1
|
Baaz M, Cardilin T, Lignet F, Zimmermann A, El Bawab S, Gabrielsson J, Jirstrand M. Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology. BMC Cancer 2023; 23:409. [PMID: 37149596 PMCID: PMC10164338 DOI: 10.1186/s12885-023-10899-y] [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: 09/05/2021] [Accepted: 04/27/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data. METHODS We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves. RESULTS The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication. CONCLUSIONS A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.
Collapse
Affiliation(s)
- Marcus Baaz
- Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden.
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
| | - Tim Cardilin
- Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden
| | - Floriane Lignet
- Translational Medicine, Quantitative Pharmacology, Merck Healthcare KGaA, Darmstadt, Germany
| | - Astrid Zimmermann
- Translation Innovation Platform Oncology, Merck Healthcare KGaA, Darmstadt, Germany
| | - Samer El Bawab
- Translational Medicine, Quantitative Pharmacology, Merck Healthcare KGaA, Darmstadt, Germany
- Present Address: Translational Medicine, Servier, Suresnes, France
| | | | - Mats Jirstrand
- Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden
| |
Collapse
|
2
|
Lorenzo G, di Muzio N, Deantoni CL, Cozzarini C, Fodor A, Briganti A, Montorsi F, Pérez-García VM, Gomez H, Reali A. Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse. iScience 2022; 25:105430. [DOI: 10.1016/j.isci.2022.105430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 09/04/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
|
3
|
Temporal optimization of radiation therapy to heterogeneous tumour populations and cancer stem cells. J Math Biol 2022; 85:51. [PMID: 36227423 DOI: 10.1007/s00285-022-01819-y] [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: 10/25/2021] [Revised: 09/19/2022] [Accepted: 09/26/2022] [Indexed: 10/17/2022]
Abstract
External beam radiation therapy is a key part of modern cancer treatments which uses high doses of radiation to destroy tumour cells. Despite its widespread usage and extensive study in theoretical, experimental, and clinical works, many questions still remain about how best to administer it. Many mathematical studies have examined optimal scheduling of radiotherapy, and most come to similar conclusions. Importantly though, these studies generally assume intratumoral homogeneity. But in recent years, it has become clear that tumours are not homogeneous masses of cancerous cells, but wildly heterogeneous masses with various subpopulations which grow and respond to treatment differently. One subpopulation of particular importance is cancer stem cells (CSCs) which are known to exhibit higher radioresistence compared with non-CSCs. Knowledge of these differences between cell types could theoretically lead to changes in optimal treatment scheduling. Only a few studies have examined this question, and interestingly, they arrive at apparent conflicting results. However, an understanding of their assumptions reveals a key difference which leads to their differing conclusions. In this paper, we generalize the problem of temporal optimization of dose distribution of radiation therapy to a two cell type model. We do so by creating a mathematical model and a numerical optimization algorithm to find the distribution of dose which leads to optimal cell kill. We then create a data set of optimization solutions and use data analysis tools to learn the relationships between model parameters and the qualitative behaviour of optimization results. Analysis of the model and discussion of biological importance are provided throughout. We find that the key factor in predicting the behaviour of the optimal distribution of radiation is the ratio between the radiosensitivities of the present cell types. These results can provide guidance for treatment in cases where clinicians have knowledge of tumour heterogeneity and of the abundance of CSCs.
Collapse
|
4
|
Ghaderi N, Jung J, Brüningk SC, Subramanian A, Nassour L, Peacock J. A Century of Fractionated Radiotherapy: How Mathematical Oncology Can Break the Rules. Int J Mol Sci 2022; 23:ijms23031316. [PMID: 35163240 PMCID: PMC8836217 DOI: 10.3390/ijms23031316] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 02/07/2023] Open
Abstract
Radiotherapy is involved in 50% of all cancer treatments and 40% of cancer cures. Most of these treatments are delivered in fractions of equal doses of radiation (Fractional Equivalent Dosing (FED)) in days to weeks. This treatment paradigm has remained unchanged in the past century and does not account for the development of radioresistance during treatment. Even if under-optimized, deviating from a century of successful therapy delivered in FED can be difficult. One way of exploring the infinite space of fraction size and scheduling to identify optimal fractionation schedules is through mathematical oncology simulations that allow for in silico evaluation. This review article explores the evidence that current fractionation promotes the development of radioresistance, summarizes mathematical solutions to account for radioresistance, both in the curative and non-curative setting, and reviews current clinical data investigating non-FED fractionated radiotherapy.
