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Gaito S, Marvaso G, Ortiz R, Crellin A, Aznar MC, Indelicato DJ, Pan S, Whitfield G, Alongi F, Jereczek-Fossa BA, Burnet N, Li MP, Rothwell B, Smith E, Colaco RJ. Proton Beam Therapy in the Oligometastatic/Oligorecurrent Setting: Is There a Role? A Literature Review. Cancers (Basel) 2023; 15:cancers15092489. [PMID: 37173955 PMCID: PMC10177340 DOI: 10.3390/cancers15092489] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Stereotactic ablative radiotherapy (SABR) and stereotactic radiosurgery (SRS) with conventional photon radiotherapy (XRT) are well-established treatment options for selected patients with oligometastatic/oligorecurrent disease. The use of PBT for SABR-SRS is attractive given the property of a lack of exit dose. The aim of this review is to evaluate the role and current utilisation of PBT in the oligometastatic/oligorecurrent setting. METHODS Using Medline and Embase, a comprehensive literature review was conducted following the PICO (Patients, Intervention, Comparison, and Outcomes) criteria, which returned 83 records. After screening, 16 records were deemed to be relevant and included in the review. RESULTS Six of the sixteen records analysed originated in Japan, six in the USA, and four in Europe. The focus was oligometastatic disease in 12, oligorecurrence in 3, and both in 1. Most of the studies analysed (12/16) were retrospective cohorts or case reports, two were phase II clinical trials, one was a literature review, and one study discussed the pros and cons of PBT in these settings. The studies presented in this review included a total of 925 patients. The metastatic sites analysed in these articles were the liver (4/16), lungs (3/16), thoracic lymph nodes (2/16), bone (2/16), brain (1/16), pelvis (1/16), and various sites in 2/16. CONCLUSIONS PBT could represent an option for the treatment of oligometastatic/oligorecurrent disease in patients with a low metastatic burden. Nevertheless, due to its limited availability, PBT has traditionally been funded for selected tumour indications that are defined as curable. The availability of new systemic therapies has widened this definition. This, together with the exponential growth of PBT capacity worldwide, will potentially redefine its commissioning to include selected patients with oligometastatic/oligorecurrent disease. To date, PBT has been used with encouraging results for the treatment of liver metastases. However, PBT could be an option in those cases in which the reduced radiation exposure to normal tissues leads to a clinically significant reduction in treatment-related toxicities.
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Affiliation(s)
- Simona Gaito
- Proton Clinical Outcomes Unit, The Christie NHS Proton Beam Therapy Centre, Manchester M20 4BX, UK
- Division of Clinical Cancer Science, School of Medical Sciences, The University of Manchester, Manchester M13 9PL, UK
| | - Giulia Marvaso
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20126 Milan, Italy
| | - Ramon Ortiz
- Department of Radiation Oncology, University of California, San Francisco, CA 94720, USA
| | - Adrian Crellin
- National Lead Proton Beam Therapy NHSe, Manchester M20 4BX, UK
| | - Marianne C Aznar
- Division of Clinical Cancer Science, School of Medical Sciences, The University of Manchester, Manchester M13 9PL, UK
| | - Daniel J Indelicato
- Department of Radiation Oncology, University of Florida, Jacksonville, FL 32206, USA
| | - Shermaine Pan
- Department of Proton Beam Therapy, The Christie Proton Beam Therapy Centre, Manchester M20 3DA, UK
| | - Gillian Whitfield
- Division of Clinical Cancer Science, School of Medical Sciences, The University of Manchester, Manchester M13 9PL, UK
- Department of Proton Beam Therapy, The Christie Proton Beam Therapy Centre, Manchester M20 3DA, UK
| | - Filippo Alongi
- Advanced Radiation Oncology Department, IRCCS Ospedale Sacro Cuore don Calabria, 37024 Verona, Italy
- Division of Radiology and Radiotherapy, University of Brescia, 25121 Brescia, Italy
| | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20126 Milan, Italy
| | - Neil Burnet
- Department of Proton Beam Therapy, The Christie Proton Beam Therapy Centre, Manchester M20 3DA, UK
| | - Michelle P Li
- Department of Proton Beam Therapy, The Christie Proton Beam Therapy Centre, Manchester M20 3DA, UK
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Bethany Rothwell
- Division of Physics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Ed Smith
- Proton Clinical Outcomes Unit, The Christie NHS Proton Beam Therapy Centre, Manchester M20 4BX, UK
- Division of Clinical Cancer Science, School of Medical Sciences, The University of Manchester, Manchester M13 9PL, UK
- Department of Proton Beam Therapy, The Christie Proton Beam Therapy Centre, Manchester M20 3DA, UK
| | - Rovel J Colaco
- Department of Proton Beam Therapy, The Christie Proton Beam Therapy Centre, Manchester M20 3DA, UK
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Bekker RA, Kim S, Pilon-Thomas S, Enderling H. Mathematical modeling of radiotherapy and its impact on tumor interactions with the immune system. Neoplasia 2022; 28:100796. [PMID: 35447601 PMCID: PMC9043662 DOI: 10.1016/j.neo.2022.100796] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 03/27/2022] [Accepted: 04/01/2022] [Indexed: 11/01/2022]
Abstract
