Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
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2
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Qin Y, Huo M, Liu X, Li SC. Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy. Front Immunol 2024; 15:1368749. [PMID: 38524135 PMCID: PMC10957591 DOI: 10.3389/fimmu.2024.1368749] [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: 01/11/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.
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Affiliation(s)
- Yurong Qin
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Miaozhe Huo
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Xingwu Liu
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
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3
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Priyankha S, Rajapandian V, Palanisamy K, Esther Rubavathy SM, Thilagavathi R, Selvam C, Prakash M. Identification of indole-based natural compounds as inhibitors of PARP-1 against triple-negative breast cancer: a computational study. J Biomol Struct Dyn 2024; 42:2667-2680. [PMID: 37154583 DOI: 10.1080/07391102.2023.2208215] [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: 01/04/2023] [Accepted: 04/19/2023] [Indexed: 05/10/2023]
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive kind of breast cancer known to mankind. It is a heterogeneous disease that is formed due to the missing estrogen, progesterone and human epidermal growth factor 2 receptors. Poly(ADP-ribose) polymerase-1 (PARP-1) protein helps in the development of TNBC by repairing the cancer cells, which proliferate and spread metastatically. To determine the potential PARP-1 inhibitors (PARPi), 0.2 million natural products from Universal Natural Product Database were screened using molecular docking and six hit compounds were selected based on their binding affinity towards PARP-1. The bio-availability and drug-like properties of these natural products were evaluated using ADMET analysis. Molecular dynamics simulations were conducted for these complexes for 200 ns to examine their structural stability and dynamic behaviour and further compared with the complex of talazoparib (TALA), an FDA-approved PARPi. Using MM/PBSA calculations, we conclude that the complexes HIT-3 and HIT-5 (-25.64 and -23.14 kcal/mol, respectively) show stronger binding energies with PARP-1 than TALA with PARP-1 (-10.74 kcal/mol). Strong interactions were observed between the compounds and hotspot residues, Asp770, Ala880, Tyr889, Tyr896, Ala898, Asp899 and Tyr907, of PARP-1 due to the existence of various types of non-covalent interactions between the compounds and PARP-1. This research offers critical information about PARPi, which could potentially be incorporated into the treatment of TNBC. Moreover, these findings were validated by comparing them with an FDA-approved PARPi.
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Affiliation(s)
- Sridhar Priyankha
- Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Varatharaj Rajapandian
- Department of Chemistry, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, Tamil Nadu, India
| | - Kandhan Palanisamy
- Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - S M Esther Rubavathy
- Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Ramasamy Thilagavathi
- Department of Biotechnology, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, India
| | - Chelliah Selvam
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Texas Southern University, Houston, Texas, USA
| | - Muthuramalingam Prakash
- Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
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Zhang S, Deshpande A, Verma BK, Wang H, Mi H, Yuan L, Ho WJ, Jaffee EM, Zhu Q, Anders RA, Yarchoan M, Kagohara LT, Fertig EJ, Popel AS. Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.11.553000. [PMID: 37645761 PMCID: PMC10462044 DOI: 10.1101/2023.08.11.553000] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.
