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Liao KL, Wieler AJ, Gascon PML. Mathematical modeling and analysis of cancer treatment with radiation and anti-PD-L1. Math Biosci 2024; 374:109218. [PMID: 38797473 DOI: 10.1016/j.mbs.2024.109218] [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: 02/10/2024] [Revised: 05/12/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
In cancer treatment, radiation therapy (RT) induces direct tumor cell death due to DNA damage, but it also enhances the deaths of radiosensitive immune cells and is followed by local relapse and up-regulation of immune checkpoint ligand PD-L1. Since the binding between PD-1 and PD-L1 curtails anti-tumor immunities, combining RT and PD-L1 inhibitor, anti-PD-L1, is a potential method to improve the treatment efficacy by RT. Some experiments support this hypothesis by showing that the combination of ionizing irradiation (IR) and anti-PD-L1 improves tumor reduction comparing to the monotherapy of IR or anti-PD-L1. In this work, we create a simplified ODE model to study the order of tumor growths under treatments of IR and anti-PD-L1. Our synergy analysis indicates that both IR and anti-PD-L1 improve the tumor reduction of each other, when IR and anti-PD-L1 are given simultaneously. When giving IR and anti-PD-L1 separately, a high dosage of IR should be given first to efficiently reduce tumor load and then followed by anti-PD-L1 with strong efficacy to maintain the tumor reduction and slow down the relapse. Increasing the duration of anti-PD-L1 improves the tumor reduction, but it cannot prolong the duration that tumor relapses to the level of the control case. Under some simplification, we also prove that the model has an unstable tumor free equilibrium and a locally asymptotically stable tumor persistent equilibrium. Our bifurcation diagram reveals a transition from tumor elimination to tumor persistence, as the tumor growth rate increases. In the tumor persistent case, both anti-PD-L1 and IR can reduce tumor amount in the long term.
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Affiliation(s)
- Kang-Ling Liao
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada.
| | - Adam J Wieler
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
| | - Pedro M Lopez Gascon
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada
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2
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Ng AM, MacKinnon KM, Cook AA, D'Alonzo RA, Rowshanfarzad P, Nowak AK, Gill S, Ebert MA. Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review. Phys Eng Sci Med 2024:10.1007/s13246-024-01458-1. [PMID: 39017990 DOI: 10.1007/s13246-024-01458-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 06/17/2024] [Indexed: 07/18/2024]
Abstract
Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.
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Affiliation(s)
- Allison M Ng
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia
| | - Kelly M MacKinnon
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia
- National Centre for Asbestos Related Diseases, The University of Western Australia, Crawley, WA, Australia
| | - Alistair A Cook
- National Centre for Asbestos Related Diseases, The University of Western Australia, Crawley, WA, Australia
- Institute for Respiratory Health, Institute for Respiratory Health, Perth, WA, Australia
- School of Biomedical Sciences, The University of Western Australia, Crawley, WA, Australia
| | - Rebecca A D'Alonzo
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia
- National Centre for Asbestos Related Diseases, The University of Western Australia, Crawley, WA, Australia
- Institute for Respiratory Health, Institute for Respiratory Health, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA, Australia
| | - Anna K Nowak
- National Centre for Asbestos Related Diseases, The University of Western Australia, Crawley, WA, Australia
- Institute for Respiratory Health, Institute for Respiratory Health, Perth, WA, Australia
- Medical School, The University of Western Australia, Crawley, WA, Australia
| | - Suki Gill
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Martin A Ebert
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA, Australia.
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, WA, Australia.