Collapse
Affiliation(s)
- Nima Ghaderi
- Department of Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA; (N.G.); (J.J.)
| | - Joseph Jung
- Department of Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA; (N.G.); (J.J.)
| | - Sarah C. Brüningk
- Machine Learning & Computational Biology Lab, Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland;
- Swiss Institute for Bioinformatics (SIB), 1015 Lausanne, Switzerland
| | - Ajay Subramanian
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA;
| | - Lauren Nassour
- Department of Radiation Oncology, University of Alabama Birmingham, Birmingham, AL 35205, USA;
| | - Jeffrey Peacock
- Department of Radiation Oncology, University of Alabama Birmingham, Birmingham, AL 35205, USA;
- Correspondence:
| |
Collapse
|
5
|
Forouzannia F, Shahrezaei V, Kohandel M. The impact of random microenvironmental fluctuations on tumor control probability. J Theor Biol 2020; 509:110494. [PMID: 32979339 DOI: 10.1016/j.jtbi.2020.110494] [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: 01/14/2020] [Revised: 08/27/2020] [Accepted: 09/12/2020] [Indexed: 11/25/2022]
Abstract
The tumor control probability (TCP) is a metric used to calculate the probability of controlling or eradicating tumors through radiotherapy. Cancer cells vary in their response to radiation, and although many factors are involved, the tumor microenvironment is a crucial one that determines radiation efficacy. The tumor microenvironment plays a significant role in cancer initiation and propagation, as well as in treatment outcome. We have developed stochastic formulations to study the impact of arbitrary microenvironmental fluctuations on TCP and extinction probability (EP), which is defined as the probability of cancer cells removal in the absence of treatment. Since the derivation of analytical solutions are not possible for complicated cases, we employ a modified Gillespie algorithm to analyze TCP and EP, considering the random variations in cellular proliferation and death rates. Our results show that increasing the standard deviation in kinetic rates initially enhances the probability of tumor eradication. However, if the EP does not reach a probability of 1, the increase in the standard deviation subsequently has a negative impact on probability of cancer cells removal, decreasing the EP over time. The greatest effect on EP has been observed when both birth and death rates are being randomly modified and are anticorrelated. In addition, similar results are observed for TCP, where radiotherapy is included, indicating that increasing the standard deviation in kinetic rates at first enhances the probability of tumor eradication. But, it has a negative impact on treatment effectiveness if the TCP does not reach a probability of 1.
Collapse
Affiliation(s)
- Farinaz Forouzannia
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada.
| | - Vahid Shahrezaei
- Department of Mathematics, Imperial College London, South Kensington Campus, London, UK
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada.
| |
Collapse
|
6
|
Song L, Zhang S, Yu S, Ma F, Wang B, Zhang C, Sun J, Mao X, Wei L. Cellular heterogeneity landscape in laryngeal squamous cell carcinoma. Int J Cancer 2020; 147:2879-2890. [PMID: 32638385 DOI: 10.1002/ijc.33192] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 06/24/2020] [Accepted: 06/29/2020] [Indexed: 12/13/2022]
Abstract
Laryngeal squamous cell carcinoma (LSCC) is a highly malignant tumor originated from respiratory system. Although there have been many improvements in therapy until now, reducing the high mortality remains difficult. Understanding the cellular heterogeneity of LSCC could contribute to improve this problem. Single-cell RNA sequencing was applied to dissect the cell composition and molecular characteristics of LSCC tissues. Immunohistochemistry staining of the LSCC tissues was performed to identify the spatial location of tumor cells. Survival analysis of marker genes was executed in The Cancer Genome Atlas to verify the correlation between each cell clusters and patients' prognosis. The LSCC tissue cells were finely grouped into various clusters, including tumor cells, immune cells, epithelial cells, fibroblasts and endothelial cells. Notably, in tumor cells, keratinocyte-like cells were in the core of tumor while malignant proliferating cells were located at the tumor edge. The malignant proliferating cells were correlated with poor prognosis. In summary, this is the first study to delineate a landscape of the LSCC intratumor heterogeneity. Our work might help researchers have a better understanding for tumor progression.