Radiotherapy is a primary therapeutic modality widely utilized with curative intent. Traditionally tumor response was hypothesized to be due to high levels of cell death induced by irreparable DNA damage. However, the immunomodulatory aspect of radiation is now widely accepted. As such, interest into the combination of radiotherapy and immunotherapy is increasing, the synergy of which has the potential to improve tumor regression beyond that observed after either treatment alone. However, questions regarding the timing (sequential vs concurrent) and dose fractionation (hyper-, standard-, or hypo-fractionation) that result in improved anti-tumor immune responses, and thus potentially enhanced tumor inhibition, remain. Here we discuss the biological response to radiotherapy and its immunomodulatory properties before giving an overview of pre-clinical data and clinical trials concerned with answering these questions. Finally, we review published mathematical models of the impact of radiotherapy on tumor-immune interactions. Ranging from considering the impact of properties of the tumor microenvironment on the induction of anti-tumor responses, to the impact of choice of radiation site in the setting of metastatic disease, these models all have an underlying feature in common: the push towards personalized therapy.
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Dynamics of an Antitumour Model with Pulsed Radioimmunotherapy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4692772. [PMID: 35677181 PMCID: PMC9168186 DOI: 10.1155/2022/4692772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 04/20/2022] [Indexed: 11/18/2022]
Abstract
In this paper, an antitumour model for characterising radiotherapy and immunotherapy processes at different fixed times is proposed. The global attractiveness of the positive periodic solution for each corresponding subsystem is proved with the integral inequality technique. Then, based on the differentiability of the solutions with respect to the initial values, the eigenvalues of the Jacobian matrix at a fixed point corresponding to the tumour-free periodic solution are determined, resulting in a sufficient condition for local stability. The solutions to the ordinary differential equations are compared, the threshold condition for the global attractiveness of the tumour-free periodic solution is provided in terms of an indicator
, and the permanence of a system with at least one tumour-present periodic solution is investigated. Furthermore, the effects of the death rate, effector cell injection dosage, therapeutic period, and effector cell activation rate on indicator
are determined through numerical simulations, and the results indicate that radioimmunotherapy is more effective than either radiotherapy or immunotherapy alone.
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Acree P, Kapadia A, Mahatme R, Zhang L, Patel D, Almoney C, Park G, Kofsky M, Matin S, Habibi M. Review of Current Accepted Practices in Identification of the Breast Lumpectomy Tumor Bed. Adv Radiat Oncol 2022; 7:100848. [PMID: 36148372 PMCID: PMC9486415 DOI: 10.1016/j.adro.2021.100848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/01/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose Of the 260,000 women diagnosed with breast cancer annually in the United States, more than 60% are treated with breast-conserving surgery or lumpectomy, followed by radiation to decrease the chance of local recurrence. More than 70% of breast cancer recurrences are localized to the original tumor cavity. Hence, targeted radiation therapy after lumpectomy is critical for recurrence prevention. With 30,000 patients annually opting for oncoplastic reconstruction of the breast after lumpectomy to improve cosmesis, the resulting tissue rearrangement increases the difficulty for radiation oncologists to accurately delineate the cavity when planning radiation therapy. Owing to the absence of a standardized protocol, it is important to assess the efficacy of various methods used to mark the tumor cavity for improved delineation. Methods and Materials A keyword search and analysis was used to compile relevant articles on PubMed (National Center for Biotechnology Information). Results Currently, a common practice for tumor cavity localization is applying titanium surgical clips to the borders of lumpectomy cavity. Tissue movement and seroma formation both impact the positioning of surgical clips within the tumor cavity and lead to significant interobserver variability. Furthermore, the main application of surgical clips is to control the small vessels during surgery, and that can create confusion when the same clips are used for tumor bed localization. All alternative solutions present more precise tumor bed delineation but possess individual concerns with workflow integration, patient comfort, and accuracy. Though liquid-based fiducials were found to be the most effective for delineating tumor cavities, there are still drawbacks for clinical use. Conclusions These findings should encourage medical innovators to develop novel techniques for tumor cavity marking to increase delineation accuracy and effectively target at-risk tissue. Future solutions in this space should consider the properties of liquid-based fiducial markers to improve radiation oncologists' ability to precisely delineate the tumor cavity.