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Affiliation(s)
- Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Atul Deshpande
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Babita K. Verma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Long Yuan
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Immunology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Elizabeth M. Jaffee
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Qingfeng Zhu
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert A. Anders
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mark Yarchoan
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Luciane T. Kagohara
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Elana J. Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Bloomberg-Kimmel Immunotherapy Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Convergence Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Jointly supervised research
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Jointly supervised research
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5
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Arulraj T, Wang H, Emens LA, Santa-Maria CA, Popel AS. A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition. SCIENCE ADVANCES 2023; 9:eadg0289. [PMID: 37390206 PMCID: PMC10313177 DOI: 10.1126/sciadv.adg0289] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/26/2023] [Indexed: 07/02/2023]
Abstract
Triple-negative breast cancer (TNBC), a highly metastatic breast cancer subtype, has limited treatment options. While a small number of patients attain clinical benefit with single-agent checkpoint inhibitors, identifying these patients before the therapy remains challenging. Here, we developed a transcriptome-informed quantitative systems pharmacology model of metastatic TNBC by integrating heterogenous metastatic tumors. In silico clinical trial with an anti-PD-1 drug, pembrolizumab, predicted that several features, such as the density of antigen-presenting cells, the fraction of cytotoxic T cells in lymph nodes, and the richness of cancer clones in tumors, could serve individually as biomarkers but had a higher predictive power as combinations of two biomarkers. We showed that PD-1 inhibition neither consistently enhanced all antitumorigenic factors nor suppressed all protumorigenic factors but ultimately reduced the tumor carrying capacity. Collectively, our predictions suggest several candidate biomarkers that might effectively predict the response to pembrolizumab monotherapy and potential therapeutic targets to develop treatment strategies for metastatic TNBC.
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Affiliation(s)
- Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Leisha A. Emens
- University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, 15213, USA
| | - Cesar A. Santa-Maria
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Neves Rebello Alves L, Dummer Meira D, Poppe Merigueti L, Correia Casotti M, do Prado Ventorim D, Ferreira Figueiredo Almeida J, Pereira de Sousa V, Cindra Sant'Ana M, Gonçalves Coutinho da Cruz R, Santos Louro L, Mendonça Santana G, Erik Santos Louro T, Evangelista Salazar R, Ribeiro Campos da Silva D, Stefani Siqueira Zetum A, Silva Dos Reis Trabach R, Imbroisi Valle Errera F, de Paula F, de Vargas Wolfgramm Dos Santos E, Fagundes de Carvalho E, Drumond Louro I. Biomarkers in Breast Cancer: An Old Story with a New End. Genes (Basel) 2023; 14:1364. [PMID: 37510269 PMCID: PMC10378988 DOI: 10.3390/genes14071364] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.
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Affiliation(s)
- Lyvia Neves Rebello Alves
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Débora Dummer Meira
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Luiza Poppe Merigueti
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Matheus Correia Casotti
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Diego do Prado Ventorim
- Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo (Ifes), Cariacica 29150-410, ES, Brazil
| | - Jucimara Ferreira Figueiredo Almeida
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Valdemir Pereira de Sousa
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Marllon Cindra Sant'Ana
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Rahna Gonçalves Coutinho da Cruz
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Luana Santos Louro
- Centro de Ciências da Saúde, Curso de Medicina, Universidade Federal do Espírito Santo (UFES), Vitória 29090-040, ES, Brazil
| | - Gabriel Mendonça Santana
- Centro de Ciências da Saúde, Curso de Medicina, Universidade Federal do Espírito Santo (UFES), Vitória 29090-040, ES, Brazil
| | - Thomas Erik Santos Louro
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Vitória 29027-502, ES, Brazil
| | - Rhana Evangelista Salazar
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Danielle Ribeiro Campos da Silva
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Aléxia Stefani Siqueira Zetum
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Raquel Silva Dos Reis Trabach
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
| | - Flávia Imbroisi Valle Errera
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Flávia de Paula
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Eldamária de Vargas Wolfgramm Dos Santos
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcântara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20551-030, RJ, Brazil
| | - Iúri Drumond Louro
- Núcleo de Genética Humana e Molecular, Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo (UFES), Vitória 29075-910, ES, Brazil
- Programa de Pós-Graduação em Biotecnologia, Universidade Federal do Espírito Santo, Vitória 29047-105, ES, Brazil
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Nikfar M, Mi H, Gong C, Kimko H, Popel AS. Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model. Cancers (Basel) 2023; 15:2750. [PMID: 37345087 DOI: 10.3390/cancers15102750] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 06/23/2023] Open
Abstract
Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.