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3
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Kim S, Byun HK, Shin J, Lee IJ, Sung W. Normal Tissue Complication Probability Modeling of Severe Radiation-Induced Lymphopenia Using Blood Dose for Patients With Hepatocellular Carcinoma. Int J Radiat Oncol Biol Phys 2024; 119:1011-1020. [PMID: 38056776 DOI: 10.1016/j.ijrobp.2023.11.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/24/2023] [Accepted: 11/25/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE This study aimed to develop a normal tissue complication probability (NTCP) model to estimate the risk of severe radiation-induced lymphopenia (SRIL; absolute lymphocyte count [ALC] < 500/μL) by using the blood dose of patients with hepatocellular carcinoma (HCC). METHODS AND MATERIALS We retrospectively collected data from 75 patients with HCC who received radiation therapy (RT) between 2015 and 2018. The hematological dose framework calculated blood dose-volume histograms (DVHs) using a predefined blood flow model, organ DVHs, the number of treatment fractions, and beam delivery time. A Lyman-Kutcher-Burman model with a generalized equivalent dose was used to establish the NTCP model, reflecting the whole-blood DVHs. Optimization of the Lyman-Kutcher-Burman parameters was conducted by minimizing a negative log-likelihood function. RESULTS There were 6, 4, 18, 33, and 14 patients in the groups with radiation-induced lymphopenia grades 0, 1, 2, 3, and 4, respectively. The median pre- and post-RT ALC values were 1410/μL (range, 520-3710/μL) and 470/μL (range, 60-1760/μL), respectively. There was a correlation between mean blood dose and ALC depletion (Pearson r = -0.664; P < .001). The average mean blood doses in each radiation-induced lymphopenia group were 2.90 Gy (95% CI, 1.96-3.85 Gy) for grade 0 to 1, 5.29 Gy (95% CI, 4.12-6.45 Gy) for grade 2, 8.81 Gy (95% CI, 7.55-10.07 Gy) for grade 3, and 11.69 Gy (95% CI, 9.82-17.57 Gy) for grade 4. When applying the developed NTCP model to predict SRIL, the area under the receiver operating characteristic curve and Brier score values were 0.89 and 0.12, respectively. CONCLUSIONS We developed the first NTCP model based on whole-blood DVHs for estimating SRIL after abdominal RT in patients with HCC. Our results showed a strong correlation between blood dose and ALC depletion, suggesting the potential to predict the risk of SRIL occurrence using blood dose.
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Affiliation(s)
- Seohan Kim
- Deparments of Biomedical Engineering and Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hwa Kyung Byun
- Department of Radiation Oncology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea
| | - Jungwook Shin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Ik Jae Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
| | - Wonmo Sung
- Deparments of Biomedical Engineering and Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
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4
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He M, Liang C, Pang Y, Jiang M, Long M, Yao Z, Wang X, Zhang R, Wu Q, Liang S, Li J. A Novel Nomogram to Predict Prognosis of Advanced Hepatocellular Carcinoma Treated with Intensity-Modulated Radiotherapy Plus Anti-PD1. J Hepatocell Carcinoma 2024; 11:913-925. [PMID: 38799002 PMCID: PMC11128222 DOI: 10.2147/jhc.s459683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Purpose The combination of radiotherapy and monoclonal antibody against programmed cell death 1 (anti-PD1) showed preliminary efficacy in hepatocellular carcinoma (HCC). This study aimed to identify the prognostic factors and construct a nomogram to predict the overall survival (OS) of patients with advanced HCC after treatment with intensity-modulated radiotherapy (IMRT) plus anti-PD1. Patients and Methods The OS and progression-free survival (PFS) of 102 patients with BCLC stage C HCC was analyzed using the Kaplan-Meier method. Potential independent prognostic factors were determined using univariate and multivariate Cox regression analyses. A nomogram was established to predict prognosis whose accuracy and reliability was verified by a calibration curve and area under the receiver operating characteristic curve (AUROC). Results The median PFS and OS rates of the 102 patients with advanced HCC were 9.9 months and 14.3 months, respectively. Ninety-three patients were evaluated for efficacy, including five (5.38%) with complete response and 48 (51.61%) with partial response, with an overall response rate of 56.99%. Grade 3 and 4 adverse reactions (AEs) were observed in 32.35% of patients; no grade 5 AEs occurred. Multivariate Cox analysis revealed albumin and alpha-fetoprotein levels, neutrophil counts 3-4 weeks after IMRT initiation, and platelet-to-lymphocyte ratio 3-4 weeks after IMRT initiation to be independent prognostic factors. The nomogram model constructed using these factors had good consistency and accuracy with 1-3 years AUROC of 78.7, 78.6, and 93.5, respectively. Conclusion IMRT plus anti-PD1 showed promising efficacy and controllable adverse reactions in treating advanced HCC. The nomogram model demonstrated good reliability and clinical applicability.
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Affiliation(s)
- Meiling He
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Chunfeng Liang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Yadan Pang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Mengjie Jiang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Meiying Long
- School of Public Health, Guangxi Medical University, Nanning, 530021, People’s Republic of China
| | - Zhongqiang Yao
- Department of General Affairs, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Xiaoting Wang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Ruijun Zhang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Qiaoyuan Wu
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Shixiong Liang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
| | - Jianxu Li
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, People’s Republic of China
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5
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Wisdom AJ, Barker CA, Chang JY, Demaria S, Formenti S, Grassberger C, Gregucci F, Hoppe BS, Kirsch DG, Marciscano AE, Mayadev J, Mouw KW, Palta M, Wu CC, Jabbour SK, Schoenfeld JD. The Next Chapter in Immunotherapy and Radiation Combination Therapy: Cancer-Specific Perspectives. Int J Radiat Oncol Biol Phys 2024; 118:1404-1421. [PMID: 38184173 DOI: 10.1016/j.ijrobp.2023.12.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/20/2023] [Accepted: 12/30/2023] [Indexed: 01/08/2024]
Abstract
Immunotherapeutic agents have revolutionized cancer treatment over the past decade. However, most patients fail to respond to immunotherapy alone. A growing body of preclinical studies highlights the potential for synergy between radiation therapy and immunotherapy, but the outcomes of clinical studies have been mixed. This review summarizes the current state of immunotherapy and radiation combination therapy across cancers, highlighting existing challenges and promising areas for future investigation.