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Zahid MU, Mohsin N, Mohamed ASR, Caudell JJ, Harrison LB, Fuller CD, Moros EG, Enderling H. Forecasting Individual Patient Response to Radiation Therapy in Head and Neck Cancer With a Dynamic Carrying Capacity Model. Int J Radiat Oncol Biol Phys 2021; 111:693-704. [PMID: 34102299 PMCID: PMC8463501 DOI: 10.1016/j.ijrobp.2021.05.132] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 12/21/2022]
Abstract
Purpose: To model and predict individual patient responses to radiation therapy. Methods and Materials: We modeled tumor dynamics as logistic growth and the effect of radiation as a reduction in the tumor carrying capacity, motivated by the effect of radiation on the tumor microenvironment. The model was assessed on weekly tumor volume data collected for 2 independent cohorts of patients with head and neck cancer from the H. Lee Moffitt Cancer Center (MCC) and the MD Anderson Cancer Center (MDACC) who received 66 to 70 Gy in standard daily fractions or with accelerated fractionation. To predict response to radiation therapy for individual patients, we developed a new forecasting framework that combined the learned tumor growth rate and carrying capacity reduction fraction (δ) distribution with weekly measurements of tumor volume reduction for a given test patient to estimate δ, which was used to predict patient-specific outcomes. Results: The model fit data from MCC with high accuracy with patient-specific δ and a fixed tumor growth rate across all patients. The model fit data from an independent cohort from MDACC with comparable accuracy using the tumor growth rate learned from the MCC cohort, showing transferability of the growth rate. The forecasting framework predicted patient-specific outcomes with 76% sensitivity and 83% specificity for locoregional control and 68% sensitivity and 85% specificity for disease-free survival with the inclusion of 4 on-treatment tumor volume measurements. Conclusions: These results demonstrate that our simple mathematical model can describe a variety of tumor volume dynamics. Furthermore, combining historically observed patient responses with a few patient-specific tumor volume measurements allowed for the accurate prediction of patient outcomes, which may inform treatment adaptation and personalization.
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Affiliation(s)
- Mohammad U Zahid
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Nuverah Mohsin
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida; Dr. Kiran C. Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jimmy J Caudell
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Louis B Harrison
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Eduardo G Moros
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
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Alfonso JCL, Grass GD, Welsh E, Ahmed KA, Teer JK, Pilon-Thomas S, Harrison LB, Cleveland JL, Mulé JJ, Eschrich SA, Torres-Roca JF, Enderling H. Tumor-immune ecosystem dynamics define an individual Radiation Immune Score to predict pan-cancer radiocurability. Neoplasia 2021; 23:1110-1122. [PMID: 34619428 PMCID: PMC8502777 DOI: 10.1016/j.neo.2021.09.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 01/10/2023] Open
Abstract
Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of antitumor immune responses. We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability. We show that iRIS correlates with local control and survival in a separate cohort of 59 lung cancer patients treated with radiation. In combination, iRIS and RSI predict radiation-induced TIES shifts in individual patients and identify candidates for radiation de-escalation and treatment escalation. This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy.