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Affiliation(s)
- Mehdi Nikfar
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Haoyang Mi
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Chang Gong
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Waltham, MA 02451, USA
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD 20878, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Sidney Kimmel Comprehensive Cancer Center, Department of Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
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8
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Mi H, Sivagnanam S, Betts CB, Liudahl SM, Jaffee EM, Coussens LM, Popel AS. Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers. Cancer Res 2022; 82:4359-4372. [PMID: 36112643 PMCID: PMC9716253 DOI: 10.1158/0008-5472.can-22-1190] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 08/04/2022] [Accepted: 09/12/2022] [Indexed: 01/24/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with poor 5-year survival rates, necessitating identification of novel therapeutic targets. Elucidating the biology of the tumor immune microenvironment (TiME) can provide vital insights into mechanisms of tumor progression. In this study, we developed a quantitative image processing platform to analyze sequential multiplexed IHC data from archival PDAC tissue resection specimens. A 27-plex marker panel was employed to simultaneously phenotype cell populations and their functional states, followed by a computational workflow to interrogate the immune contextures of the TiME in search of potential biomarkers. The PDAC TiME reflected a low-immunogenic ecosystem with both high intratumoral and intertumoral heterogeneity. Spatial analysis revealed that the relative distance between IL10+ myelomonocytes, PD-1+ CD4+ T cells, and granzyme B+ CD8+ T cells correlated significantly with survival, from which a spatial proximity signature termed imRS was derived that correlated with PDAC patient survival. Furthermore, spatial enrichment of CD8+ T cells in lymphoid aggregates was also linked to improved survival. Altogether, these findings indicate that the PDAC TiME, generally considered immuno-dormant or immunosuppressive, is a spatially nuanced ecosystem orchestrated by ordered immune hierarchies. This new understanding of spatial complexity may guide novel treatment strategies for PDAC. SIGNIFICANCE Quantitative image analysis of PDAC specimens reveals intertumoral and intratumoral heterogeneity of immune populations and identifies spatial immune architectures that are significantly associated with disease prognosis.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Corresponding Authors: Haoyang Mi, Johns Hopkins University, Baltimore, MD 21205. Phone: 410-528-3768; E-mail: ; and Lisa M. Coussens,
| | | | - Courtney B. Betts
- Department of Cell, Development, and Cancer Biology, Oregon Health and Science University, Portland, Oregon
| | - Shannon M. Liudahl
- Department of Cell, Development, and Cancer Biology, Oregon Health and Science University, Portland, Oregon
| | - Elizabeth M. Jaffee
- Skip Viragh Center for Pancreatic Cancer, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins Medicine, Baltimore, Maryland.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Lisa M. Coussens
- Department of Cell, Development, and Cancer Biology, Oregon Health and Science University, Portland, Oregon.,Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, Oregon.,Knight Cancer Institute, Portland, Oregon.,Corresponding Authors: Haoyang Mi, Johns Hopkins University, Baltimore, MD 21205. Phone: 410-528-3768; E-mail: ; and Lisa M. Coussens,
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
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9
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Furukawa N, Stearns V, Santa-Maria CA, Popel AS. The tumor microenvironment and triple-negative breast cancer aggressiveness: shedding light on mechanisms and targeting. Expert Opin Ther Targets 2022; 26:1041-1056. [PMID: 36657483 PMCID: PMC10189896 DOI: 10.1080/14728222.2022.2170779] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
INTRODUCTION In contrast to other breast cancer subtypes, there are currently limited options of targeted therapies for triple-negative breast cancer (TNBC). Immense research has demonstrated that not only cancer cells but also stromal cells and immune cells in the tumor microenvironment (TME) play significant roles in the progression of TNBC. It is thus critical to understand the components of the TME of TNBC and the interactions between the various cell populations. AREAS COVERED The components of the TME of TNBC identified by single-cell technologies are reviewed. Furthermore, the molecular interactions between the cells and the potential therapeutic targets contributing to the progression of TNBC are discussed. EXPERT OPINION Single-cell omics studies have contributed to the classification of cells in the TME and the identification of important cell types involved in the progression and the treatment of the tumor. The interactions between cancer cells and stromal cells/immune cells in the TME have led to the discovery of potential therapeutic targets. Experimental data with spatial and temporal resolution will further boost the understanding of the TME of TNBC.