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Affiliation(s)
- Amy J Wisdom
- Harvard Radiation Oncology Program, Boston, Massachusetts
| | - Christopher A Barker
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joe Y Chang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Sandra Demaria
- Department of Radiation Oncology, Weill Cornell Medicine, New York, New York
| | - Silvia Formenti
- Department of Radiation Oncology, Weill Cornell Medicine, New York, New York
| | - Clemens Grassberger
- Department of Radiation Oncology, University of Washington, Fred Hutch Cancer Center, Seattle, Washington
| | - Fabiana Gregucci
- Department of Radiation Oncology, Weill Cornell Medicine, New York, New York
| | - Bradford S Hoppe
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida
| | - David G Kirsch
- Department of Radiation Oncology, University of Toronto, Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Ariel E Marciscano
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jyoti Mayadev
- Department of Radiation Oncology, UC San Diego School of Medicine, San Diego, California
| | - Kent W Mouw
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Manisha Palta
- Department of Radiation Oncology, Duke Cancer Center, Durham, North Carolina
| | - Cheng-Chia Wu
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, New York
| | - Salma K Jabbour
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey.
| | - Jonathan D Schoenfeld
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, Massachusetts.
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6
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Metzcar J, Jutzeler CR, Macklin P, Köhn-Luque A, Brüningk SC. A review of mechanistic learning in mathematical oncology. Front Immunol 2024; 15:1363144. [PMID: 38533513 PMCID: PMC10963621 DOI: 10.3389/fimmu.2024.1363144] [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: 12/29/2023] [Accepted: 02/20/2024] [Indexed: 03/28/2024] Open
Abstract
Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework.
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Affiliation(s)
- John Metzcar
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
- Informatics, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Catherine R. Jutzeler
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Paul Macklin
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Sarah C. Brüningk
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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7
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Kang M, Shin Y, Kim Y, Ha S, Sung W. Modeling the Synergistic Impact of Yttrium 90 Radioembolization and Immune Checkpoint Inhibitors on Hepatocellular Carcinoma. Bioengineering (Basel) 2024; 11:106. [PMID: 38391592 PMCID: PMC10886259 DOI: 10.3390/bioengineering11020106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/24/2024] Open
Abstract
The impact of yttrium 90 radioembolization (Y90-RE) in combination with immune checkpoint inhibitors (ICIs) has recently gained attention. However, it is unclear how sequencing and dosage affect therapeutic efficacy. The purpose of this study was to develop a mathematical model to simulate the synergistic effects of Y90-RE and ICI combination therapy and find the optimal treatment sequences and dosages. We generated a hypothetical patient cohort and conducted simulations to apply different treatments to the same patient. The compartment of models is described with ordinary differential equations (ODEs), which represent targeted tumors, non-targeted tumors, and lymphocytes. We considered Y90-RE as a local treatment and ICIs as a systemic treatment. The model simulations show that Y90-RE and ICIs administered simultaneously yield greater benefits than subsequent sequential therapy. In addition, applying Y90-RE before ICIs has more benefits than applying ICIs before Y90-RE. Moreover, we also observed that the median PFS increased up to 31~36 months, and the DM rates at 3 years decreased up to 36~48% as the dosage of the two drugs increased (p < 0.05). The proposed model predicts a significant benefit of Y90-RE with ICIs from the results of the reduced irradiated tumor burden and the associated immune activation and suppression. Our model is expected to help optimize complex strategies and predict the efficacy of clinical trials for HCC patients.
