1
|
Daniel Grass G, Alfonso JCL, Welsh E, Ahmed KA, Teer JK, Pilon-Thomas S, Harrison LB, Cleveland JL, Mulé JJ, Eschrich SA, Enderling H, Torres-Roca JF. The Radiosensitivity Index (RSI) Gene Signature Identifies Distinct Tumor Immune Microenvironment Characteristics Associated with Susceptibility to Radiotherapy. Int J Radiat Oncol Biol Phys 2022; 113:635-647. [PMID: 35289298 DOI: 10.1016/j.ijrobp.2022.03.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 12/09/2022]
Abstract
PURPOSE Radiotherapy (RT) is a mainstay of cancer care and accumulating evidence suggests the potential for synergism with components of the immune response. However, little data describes the tumor immune contexture in relation to RT-sensitivity. To address this challenge, we employed the radiation sensitivity index (RSI) gene signature to estimate the RT-sensitivity of >10,000 primary tumors and characterized their immune microenvironments in relation to the RSI. MATERIAL AND METHODS We analyzed gene expression profiles of 10,469 primary tumors (31 types) within a prospective tissue collection protocol. The RT-sensitivity of each tumor was estimated by the RSI and respective distributions were characterized. The tumor biology measured by the RSI was evaluated by differentially expressed genes (DEGs) combined with single sample gene set enrichment analysis (ssGSEA). Differences in the expression of immune regulatory molecules were assessed and deconvolution algorithms were used to estimate immune cell infiltrates in relation to the RSI. A subset (n=2,368) of tumors underwent DNA sequencing for mutational frequency characterization. RESULTS We identified a wide range of RSI values within and across various tumor types, with several demonstrating non-unimodal distributions (e.g. colon, renal, lung, prostate, esophagus, pancreas and PAM50 breast subtypes; p <0.05). Across all tumors types, stratifying RSI at a tumor type-specific median, identified 7,148 DEGs, of which 146 were coordinate in direction. Network topology analysis demonstrates RSI measures a coordinated STAT1, IRF1, and CCL4/MIP-1β transcriptional network. Tumors with an estimated high sensitivity to RT demonstrated distinct enrichment of interferon-associated signaling pathways and immune cell infiltrates (e.g. CD8+ T cells, activated natural killer cells, M1-macrophages; q < 0.05), which was in the context of diverse expression patterns of various immunoregulatory molecules. CONCLUSION This analysis describes the immune microenvironments of patient tumors in relation to the RSI gene expression signature.
Collapse
Affiliation(s)
- G Daniel Grass
- Departments of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - Juan C L Alfonso
- Departments of Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research
| | - Eric Welsh
- Departments of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - Kamran A Ahmed
- Departments of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - Jamie K Teer
- Departments of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - Shari Pilon-Thomas
- Departments of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - Louis B Harrison
- Departments of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - John L Cleveland
- Departments of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - James J Mulé
- Departments of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - Steven A Eschrich
- Departments of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA
| | - Heiko Enderling
- Departments of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA; Departments of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA.
| | - Javier F Torres-Roca
- Departments of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa FL, USA.
| |
Collapse
|
2
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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.
| |
Collapse
|
3
|
Parsai S, Qiu RLJ, Qi P, Alfonso JCL, Donaghue J, Murray E, Majkszak D, Dorio N, Fuller CD, Brock K, Koyfman S, Woody N, Joshi N, Scott JG. Technical Note: A step-by-step guide to Temporally Feathered Radiation Therapy planning for head and neck cancer. J Appl Clin Med Phys 2020; 21:209-215. [PMID: 32383296 PMCID: PMC7386183 DOI: 10.1002/acm2.12893] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 03/20/2020] [Accepted: 04/06/2020] [Indexed: 12/26/2022] Open
Abstract
Purpose Prior in silico simulations propose that Temporally Feathered Radiation Therapy (TFRT) may reduce toxicity related to head and neck radiation therapy. In this study we demonstrate a step‐by‐step guide to TFRT planning with modern treatment planning systems. Methods One patient with oropharyngeal cancer planned for definitive radiation therapy using intensity‐modulated radiation therapy (IMRT) techniques was replanned using the TFRT technique. Five organs at risk (OAR) were identified to be feathered. A “base plan” was first created based on desired planning target volumes (PTV) coverage, plan conformality, and OAR constraints. The base plan was then re‐optimized by modifying planning objectives, to generate five subplans. All beams from each subplan were imported onto one trial to create the composite TFRT plan. The composite TFRT plan was directly compared with the non‐TFRT IMRT plan. During plan assessment, the composite TFRT was first evaluated followed by each subplan to meet preset compliance criteria. Results The following organs were feathered: oral cavity, right submandibular gland, left submandibular gland, supraglottis, and OAR Pharynx. Prescription dose PTV coverage (>95%) was met in each subplan and the composite TFRT plan. Expected small variations in dose were observed among the plans. The percent variation between the high fractional dose and average low fractional dose was 29%, 28%, 24%, 19%, and 10% for the oral cavity, right submandibular, left submandibular, supraglottis, and OAR pharynx nonoverlapping with the PTV. Conclusions Temporally Feathered Radiation Therapy planning is possible with modern treatment planning systems. Modest dosimetric changes are observed with TFRT planning compared with non‐TFRT IMRT planning. We await the results of the current prospective trial to seeking to demonstrate the feasibility of TFRT in the modern clinical workflow (NCT03768856). Further studies will be required to demonstrate the potential benefit of TFRT over non‐TFRT IMRT Planning.