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Affiliation(s)
- Natsuki Furukawa
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Vered Stearns
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Cesar A. Santa-Maria
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, USA
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA
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10
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Sové RJ, Verma BK, Wang H, Ho WJ, Yarchoan M, Popel AS. Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model. J Immunother Cancer 2022; 10:e005414. [PMID: 36323435 PMCID: PMC9639136 DOI: 10.1136/jitc-2022-005414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is the third-leading cause of cancer-related death worldwide. Most patients with HCC are diagnosed at an advanced stage, and the median survival for patients with advanced HCC treated with modern systemic therapy is less than 2 years. This leaves the advanced stage patients with limited treatment options. Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or its ligand, are widely used in the treatment of HCC and are associated with durable responses in a subset of patients. ICIs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) also have clinical activity in HCC. Combination therapy of nivolumab (anti-PD-1) and ipilimumab (anti-CTLA-4) is the first treatment option for HCC to be approved by Food and Drug Administration that targets more than one immune checkpoints. METHODS In this study, we used the framework of quantitative systems pharmacology (QSP) to perform a virtual clinical trial for nivolumab and ipilimumab in HCC patients. Our model incorporates detailed biological mechanisms of interactions of immune cells and cancer cells leading to antitumor response. To conduct virtual clinical trial, we generate virtual patient from a cohort of 5,000 proposed patients by extending recent algorithms from literature. The model was calibrated using the data of the clinical trial CheckMate 040 (ClinicalTrials.gov number, NCT01658878). RESULTS Retrospective analyses were performed for different immune checkpoint therapies as performed in CheckMate 040. Using machine learning approach, we predict the importance of potential biomarkers for immune blockade therapies. CONCLUSIONS This is the first QSP model for HCC with ICIs and the predictions are consistent with clinically observed outcomes. This study demonstrates that using a mechanistic understanding of the underlying pathophysiology, QSP models can facilitate patient selection and design clinical trials with improved success.
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Affiliation(s)
- Richard J Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Babita K Verma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Won Jin Ho
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mark Yarchoan
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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11
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Wang H, Zhao C, Santa-Maria CA, Emens LA, Popel AS. Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer. iScience 2022; 25:104702. [PMID: 35856032 PMCID: PMC9287616 DOI: 10.1016/j.isci.2022.104702] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/05/2022] [Accepted: 06/27/2022] [Indexed: 11/07/2022] Open
Abstract
Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation. A mechanistic model of quantitative systems pharmacology in immuno-oncology Dynamics of tumor-associated macrophages are integrated into our previous work Conducting in silico clinical trials to predict clinical response to cancer therapy
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu211166, China
| | - Cesar A Santa-Maria
- Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA
| | - Leisha A Emens
- University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA
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12
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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] [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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13
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Hobbs EA, Chen N, Kuriakose A, Bonefas E, Lim B. Prognostic/predictive markers in systemic therapy resistance and metastasis in breast cancer. Ther Adv Med Oncol 2022; 14:17588359221112698. [PMID: 35860831 PMCID: PMC9290149 DOI: 10.1177/17588359221112698] [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: 04/17/2022] [Accepted: 06/23/2022] [Indexed: 01/12/2023] Open
Abstract
Breast cancer is a highly heterogeneous group of diseases posing a significant challenge in biomarker-driven research and the development of effective targeted therapies. Especially the treatment of metastatic breast cancer poses even more challenges, as we still lose more than 42,000 women and men each year in the United States alone. New biological insight helps to improve breast cancer treatment through early detection, adaptation to chemotherapy resistance, and tailoring to find the right size of care. This review focuses on existing and new areas of predictive biomarkers under development to tailor the management of breast cancer and the application of integrative approaches that have resulted in the promising candidate biomarker discovery. Furthermore, we review new methods to detect metastatic progression using imaging, and blood-based assays. We hope to increase the attention and awareness of a new generation of therapeutic development strategies in metastatic breast cancer.