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Affiliation(s)
- Minah Kang
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Yerim Shin
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Yeseul Kim
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Sangseok Ha
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Wonmo Sung
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Chu C, Low YLC, Ma L, Wang Y, Cox T, Doré V, Masters CL, Goudey B, Jin L, Pan Y. How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer's Disease Research and Clinical Practices? J Alzheimers Dis 2024; 97:89-100. [PMID: 38007665 DOI: 10.3233/jad-230938] [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] [Indexed: 11/27/2023]
Abstract
The accumulation of amyloid-β (Aβ) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aβ, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone. Mathematical modeling can also simulate the effects of therapeutics on brain Aβ levels, thereby holding potential for drug efficacy simulation and the optimization of personalized treatment approaches. In this review, we provide an overview of the mathematical models that have been used to simulate brain levels of Aβ (oligomers, protofibrils, and/or plaques). We classify the models into five categories: the general ordinary differential equation models, the general partial differential equation models, the network models, the linear optimal ordinary differential equation models, and the modified partial differential equation models (i.e., Smoluchowski equation models). The assumptions, advantages and limitations of these models are discussed. Given the popularity of using the Smoluchowski equation models to simulate brain levels of Aβ, our review summarizes the history and major advancements in these models (e.g., their application to predict the onset of AD and their combined use with network models). This review is intended to bring mathematical modeling to the attention of more scientists and clinical researchers working on AD to promote cross-disciplinary research.
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Affiliation(s)
- Chenyin Chu
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yi Ling Clare Low
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Liwei Ma
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yihan Wang
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Timothy Cox
- The Australian e-Health Research Centre, CSIRO, Parkville, Victoria, Australia
| | - Vincent Doré
- The Australian e-Health Research Centre, CSIRO, Parkville, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Benjamin Goudey
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
| | - Liang Jin
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Yijun Pan
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Department of Organ Anatomy, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
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9
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Chami P, Diab Y, Khalil DN, Azhari H, Jarnagin WR, Abou-Alfa GK, Harding JJ, Hajj J, Ma J, El Homsi M, Reyngold M, Crane C, Hajj C. Radiation and Immune Checkpoint Inhibitors: Combination Therapy for Treatment of Hepatocellular Carcinoma. Int J Mol Sci 2023; 24:16773. [PMID: 38069095 PMCID: PMC10706661 DOI: 10.3390/ijms242316773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
The liver tumor immune microenvironment has been thought to possess a critical role in the development and progression of hepatocellular carcinoma (HCC). Despite the approval of immune checkpoint inhibitors (ICIs), such as programmed cell death receptor 1 (PD-1)/programmed cell death ligand 1 (PD-L1) and cytotoxic T lymphocyte associated protein 4 (CTLA-4) inhibitors, for several types of cancers, including HCC, liver metastases have shown evidence of resistance or poor response to immunotherapies. Radiation therapy (RT) has displayed evidence of immunosuppressive effects through the upregulation of immune checkpoint molecules post-treatment. However, it was revealed that the limitations of ICIs can be overcome through the use of RT, as it can reshape the liver immune microenvironment. Moreover, ICIs are able to overcome the RT-induced inhibitory signals, effectively restoring anti-tumor activity. Owing to the synergetic effect believed to arise from the combination of ICIs with RT, several clinical trials are currently ongoing to assess the efficacy and safety of this treatment for patients with HCC.
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Affiliation(s)
- Perla Chami
- Faculty of Medicine, American University of Beirut, Beirut 1107, Lebanon;
| | - Youssef Diab
- Faculty of Medicine, University of Balamand, Beirut 1100, Lebanon; (Y.D.)
| | - Danny N. Khalil
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
| | - Hassan Azhari
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
| | - William R. Jarnagin
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
- Department of Surgery, Weill Medical College, Cornell University, New York, NY 10021, USA
| | - Ghassan K. Abou-Alfa
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
- Department of Medicine, Weill Medical College, Cornell University, New York, NY 10021, USA
| | - James J. Harding
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
- Department of Medicine, Weill Medical College, Cornell University, New York, NY 10021, USA
| | - Joseph Hajj
- Faculty of Medicine, University of Balamand, Beirut 1100, Lebanon; (Y.D.)
| | - Jennifer Ma
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
| | - Maria El Homsi
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
| | - Marsha Reyngold
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
| | | | - Carla Hajj
- Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
- New York Proton Center, New York, NY 10035, USA
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10
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Tamai Y, Fujiwara N, Tanaka T, Mizuno S, Nakagawa H. Combination Therapy of Immune Checkpoint Inhibitors with Locoregional Therapy for Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:5072. [PMID: 37894439 PMCID: PMC10605879 DOI: 10.3390/cancers15205072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is estimated to be the fourth leading cause of cancer-related deaths globally, and its overall prognosis is dismal because most cases are diagnosed at a late stage and are unamenable to curative treatment. The emergence of immune checkpoint inhibitors (ICIs) has dramatically improved the therapeutic efficacy for advanced hepatocellular carcinoma; however, their response rates remain unsatisfactory, partly because >50% of HCC exhibit an ICI-nonresponsive tumor microenvironment characterized by a paucity of cytotoxic T cells (immune-cold), as well as difficulty in their infiltration into tumor sites (immune excluded). To overcome this limitation, combination therapies with locoregional therapies, including ablation, transarterial embolization, and radiotherapy, which are usually used for early stage HCCs, have been actively explored to enhance ICI efficacy by promoting the release of tumor-associated antigens and cytokines, and eventually accelerating the so-called cancer-immunity cycle. Various combination therapies have been investigated in early- to late-phase clinical trials, and some have shown promising results. This comprehensive article provides an overview of the immune landscape for HCC to understand ICI efficacy and its limitations and, subsequently, reviews the status of combinatorial therapies of ICIs with locoregional therapy for HCC.