Collapse
Affiliation(s)
- Shireen Parsai
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Richard L J Qiu
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, USA
| | - Peng Qi
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Juan C L Alfonso
- Braunschweig Integrated Centre of Systems Biology, Hemholtz Centre of Infection Research, Braunschweig, Germany
| | - Jeremy Donaghue
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Eric Murray
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - David Majkszak
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Nicole Dorio
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Kristy Brock
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Shlomo Koyfman
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Neil Woody
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Nikhil Joshi
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jacob G Scott
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| |
Collapse
|
4
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
5
|
Alfonso JCL, Talkenberger K, Seifert M, Klink B, Hawkins-Daarud A, Swanson KR, Hatzikirou H, Deutsch A. The biology and mathematical modelling of glioma invasion: a review. J R Soc Interface 2018; 14:rsif.2017.0490. [PMID: 29118112 DOI: 10.1098/rsif.2017.0490] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 10/17/2017] [Indexed: 12/13/2022] Open
Abstract
Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.
Collapse
Affiliation(s)
- J C L Alfonso
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.,Centre for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - K Talkenberger
- Centre for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - M Seifert
- Institute for Medical Informatics and Biometry, Technische Universität Dresden, Germany.,National Center for Tumor Diseases (NCT), Dresden, Germany
| | - B Klink
- Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Germany.,National Center for Tumor Diseases (NCT), Dresden, Germany.,German Cancer Consortium (DKTK), partner site, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - A Hawkins-Daarud
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, USA
| | - K R Swanson
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, USA
| | - H Hatzikirou
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.,Centre for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - A Deutsch
- Centre for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| |
Collapse
|
6
|
Alfonso JCL, Köhn-Luque A, Stylianopoulos T, Feuerhake F, Deutsch A, Hatzikirou H. Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights. Sci Rep 2016; 6:37283. [PMID: 27876890 PMCID: PMC5120360 DOI: 10.1038/srep37283] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 10/26/2016] [Indexed: 12/18/2022] Open
Abstract
Gliomas are highly invasive brain tumours characterised by poor prognosis and limited response to therapy. There is an ongoing debate on the therapeutic potential of vaso-modulatory interventions against glioma invasion. Prominent vasculature-targeting therapies involve tumour blood vessel deterioration and normalisation. The former aims at tumour infarction and nutrient deprivation induced by blood vessel occlusion/collapse. In contrast, the therapeutic intention of normalising the abnormal tumour vasculature is to improve the efficacy of conventional treatment modalities. Although these strategies have shown therapeutic potential, it remains unclear why they both often fail to control glioma growth. To shed some light on this issue, we propose a mathematical model based on the migration/proliferation dichotomy of glioma cells in order to investigate why vaso-modulatory interventions have shown limited success in terms of tumour clearance. We found the existence of a critical cell proliferation/diffusion ratio that separates glioma responses to vaso-modulatory interventions into two distinct regimes. While for tumours, belonging to one regime, vascular modulations reduce the front speed and increase the infiltration width, for those in the other regime, the invasion speed increases and infiltration width decreases. We discuss how these in silico findings can be used to guide individualised vaso-modulatory approaches to improve treatment success rates.