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Affiliation(s)
- Juan C L Alfonso
- Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - G Daniel Grass
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Eric Welsh
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Kamran A Ahmed
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jamie K Teer
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Shari Pilon-Thomas
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Louis B Harrison
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - John L Cleveland
- Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - James J Mulé
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Steven A Eschrich
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Javier F Torres-Roca
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Heiko Enderling
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
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Muncey AR, Patel SY, Whelan CJ, Ackerman RS, Gatenby RA. The Intersection of Regional Anesthesia and Cancer Progression: A Theoretical Framework. Cancer Control 2021; 27:1073274820965575. [PMID: 33070618 PMCID: PMC7791454 DOI: 10.1177/1073274820965575] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
The surgical stress and inflammatory response and volatile anesthetic
agents have been shown to promote tumor metastasis in animal and
in-vitro studies. Regional neuraxial anesthesia protects against these
effects by decreasing the surgical stress and inflammatory response
and associated changes in immune function in animals. However,
evidence of a similar effect in humans remains equivocal due to the
high variability and retrospective nature of clinical studies and
difficulty in directly comparing regional versus general anesthesia in
humans. We propose a theoretical framework to address the question of
regional anesthesia as protective against metastasis. This theoretical construct views the immune system, circulating tumor
cells, micrometastases, and inflammatory mediators as distinct
populations in a highly connected system. In ecological theory, highly
connected populations demonstrate more resilience to local
perturbations but are prone to system-wide shifts compared with their
poorly connected counterparts. Neuraxial anesthesia transforms the
otherwise system-wide perturbations of the surgical stress and
inflammatory response and volatile anesthesia into a comparatively
local perturbation to which the system is more resilient. We propose
this framework for experimental and mathematical models to help
determine the impact of anesthetic choice on recurrence and metastasis
and create therapeutic strategies to improve cancer outcomes after
surgery.
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Sung W, Grassberger C, McNamara AL, Basler L, Ehrbar S, Tanadini-Lang S, Hong TS, Paganetti H. A tumor-immune interaction model for hepatocellular carcinoma based on measured lymphocyte counts in patients undergoing radiotherapy. Radiother Oncol 2020; 151:73-81. [PMID: 32679308 DOI: 10.1016/j.radonc.2020.07.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE The impact of radiation therapy on the immune system has recently gained attention particularly when delivered in combination with immunotherapy. However, it is unclear how different treatment fractionation regimens influence the interaction between the immune system and radiation. The goal of this work was to develop a mathematical model that quantifies both the immune stimulating as well as the immunosuppressive effects of radiotherapy and simulates the effects of different fractionation regimens based on patient data. METHODS AND MATERIALS The framework describes the temporal evolution of tumor cells, lymphocytes, and inactivated dying tumor cells releasing antigens during radiation therapy, specifically modeling how recruited lymphocytes inhibit tumor progression. The parameters of the model were partly taken from the literature and in part extracted from blood samples (circulating lymphocytes: CLs) collected from hepatocellular carcinoma patients undergoing radiotherapy and their outcomes. The dose volume histograms to circulating lymphocytes were calculated with a probability-based model. RESULTS Based on the fitted parameters, the model enabled a study into the depletion and recovery of CLs in patients as a function of fractionation regimen. Our results quantify the ability of short fractionation regimens to lead to shorter periods of lymphocyte depletion and predict faster recovery after the end of treatment. The model shows that treatment breaks between fractions can prolong the period of lymphocyte depletion and should be avoided. CONCLUSIONS This study introduces a mathematical model for tumor-immune interactions using clinically extracted radiotherapy patient data, which can be applied to design trials aimed at minimizing lymphocyte depleting effects in radiation therapy.
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Affiliation(s)
- Wonmo Sung
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States
| | - Aimee Louise McNamara
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States
| | - Lucas Basler
- Department of Radiation Oncology, Paul Scherrer Institut, Villigen, Switzerland
| | - Stefanie Ehrbar
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
| | | | - Theodore S Hong
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States.