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Affiliation(s)
- Evthokia A. Hobbs
- Hematology and Oncology, Oregon Health & Science University, Portland, OR, USA
| | - Natalie Chen
- Hematology and Oncology, Baylor College of Medicine, Houston, TX, USA
| | - Alphi Kuriakose
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
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14
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Prybutok AN, Cain JY, Leonard JN, Bagheri N. Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems. Curr Opin Biotechnol 2022; 75:102704. [DOI: 10.1016/j.copbio.2022.102704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/27/2022] [Accepted: 02/06/2022] [Indexed: 11/03/2022]
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15
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Luo MC, Nikolopoulou E, Gevertz JL. From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers. Front Oncol 2022; 12:793908. [PMID: 35574407 PMCID: PMC9097280 DOI: 10.3389/fonc.2022.793908] [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: 10/12/2021] [Accepted: 03/09/2022] [Indexed: 11/13/2022] Open
Abstract
An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.
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Affiliation(s)
- Michael C Luo
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, United States
| | - Elpiniki Nikolopoulou
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| | - Jana L Gevertz
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, United States
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16
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Mi H, Ho WJ, Yarchoan M, Popel AS. Multi-Scale Spatial Analysis of the Tumor Microenvironment Reveals Features of Cabozantinib and Nivolumab Efficacy in Hepatocellular Carcinoma. Front Immunol 2022; 13:892250. [PMID: 35634309 PMCID: PMC9136005 DOI: 10.3389/fimmu.2022.892250] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Background Concomitant inhibition of vascular endothelial growth factor (VEGF) and programmed cell death protein 1 (PD-1) or its ligand PD-L1 is a standard of care for patients with advanced hepatocellular carcinoma (HCC), but only a minority of patients respond, and responses are usually transient. Understanding the effects of therapies on the tumor microenvironment (TME) can provide insights into mechanisms of therapeutic resistance. Methods 14 patients with HCC were treated with the combination of cabozantinib and nivolumab through the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Among them, 12 patients (5 responders + 7 non-responders) underwent successful margin negative resection and are subjects to tissue microarray (TMA) construction containing 37 representative tumor region cores. Using the TMAs, we performed imaging mass cytometry (IMC) with a panel of 27-cell lineage and functional markers. All multiplexed images were then segmented to generate a single-cell dataset that enables (1) tumor-immune compartment analysis and (2) cell community analysis based on graph-embedding methodology. Results from these hierarchies are merged into response-associated biological process patterns. Results Image processing on 37 multiplexed-images discriminated 59,453 cells and was then clustered into 17 cell types. Compartment analysis showed that at immune-tumor boundaries from NR, PD-L1 level on tumor cells is significantly higher than remote regions; however, Granzyme B expression shows the opposite pattern. We also identify that the close proximity of CD8+ T cells to arginase 1hi (Arg1hi) macrophages, rather than CD4+ T cells, is a salient feature of the TME in non-responders. Furthermore, cell community analysis extracted 8 types of cell-cell interaction networks termed cellular communities (CCs). We observed that in non-responders, macrophage-enriched CC (MCC) and lymphocyte-enriched CC (LCC) strongly communicate with tumor CC, whereas in responders, such communications were undermined by the engagement between MCC and LCC. Conclusion These results demonstrate the feasibility of a novel application of multiplexed image analysis that is broadly applicable to quantitative analysis of pathology specimens in immuno-oncology and provides further evidence that CD163-Arg1hi macrophages may be a therapeutic target in HCC. The results also provide critical information for the development of mechanistic quantitative systems pharmacology models aimed at predicting outcomes of clinical trials.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: Haoyang Mi, ; Mark Yarchoan,