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Affiliation(s)
- Yasuyuki Tamai
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan; (Y.T.); (T.T.); (H.N.)
| | - Naoto Fujiwara
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan; (Y.T.); (T.T.); (H.N.)
| | - Takamitsu Tanaka
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan; (Y.T.); (T.T.); (H.N.)
| | - Shugo Mizuno
- Department of Hepatobiliary Pancreatic and Transplant Surgery, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan;
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Tsu 514-8507, Japan; (Y.T.); (T.T.); (H.N.)
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11
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Yang Y, Xiong L, Li M, Jiang P, Wang J, Li C. Advances in radiotherapy and immunity in hepatocellular carcinoma. J Transl Med 2023; 21:526. [PMID: 37542324 PMCID: PMC10401766 DOI: 10.1186/s12967-023-04386-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/24/2023] [Indexed: 08/06/2023] Open
Abstract
Primary liver cancer is one of the most common malignant tumours worldwide; it caused approximately 830,000 deaths in 2020. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for over 80% of all cases. Various methods, including surgery, chemotherapy, radiotherapy, and radiofrequency ablation, have been widely used in the treatment of HCC. With the advancement of technology, radiotherapy has become increasingly important in the comprehensive treatment of HCC. However, due to the insufficient sensitivity of tumour cells to radiation, there are still multiple limitation in clinical application of radiotherapy. In recent years, the role of immunotherapy in cancer has been increasingly revealed, and more researchers have turned their attention to the combined application of immunotherapy and radiotherapy in the hope of achieving better treatment outcomes. This article reviews the progress on radiation therapy in HCC and the current status of its combined application with immunotherapy, and discusses the prospects and value of radioimmunotherapy in HCC.
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Affiliation(s)
- Yuhan Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China
| | - Liting Xiong
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
| | - Mengyuan Li
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China
| | - Ping Jiang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China.
| | - Junjie Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China.
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China.
| | - Chunxiao Li
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, 100191, China.
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12
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Shin Y, Chang JS, Kim Y, Shin SJ, Kim J, Kim TH, Liu M, Olson R, Kim JS, Sung W. Mathematical prediction with pretreatment growth rate of metastatic cancer on outcomes: implications for the characterization of oligometastatic disease. Front Oncol 2023; 13:1061881. [PMID: 37313457 PMCID: PMC10258314 DOI: 10.3389/fonc.2023.1061881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 05/10/2023] [Indexed: 06/15/2023] Open
Abstract
Background Oligometastatic disease (OMD) represents an indolent cancer status characterized by slow tumor growth and limited metastatic potential. The use of local therapy in the management of the condition continues to rise. This study aimed to investigate the advantage of pretreatment tumor growth rate in addition to baseline disease burden in characterizing OMDs, generally defined by the presence of ≤ 5 metastatic lesions. Methods The study included patients with metastatic melanoma treated with pembrolizumab. Gross tumor volume of all metastases was contoured on imaging before (TP-1) and at the initiation of pembrolizumab (TP0). Pretreatment tumor growth rate was calculated by an exponential ordinary differential equation model using the sum of tumor volumes at TP-1 and TP0 and the time interval between TP-1. and TP0. Patients were divided into interquartile groups based on pretreatment growth rate. Overall survival, progression-free survival, and subsequent progression-free survival were the study outcomes. Results At baseline, median cumulative volume and number of metastases were 28.4 cc (range, 0.4-1194.8 cc) and 7 (range, 1-73), respectively. The median interval between TP-1 and TP0 was -90 days and pretreatment tumor growth rate (×10-2 days-1) was median 4.71 (range -0.62 to 44.1). The slow-paced group (pretreatment tumor growth rate ≤ 7.6 ×10-2 days-1, the upper quartile) had a significantly higher overall survival rate, progression-free survival, and subsequent progression-free survival compared to those of the fast-paced group (pretreatment tumor growth rate > 7.6 ×10-2 days-1). Notably, these differences were prominent in the subgroup with >5 metastases. Conclusion Pretreatment tumor growth rate is a novel prognostic metric associated with overall survival, progression-free survival, and subsequent progression-free survival among metastatic melanoma patients, especially patients with >5 metastases. Future prospective studies should validate the advantage of disease growth rate plus disease burden in better defining OMDs.