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10
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Alfonso JCL, Papaxenopoulou LA, Mascheroni P, Meyer-Hermann M, Hatzikirou H. On the Immunological Consequences of Conventionally Fractionated Radiotherapy. iScience 2020; 23:100897. [PMID: 32092699 PMCID: PMC7038527 DOI: 10.1016/j.isci.2020.100897] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/25/2020] [Accepted: 02/04/2020] [Indexed: 12/17/2022] Open
Abstract
Emerging evidence demonstrates that radiotherapy induces immunogenic death on tumor cells that emit immunostimulating signals resulting in tumor-specific immune responses. However, the impact of tumor features and microenvironmental factors on the efficacy of radiation-induced immunity remains to be elucidated. Herein, we use a calibrated model of tumor-effector cell interactions to investigate the potential benefits and immunological consequences of radiotherapy. Simulations analysis suggests that radiotherapy success depends on the functional tumor vascularity extent and reveals that the pre-treatment tumor size is not a consistent determinant of treatment outcomes. The one-size-fits-all approach of conventionally fractionated radiotherapy is predicted to result in some overtreated patients. In addition, model simulations also suggest that an arbitrary increase in treatment duration does not necessarily result in better tumor control. This study highlights the potential benefits of tumor-immune ecosystem profiling during treatment planning to better harness the immunogenic potential of radiotherapy. Radiotherapy success depends on radiation-induced tumor-specific immune responses Pre-treatment tumor size is not a consistent determinant of radiotherapy outcomes Increase in treatment duration does not necessarily result in better tumor control Tumor vascularity impacts antitumor efficacy of radiation-induced immune responses
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Affiliation(s)
- Juan Carlos L Alfonso
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Rebenring 56, 38106 Braunschweig, Germany
| | - Lito A Papaxenopoulou
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Rebenring 56, 38106 Braunschweig, Germany
| | - Pietro Mascheroni
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Rebenring 56, 38106 Braunschweig, Germany
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Rebenring 56, 38106 Braunschweig, Germany; Centre for Individualised Infection Medicine (CIIM), Feodor-Lynen-Straße 15, 30625 Hannover, Germany; Institute of Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Spielmannstraße 7, 38106 Braunschweig, Germany.
| | - Haralampos Hatzikirou
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Rebenring 56, 38106 Braunschweig, Germany.
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Montaseri G, Alfonso JCL, Hatzikirou H, Meyer-Hermann M. A minimal modeling framework of radiation and immune system synergy to assist radiotherapy planning. J Theor Biol 2020; 486:110099. [PMID: 31790681 DOI: 10.1016/j.jtbi.2019.110099] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 10/15/2019] [Accepted: 11/28/2019] [Indexed: 02/07/2023]
Abstract
Recent evidence indicates the ability of radiotherapy to induce local and systemic tumor-specific immune responses as a result of immunogenic cell death. However, fractionation regimes routinely used in clinical practice typically ignore the synergy between radiation and the immune system, and instead attempt to completely eradicate tumors by the direct lethal effect of radiation on cancer cells. This paradigm is expected to change in the near future due to the potential benefits of considering radiation-induced antitumor immunity during treatment planning. Towards this goal, we propose a minimal modeling framework based on key aspects of the tumor-immune system interplay to simulate the effects of radiation on tumors and the immunological consequences of radiotherapy. The impacts of tumor-associated vasculature and intratumoral oxygen-mediated heterogeneity on treatment outcomes are ininvestigated. The model provides estimates of the minimum radiation doses required for tumor eradication given a certain number of treatment fractions. Moreover, estimates of treatment duration for disease control given predetermined fractional radiation doses can be also obtained. Although theoretical in nature, this study motivates the development and establishment of immune-based decision-support tools in radiotherapy planning.
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Affiliation(s)
- Ghazal Montaseri
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany; Centre for Individualised Infection Medicine (CIIM), Hannover, Germany
| | - Juan Carlos López Alfonso
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany; Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany.
| | - Haralampos Hatzikirou
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany; Centre for Individualised Infection Medicine (CIIM), Hannover, Germany; Institute of Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Germany.
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Brady R, Enderling H. Mathematical Models of Cancer: When to Predict Novel Therapies, and When Not to. Bull Math Biol 2019; 81:3722-3731. [PMID: 31338741 PMCID: PMC6764933 DOI: 10.1007/s11538-019-00640-x] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/02/2019] [Indexed: 12/27/2022]
Abstract
The number of publications on mathematical modeling of cancer is growing at an exponential rate, according to PubMed records, provided by the US National Library of Medicine and the National Institutes of Health. Seminal papers have initiated and promoted mathematical modeling of cancer and have helped define the field of mathematical oncology (Norton and Simon in J Natl Cancer Inst 58:1735-1741, 1977; Norton in Can Res 48:7067-7071, 1988; Hahnfeldt et al. in Can Res 59:4770-4775, 1999; Anderson et al. in Comput Math Methods Med 2:129-154, 2000. https://doi.org/10.1080/10273660008833042 ; Michor et al. in Nature 435:1267-1270, 2005. https://doi.org/10.1038/nature03669 ; Anderson et al. in Cell 127:905-915, 2006. https://doi.org/10.1016/j.cell.2006.09.042 ; Benzekry et al. in PLoS Comput Biol 10:e1003800, 2014. https://doi.org/10.1371/journal.pcbi.1003800 ). Following the introduction of undergraduate and graduate programs in mathematical biology, we have begun to see curricula developing with specific and exclusive focus on mathematical oncology. In 2018, 218 articles on mathematical modeling of cancer were published in various journals, including not only traditional modeling journals like the Bulletin of Mathematical Biology and the Journal of Theoretical Biology, but also publications in renowned science, biology, and cancer journals with tremendous impact in the cancer field (Cell, Cancer Research, Clinical Cancer Research, Cancer Discovery, Scientific Reports, PNAS, PLoS Biology, Nature Communications, eLife, etc). This shows the breadth of cancer models that are being developed for multiple purposes. While some models are phenomenological in nature following a bottom-up approach, other models are more top-down data-driven. Here, we discuss the emerging trend in mathematical oncology publications to predict novel, optimal, sometimes even patient-specific treatments, and propose a convention when to use a model to predict novel treatments and, probably more importantly, when not to.