Collapse
Affiliation(s)
- J C L Alfonso
- Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, Braunschweig, Germany.,Center for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - A Köhn-Luque
- Department of Biostatistics, Faculty of Medicine, University of Oslo, Norway.,BigInsight, Centre for Research-based Innovation (SFI), Oslo, Norway
| | - T Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - F Feuerhake
- Institute of Pathology, Medical School of Hannover, Germany.,Institute of Neuropathology, University Clinic Freiburg, Germany
| | - A Deutsch
- Center for Information Services and High Performance Computing, Technische Universität Dresden, Germany
| | - H Hatzikirou
- Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, Braunschweig, Germany
| |
Collapse
|
7
|
Hatzikirou H, Alfonso JCL, Mühle S, Stern C, Weiss S, Meyer-Hermann M. Cancer therapeutic potential of combinatorial immuno- and vasomodulatory interventions. J R Soc Interface 2016; 12:rsif.2015.0439. [PMID: 26510827 DOI: 10.1098/rsif.2015.0439] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Currently, most of the basic mechanisms governing tumour-immune system interactions, in combination with modulations of tumour-associated vasculature, are far from being completely understood. Here, we propose a mathematical model of vascularized tumour growth, where the main novelty is the modelling of the interplay between functional tumour vasculature and effector cell recruitment dynamics. Parameters are calibrated on the basis of different in vivo immunocompromised Rag1(-/-) and wild-type (WT) BALB/c murine tumour growth experiments. The model analysis supports that tumour vasculature normalization can be a plausible and effective strategy to treat cancer when combined with appropriate immunostimulations. We find that improved levels of functional tumour vasculature, potentially mediated by normalization or stress alleviation strategies, can provide beneficial outcomes in terms of tumour burden reduction and growth control. Normalization of tumour blood vessels opens a therapeutic window of opportunity to augment the antitumour immune responses, as well as to reduce intratumoral immunosuppression and induced hypoxia due to vascular abnormalities. The potential success of normalizing tumour-associated vasculature closely depends on the effector cell recruitment dynamics and tumour sizes. Furthermore, an arbitrary increase in the initial effector cell concentration does not necessarily imply better tumour control. We evidence the existence of an optimal concentration range of effector cells for tumour shrinkage. Based on these findings, we suggest a theory-driven therapeutic proposal that optimally combines immuno- and vasomodulatory interventions.
Collapse
Affiliation(s)
- H Hatzikirou
- Center for Advancing Electronics, Technische Universität Dresden, 01062 Dresden, Germany Center for Information Services and High Performance Computing, Technische Universität Dresden, 01062 Dresden, Germany Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Center for Infectious Research, Inhoffenstrasse 7, 38124 Braunschweig, Germany
| | - J C L Alfonso
- Center for Advancing Electronics, Technische Universität Dresden, 01062 Dresden, Germany Center for Information Services and High Performance Computing, Technische Universität Dresden, 01062 Dresden, Germany
| | - S Mühle
- Molecular Immunology, Helmholtz Center for Infectious Research, Inhoffenstrasse 7, 38124 Braunschweig, Germany
| | - C Stern
- Molecular Immunology, Helmholtz Center for Infectious Research, Inhoffenstrasse 7, 38124 Braunschweig, Germany
| | - S Weiss
- Molecular Immunology, Helmholtz Center for Infectious Research, Inhoffenstrasse 7, 38124 Braunschweig, Germany Institute of Immunology, Medical School Hannover, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany
| | - M Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Center for Infectious Research, Inhoffenstrasse 7, 38124 Braunschweig, Germany Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, 38106 Braunschweig, Germany
| |
Collapse
|
8
|
Alfonso JCL, Herrero MA, Núñez L. A dose-volume histogram based decision-support system for dosimetric comparison of radiotherapy treatment plans. Radiat Oncol 2015; 10:263. [PMID: 26715096 PMCID: PMC4696205 DOI: 10.1186/s13014-015-0569-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 12/08/2015] [Indexed: 12/05/2022] Open
Abstract
Background The choice of any radiotherapy treatment plan is usually made after the evaluation of a few preliminary isodose distributions obtained from different beam configurations. Despite considerable advances in planning techniques, such final decision remains a challenging task that would greatly benefit from efficient and reliable assessment tools. Methods For any dosimetric plan considered, data on dose-volume histograms supplied by treatment planning systems are used to provide estimates on planning target coverage as well as on sparing of organs at risk and the remaining healthy tissue. These partial metrics are then combined into a dose distribution index (DDI), which provides a unified, easy-to-read score for each competing radiotherapy plan. To assess the performance of the proposed scoring system, DDI figures for fifty brain cancer patients were retrospectively evaluated. Patients were divided in three groups depending on tumor location and malignancy. For each patient, three tentative plans were designed and recorded during planning, one of which was eventually selected for treatment. We thus were able to compare the plans with better DDI scores and those actually delivered. Results When planning target coverage and organs at risk sparing are considered as equally important, the tentative plan with the highest DDI score is shown to coincide with that actually delivered in 32 of the 50 patients considered. In 15 (respectively 3) of the remaining 18 cases, the plan with highest DDI value still coincides with that actually selected, provided that organs at risk sparing is given higher priority (respectively, lower priority) than target coverage. Conclusions DDI provides a straightforward and non-subjective tool for dosimetric comparison of tentative radiotherapy plans. In particular, DDI readily quantifies differences among competing plans with similar-looking dose-volume histograms and can be easily implemented for any tumor type and localization, irrespective of the planning system and irradiation technique considered. Moreover, DDI permits to estimate the dosimetry impact of different priorities being assigned to sparing of organs at risk or to better target coverage.