| | - Won Jin Ho
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mark Yarchoan
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: Haoyang Mi, ; Mark Yarchoan,
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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17
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Zhang Y, Wang H, Oliveira RHM, Zhao C, Popel AS. Systems biology of angiogenesis signaling: Computational models and omics. WIREs Mech Dis 2021; 14:e1550. [PMID: 34970866 PMCID: PMC9243197 DOI: 10.1002/wsbm.1550] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 01/10/2023]
Abstract
Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases. This article is categorized under:Cardiovascular Diseases > Computational Models Cancer > Computational Models
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Affiliation(s)
- Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Rebeca Hannah M Oliveira
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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18
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Shrihastini V, Muthuramalingam P, Adarshan S, Sujitha M, Chen JT, Shin H, Ramesh M. Plant Derived Bioactive Compounds, Their Anti-Cancer Effects and In Silico Approaches as an Alternative Target Treatment Strategy for Breast Cancer: An Updated Overview. Cancers (Basel) 2021; 13:cancers13246222. [PMID: 34944840 PMCID: PMC8699774 DOI: 10.3390/cancers13246222] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 12/13/2022] Open
Abstract
Cancer is one of the most common malignant diseases that occur worldwide, among which breast cancer is the second leading cause of death in women. The subtypes are associated with differences in the outcome and were selected for treatments according to the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor. Triple-negative breast cancer, one of the subtypes of breast cancer, is difficult to treat and can even lead to death. If breast cancer is not treated during the initial stages, it may spread to nearby organs, a process called metastasis, through the blood or lymph system. For in vitro studies, MCF-7, MDA-MB-231, MDA-MB-468, and T47B are the most commonly used breast cancer cell lines. Clinically, chemotherapy and radiotherapy are usually expensive and can also cause side effects. To overcome these issues, medicinal plants could be the best alternative for chemotherapeutic drugs with fewer side effects and cost-effectiveness. Furthermore, the genes involved in breast cancer can be regulated and synergized with signaling molecules to suppress the proliferation of breast cancer cells. In addition, nanoparticles encapsulating (nano-encapsulation) medicinal plant extracts showed a significant reduction in the apoptotic and cytotoxic activities of breast cancer cells. This present review mainly speculates an overview of the native medicinal plant derived anti-cancerous compounds with its efficiency, types and pathways involved in breast cancer along with its genes, the mechanism of breast cancer brain metastasis, chemoresistivity and its mechanism, bioinformatics approaches which could be an effective alternative for drug discovery.
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Affiliation(s)
- Vijayakumar Shrihastini
- Department of Biotechnology, Sri Shakthi Institute of Engineering and Technology, Coimbatore 641062, Tamil Nadu, India; (V.S.); (M.S.)
| | - Pandiyan Muthuramalingam
- Department of Biotechnology, Sri Shakthi Institute of Engineering and Technology, Coimbatore 641062, Tamil Nadu, India; (V.S.); (M.S.)
- Correspondence: (P.M.); (J.-T.C.)
| | - Sivakumar Adarshan
- Department of Biotechnology, Science Campus, Alagappa University, Karaikudi 630003, Tamil Nadu, India; (S.A.); (M.R.)
| | - Mariappan Sujitha
- Department of Biotechnology, Sri Shakthi Institute of Engineering and Technology, Coimbatore 641062, Tamil Nadu, India; (V.S.); (M.S.)
| | - Jen-Tsung Chen
- Department of Life Sciences, National University of Kaohsiung, Kaohsiung 811, Taiwan
- Correspondence: (P.M.); (J.-T.C.)
| | - Hyunsuk Shin
- Department of Horticultural Sciences, Gyeongsang National University, Jinju 52725, Korea;
| | - Manikandan Ramesh
- Department of Biotechnology, Science Campus, Alagappa University, Karaikudi 630003, Tamil Nadu, India; (S.A.); (M.R.)
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