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Affiliation(s)
- Yerim Shin
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
- BC Cancer - Vancouver Centre, Vancouver, BC, Canada
| | - Yeseul Kim
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sang Joon Shin
- Division of Medical Oncology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jina Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Tae Hyung Kim
- Department of Radiation Oncology, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Republic of Korea
| | - Mitchell Liu
- BC Cancer - Vancouver Centre, Vancouver, BC, Canada
| | - Robert Olson
- BC Cancer, Centre for the North, Prince George, BC, Canada
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Wonmo Sung
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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13
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Kim Y, Choe BY, Suh TS, Sung W. A Mathematical Model for Predicting Patient Responses to Combined Radiotherapy with CTLA-4 Immune Checkpoint Inhibitors. Cells 2023; 12:cells12091305. [PMID: 37174706 PMCID: PMC10177154 DOI: 10.3390/cells12091305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/27/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
The purpose of this study was to develop a cell-cell interaction model that could predict a tumor's response to radiotherapy (RT) combined with CTLA-4 immune checkpoint inhibition (ICI) in patients with hepatocellular carcinoma (HCC). The previously developed model was extended by adding a new term representing tremelimumab, an inhibitor of CTLA-4. The distribution of the new immune activation term was derived from the results of a clinical trial for tremelimumab monotherapy (NCT01008358). The proposed model successfully reproduced longitudinal tumor diameter changes in HCC patients treated with tremelimumab (complete response = 0%, partial response = 17.6%, stable disease = 58.8%, and progressive disease = 23.6%). For the non-irradiated tumor control group, adding ICI to RT increased the clinical benefit rate from 8% to 32%. The simulation predicts that it is beneficial to start CTLA-4 blockade before RT in terms of treatment sequences. We developed a mathematical model that can predict the response of patients to the combined CTLA-4 blockade with radiation therapy. We anticipate that the developed model will be helpful for designing clinical trials with the ultimate aim of maximizing the efficacy of ICI-RT combination therapy.
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Affiliation(s)
- Yongjin Kim
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Bo-Young Choe
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Tae Suk Suh
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Wonmo Sung
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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14
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Helm A, Totis C, Durante M, Fournier C. Are charged particles a good match for combination with immunotherapy? Current knowledge and perspectives. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2023; 376:1-36. [PMID: 36997266 DOI: 10.1016/bs.ircmb.2023.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Charged particle radiotherapy, mainly using protons and carbon ions, provides physical characteristics allowing for a volume conformal irradiation and a reduction of the integral dose to normal tissue. Carbon ion therapy additionally features an increased biological effectiveness resulting in peculiar molecular effects. Immunotherapy, mostly performed with immune checkpoint inhibitors, is nowadays considered a pillar in cancer therapy. Based on the advantageous features of charged particle radiotherapy, we review pre-clinical evidence revealing a strong potential of its combination with immunotherapy. We argue that the combination therapy deserves further investigation with the aim of translation in clinics, where a few studies have been set up already.
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Affiliation(s)
- A Helm
- Biophysics Department, GSI, Darmstadt, Germany
| | - C Totis
- Biophysics Department, GSI, Darmstadt, Germany
| | - M Durante
- Biophysics Department, GSI, Darmstadt, Germany.
| | - C Fournier
- Biophysics Department, GSI, Darmstadt, Germany
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15
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Chen L, Zhang R, Lin Z, Tan Q, Huang Z, Liang B. Radiation therapy in the era of immune treatment for hepatocellular carcinoma. Front Immunol 2023; 14:1100079. [PMID: 36742293 PMCID: PMC9895775 DOI: 10.3389/fimmu.2023.1100079] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/06/2023] [Indexed: 01/22/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment in recent years and provide new opportunities to treat hepatocellular carcinoma (HCC). To date, several ICIs have been approved by the FDA for advanced HCC in first-line or second-line therapy. Downstaging conversion therapy for potentially resectable HCC to provide opportunities for surgical intervention is challenging. ICIs have become a hot spot in this field due to their high response rate. However, HCC has various etiologies and can evade the immune system through multiple mechanisms, which limit the efficacy of ICI monotherapy and demand novel combination strategies. Radiation therapy (RT) is also a candidate for conversion therapy in HCC and is currently gaining increasing attention as a good combination partner with ICIs due to its ability to modulate the tumor microenvironment. In this review, we illustrate the current indications for ICIs and RT in HCC, the rationale for their synergistic combination, and the current clinical trials in combination therapy. We also speculate on predictive biomarkers and novel future strategies to further enhance the efficacy of this combination. This review aims to provide references for future research on radiation and immunotherapy to arrive at a promising new era of HCC treatment.