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Affiliation(s)
- Renee Brady
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33647, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33647, USA.
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Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy. Trends Cancer 2019; 5:467-474. [PMID: 31421904 DOI: 10.1016/j.trecan.2019.06.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 06/14/2019] [Accepted: 06/21/2019] [Indexed: 11/21/2022]
Abstract
In current radiation oncology practice, treatment protocols are prescribed based on the average outcomes of large clinical trials, with limited personalization and without adaptations of dose or dose fractionation to individual patients based on their individual clinical responses. Predicting tumor responses to radiation and comparing predictions against observed responses offers an opportunity for novel treatment evaluation. These analyses can lead to protocol adaptation aimed at the improvement of patient outcomes with better therapeutic ratios. We foresee the integration of mathematical models into radiation oncology to simulate individual patient tumor growth and predict treatment response as dynamic biomarkers for personalized adaptive radiation therapy (RT).
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Alfonso JCL, Berk L. Modeling the effect of intratumoral heterogeneity of radiosensitivity on tumor response over the course of fractionated radiation therapy. Radiat Oncol 2019; 14:88. [PMID: 31146751 PMCID: PMC6543639 DOI: 10.1186/s13014-019-1288-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 05/06/2019] [Indexed: 01/31/2023] Open
Abstract
Background Standard radiobiology theory of radiation response assumes a uniform innate radiosensitivity of tumors. However, experimental data show that there is significant intratumoral heterogeneity of radiosensitivity. Therefore, a model with heterogeneity was developed and tested using existing experimental data to show the potential effects from the presence of an intratumoral distribution of radiosensitivity on radiation therapy response over a protracted radiation therapy treatment course. Methods The standard radiation response curve was modified to account for a distribution of radiosensitivity, and for variations in the repopulation rates of the tumor cell subpopulations. Experimental data from the literature were incorporated to determine the boundaries of the model. The proposed model was then used to show the changes in radiosensitivity of the tumor during treatment, and the effects of fraction size, α/β ratio and variation of the repopulation rates of tumor cells. Results In the presence of an intratumoral distribution of radiosensitivity, there is rapid selection of radiation-resistant cells over a course of fractionated radiation therapy. Standard treatment fractionation regimes result in the near-complete replacement of the initial population of sensitive cells with a population of more resistant cells. Further, as treatment progresses, the tumor becomes more resistant to further radiation treatment, making each fractional dose less efficacious. A wider initial distribution induces increased radiation resistance. Hypofractionation is more efficient in a heterogeneous tumor, with increased cell kill for biologically equivalent doses, while inducing less resistance. The model also shows that a higher growth rate in resistant cells can account for the accelerated repopulation that is seen during the clinical treatment of patients. Conclusions Modeling of tumor cell survival with radiosensitivity heterogeneity alters the predicted tumor response, and explains the induction of radiation resistance by radiation treatment, the development of accelerated repopulation, and the potential beneficial effects of hypofractionation. Tumor response to treatment may be better predicted by assaying for the distribution of radiosensitivity, or the extreme of the radiosensitivity, rather than measuring the initial, general radiation sensitivity of the untreated tumor.
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Affiliation(s)
- J C L Alfonso
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
| | - L Berk
- Division of Radiation Oncology, Department of Radiology, Morsani School of Medicine at the University of South Florida, Tampa, FL, USA
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