Collapse
Affiliation(s)
- J C L Alfonso
- Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Nöthnitzer Str. 46, Dresden, 01062, Germany.
| | - M A Herrero
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Universidad Complutense de Madrid (UCM), Ciudad Universitaria, Plaza Ciencias 3, Madrid, 28040, Spain.
| | - L Núñez
- Radiophysics Department, Hospital Universitario Puerta de Hierro (HUPH), Calle Manuel de Falla 1 Majadahonda, Madrid, 28222, Spain.
| |
Collapse
|
9
|
Abstract
Despite recent advances in the field of Oncoimmunology, the success potential of immunomodulatory therapies against cancer remains to be elucidated. One of the reasons is the lack of understanding on the complex interplay between tumor growth dynamics and the associated immune system responses. Toward this goal, we consider a mathematical model of vascularized tumor growth and the corresponding effector cell recruitment dynamics. Bifurcation analysis allows for the exploration of model's dynamic behavior and the determination of these parameter regimes that result in immune-mediated tumor control. In this work, we focus on a particular tumor evasion regime that involves tumor and effector cell concentration oscillations of slowly increasing and decreasing amplitude, respectively. Considering a temporal multiscale analysis, we derive an analytically tractable mapping of model solutions onto a weakly negatively damped harmonic oscillator. Based on our analysis, we propose a theory-driven intervention strategy involving immunostimulating and immunosuppressive phases to induce long-term tumor control.
Collapse
Affiliation(s)
- A I Reppas
- a Center for Advancing Electronics; Technische Universität Dresden ; Dresden , Germany
| | - J C L Alfonso
- a Center for Advancing Electronics; Technische Universität Dresden ; Dresden , Germany
| | - H Hatzikirou
- a Center for Advancing Electronics; Technische Universität Dresden ; Dresden , Germany
| |
Collapse
|
10
|
Alfonso JCL, Buttazzo G, García-Archilla B, Herrero MA, Núñez L. Selecting radiotherapy dose distributions by means of constrained optimization problems. Bull Math Biol 2014; 76:1017-44. [PMID: 24599739 DOI: 10.1007/s11538-014-9945-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 02/12/2014] [Indexed: 12/31/2022]
Abstract
The main steps in planning radiotherapy consist in selecting for any patient diagnosed with a solid tumor (i) a prescribed radiation dose on the tumor, (ii) bounds on the radiation side effects on nearby organs at risk and (iii) a fractionation scheme specifying the number and frequency of therapeutic sessions during treatment. The goal of any radiotherapy treatment is to deliver on the tumor a radiation dose as close as possible to that selected in (i), while at the same time conforming to the constraints prescribed in (ii). To this day, considerable uncertainties remain concerning the best manner in which such issues should be addressed. In particular, the choice of a prescription radiation dose is mostly based on clinical experience accumulated on the particular type of tumor considered, without any direct reference to quantitative radiobiological assessment. Interestingly, mathematical models for the effect of radiation on biological matter have existed for quite some time, and are widely acknowledged by clinicians. However, the difficulty to obtain accurate in vivo measurements of the radiobiological parameters involved has severely restricted their direct application in current clinical practice.In this work, we first propose a mathematical model to select radiation dose distributions as solutions (minimizers) of suitable variational problems, under the assumption that key radiobiological parameters for tumors and organs at risk involved are known. Second, by analyzing the dependence of such solutions on the parameters involved, we then discuss the manner in which the use of those minimizers can improve current decision-making processes to select clinical dosimetries when (as is generally the case) only partial information on model radiosensitivity parameters is available. A comparison of the proposed radiation dose distributions with those actually delivered in a number of clinical cases strongly suggests that solutions of our mathematical model can be instrumental in deriving good quality tests to select radiotherapy treatment plans in rather general situations.
Collapse
Affiliation(s)
- J C L Alfonso
- Departamento de Matemática Aplicada, Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid (UCM), Plaza de las Ciencias s/n, 28040, Madrid, Spain,
| | | | | | | | | |
Collapse
|