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Affiliation(s)
- Lingjuan Chen
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ruiguang Zhang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenyu Lin
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiaoyun Tan
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhiyong Huang
- Hepatic Surgery Center, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Binyong Liang
- Hepatic Surgery Center, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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16
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Butner JD, Dogra P, Chung C, Pasqualini R, Arap W, Lowengrub J, Cristini V, Wang Z. Mathematical modeling of cancer immunotherapy for personalized clinical translation. NATURE COMPUTATIONAL SCIENCE 2022; 2:785-796. [PMID: 38126024 PMCID: PMC10732566 DOI: 10.1038/s43588-022-00377-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2023]
Abstract
Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - John Lowengrub
- Department of Mathematics, University of California at Irvine, Irvine, CA, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, 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
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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Edeline J, Meyer T, Blanc JF, Raoul JL. New Challenges Facing Systemic Therapies of Advanced HCC in the Era of Different First-Line Immunotherapy-Based Combinations. Cancers (Basel) 2022; 14:cancers14235868. [PMID: 36497349 PMCID: PMC9739025 DOI: 10.3390/cancers14235868] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/02/2022] Open
Abstract
The standard of care of first-line systemic therapy for advanced hepatocellular carcinoma (HCC) is currently changing with the results of the IMbrave150 trial which are demonstrating superiority of the atezolizumab-bevacizumab combination over sorafenib, modifying this line of treatment for the first time in over 10 years. Recently, other immunotherapy-based combinations (durvalumab-tremelimumab, lenvatinib-pembrolizumab, cabozantinib-atezolizumab, and camrelizumab-rivoceranib) reported results in phase III studies, and might challenge this new standard of care. This revolution will lead to a considerable change in practice, and highlight challenges for future drug development. In this review, we will, firstly, describe results of the different combinations, and discuss the difficulties in selecting the first-line treatment. We will then present the different recommendations about second-line treatment following the first-line immunotherapy-based combination, discussing the rationale for the differences in existing recommendations. We will finally discuss the challenges for future drug development in advanced HCC.
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Affiliation(s)
- Julien Edeline
- INSERM, Department of Medical Oncology, University Rennes, CLCC Eugène Marquis, COSS (Chemistry Oncogenesis Stress Signaling)—UMR_S 1242, F-35000 Rennes, France
- Correspondence:
| | - Tim Meyer
- Medical Oncology, University College London, London WC1E 6BT, UK
| | | | - Jean-Luc Raoul
- Medical Oncology, Institut de Cancérologie de l’Ouest, 44805 Nantes, France
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18
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Li JX, Su TS, Gong WF, Zhong JH, Yan LY, Zhang J, Li LQ, He ML, Zhang RJ, Du YQ, Wang XT, Liang SX, Xiang BD. Combining stereotactic body radiotherapy with camrelizumab for unresectable hepatocellular carcinoma: a single-arm trial. Hepatol Int 2022; 16:1179-1187. [PMID: 36001228 PMCID: PMC9525355 DOI: 10.1007/s12072-022-10396-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 07/16/2022] [Indexed: 01/27/2023]
Abstract
PURPOSE Stereotactic body radiotherapy (SBRT) may have significant immunomodulatory effects that enhance tumor response to immune checkpoint inhibitors. This phase 2 clinical trial was conducted to evaluate the safety and efficacy of combining palliative SBRT with camrelizumab (an anti-PD1 monoclonal antibody) in patients with unresectable hepatocellular carcinoma (uHCC). METHODS Patients with uHCC, Child-Pugh A/B liver function, and at least one measurable lesion were enrolled between April 2020 and August 2022. Patients were administered 200 mg camrelizumab intravenously from the first day of palliative SBRT and then every 3 weeks. Palliative SBRT was delivered daily over five fractions per week, with a dose range of 30-50 Gy. The primary endpoints were objective response rate (ORR) and safety. This trial was registered at ClinicalTrials.gov (NCT04193696). RESULTS Twenty-one patients were enrolled; the median radiation dose was 40 Gy, and the median number of cycles of camrelizumab was five. The ORR was 52.4%. After a median follow-up of 19.7 months, the median progression-free and overall survival were 5.8 and 14.2 months, respectively. The overall survival probability was 85.7% at 6 months, 76.2% at 9 months, and 59.9% at 12 months. All grade 3 treatment-related adverse events (TRAEs) occurred in five patients (23.8%) and were manageable. No grade 4/5 TRAEs were observed. CONCLUSION Palliative SBRT plus camrelizumab showed promising antitumor activity against uHCC. Toxicities were manageable with no unexpected safety issues. This study provides evidence of a new therapeutic method for the treatment of uHCC.
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Affiliation(s)
- Jian-Xu Li
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Ting-Shi Su
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Wen-Feng Gong
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Jian-Hong Zhong
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Liu-Ying Yan
- Department of General Affairs, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jie Zhang
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Li-Qing Li
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Mei-Ling He
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Rui-Jun Zhang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - You-Qin Du
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Xiao-Ting Wang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, China
| | - Shi-Xiong Liang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, 530021, China.
| | - Bang-De Xiang
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530021, China.
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19
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Wang L, Qiu L, Ke Q, Ji H, Wu J. Systematic review of adjuvant external beam radiotherapy for hepatocellular carcinoma following radical hepatectomy. Radiother Oncol 2022; 175:101-111. [PMID: 35998838 DOI: 10.1016/j.radonc.2022.08.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND AND AIM Recurrence remains the main bottleneck hindering outcomes of hepatectomy for hepatocellular carcinoma (HCC). Owing to technological advances, external beam radiotherapy (EBRT) is being increasingly used in the management of HCC; however, there is no consensus on the role of adjuvant EBRT following hepatectomy. METHODS A systematic review was conducted according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. PubMed, MedLine, Embase, the Cochrane Library, Web of Knowledge were used to screen eligible studies (published as of May 1st, 2022) that evaluated the clinical safety and efficacy of EBRT for HCC receiving hepatectomy. The endpoints were disease-free survival (DFS), overall survival (OS), and adverse events (AEs). RESULTS A total of ten studies were eligible (three randomized controlled trials, one phase II trial, and six retrospective comparative studies). The pooled hazard ratio (HR) for median DFS and OS were both in favor of adjuvant EBRT compared with surgery alone (all P<0.05), and the advantage of adjuvant EBRT was also confirmed in subgroups stratified by different populations (narrow margin, P<0.05; microvascular invasion, P<0.05; portal vein tumor thrombus, P<0.05) and study designs (prospective studies, P<0.05; retrospective studies, P<0.05). Adjuvant EBRT was also found to be superior to adjuvant TACE (P<0.05). Pooled rates of overall AEs and severe AEs were 65.3% and 12.2%, but no fatal AEs were reported. CONCLUSION Adjuvant EBRT can be considered for HCC patients, especially those with a high risk of recurrence. Further studies are required for validation of these findings.
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Affiliation(s)
- Lei Wang
- College of Clinical Medicine for Oncology, Fujian Medical University, Fuzhou, Fujian, China; Department of Radiation Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Lu Qiu
- Department of Radiation Oncology, Zhangzhou Affiliated Hospital of Fujian medical University, Fuzhou, Zhangzhou, China
| | - Qiao Ke
- College of Clinical Medicine for Oncology, Fujian Medical University, Fuzhou, Fujian, China; Department of Hepatopancreatobiliary Surgery, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Hongbing Ji
- Department of Radiation Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China.
| | - Junxin Wu
- College of Clinical Medicine for Oncology, Fujian Medical University, Fuzhou, Fujian, China; Department of Radiation Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China.
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20
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Sung W, Cho B. Modeling of radiation effects to immune system: a review. THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY 2022; 81:1013-1019. [PMID: 35966936 PMCID: PMC9358382 DOI: 10.1007/s40042-022-00574-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/17/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
Cancer metastasis is the major cause of cancer mortality and accounts for about 90% of cancer death. Although radiation therapy has been considered to reduce the localized cancer burden, emerging evidence that radiation can potentially turn tumors into an in situ vaccine has raised significant interest in combining radiation with immunotherapy. However, the combination approach might be limited by the radiation-induced immunosuppression. Assessment of radiation effects on the immune system at the patient level is critical to maximize the systemic antitumor response of radiation. In this review, we summarize the developed solutions in three different categories for systemic radiation therapy: blood dose, radiation-induced lymphopenia, and tumor control. Furthermore, we address how they could be combined to optimize radiotherapy regimens and maximize their synergy with immunotherapy.
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Affiliation(s)
- Wonmo Sung
- Department of Biomedical Engineering and of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Byungchul Cho
- Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
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