Collapse
Affiliation(s)
- Lianhao Song
- Department of Microbiology, Harbin Medical University, Harbin, China.,Wu Lien-Teh Institute, Harbin Medical University, Harbin, China
| | - Siwei Zhang
- Department of Microbiology, Harbin Medical University, Harbin, China.,Wu Lien-Teh Institute, Harbin Medical University, Harbin, China
| | - Siyang Yu
- Department of Microbiology, Harbin Medical University, Harbin, China.,Wu Lien-Teh Institute, Harbin Medical University, Harbin, China
| | - Fen Ma
- Department of Microbiology, Harbin Medical University, Harbin, China.,Wu Lien-Teh Institute, Harbin Medical University, Harbin, China
| | - Bozhi Wang
- Department of Microbiology, Harbin Medical University, Harbin, China.,Wu Lien-Teh Institute, Harbin Medical University, Harbin, China
| | - Congcong Zhang
- Department of Microbiology, Harbin Medical University, Harbin, China.,Wu Lien-Teh Institute, Harbin Medical University, Harbin, China
| | - Ji Sun
- Department of Otolaryngology, Head and Neck Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xionghui Mao
- Department of Otolaryngology, Head and Neck Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Lanlan Wei
- Department of Microbiology, Harbin Medical University, Harbin, China.,Wu Lien-Teh Institute, Harbin Medical University, Harbin, China
| |
Collapse
|
7
|
Antonopoulos M, Dionysiou D, Stamatakos G, Uzunoglu N. Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study. BMC Bioinformatics 2019; 20:442. [PMID: 31455206 PMCID: PMC6712764 DOI: 10.1186/s12859-019-2997-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/16/2019] [Indexed: 01/10/2023] Open
Abstract
Background Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. Results This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. Conclusion In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.
Collapse
Affiliation(s)
- Markos Antonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.
| | - Dimitra Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Nikolaos Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| |
Collapse
|
8
|
Stiehl T, Marciniak-Czochra A. How to Characterize Stem Cells? Contributions from Mathematical Modeling. CURRENT STEM CELL REPORTS 2019. [DOI: 10.1007/s40778-019-00155-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
9
|
Spring BQ, Lang RT, Kercher EM, Rizvi I, Wenham RM, Conejo-Garcia JR, Hasan T, Gatenby RA, Enderling H. Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies. FRONTIERS IN PHYSICS 2019; 7:46. [PMID: 31123672 PMCID: PMC6529192 DOI: 10.3389/fphy.2019.00046] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cancer photomedicine offers unique mechanisms for inducing local tumor damage with the potential to stimulate local and systemic anti-tumor immunity. Optically-active nanomedicine offers these features as well as spatiotemporal control of tumor-focused drug release to realize synergistic combination therapies. Achieving quantitative dosimetry is a major challenge, and dosimetry is fundamental to photomedicine for personalizing and tailoring therapeutic regimens to specific patients and anatomical locations. The challenge of dosimetry is perhaps greater for photomedicine than many standard therapies given the complexity of light delivery and light-tissue interactions as well as the resulting photochemistry responsible for tumor damage and drug-release, in addition to the usual intricacies of therapeutic agent delivery. An emerging multidisciplinary approach in oncology utilizes mathematical and computational models to iteratively and quantitively analyze complex dosimetry, and biological response parameters. These models are parameterized by preclinical and clinical observations and then tested against previously unseen data. Such calibrated and validated models can be deployed to simulate treatment doses, protocols, and combinations that have not yet been experimentally or clinically evaluated and can provide testable optimal treatment outcomes in a practical workflow. Here, we foresee the utility of these computational approaches to guide adaptive therapy, and how mathematical models might be further developed and integrated as a novel methodology to guide precision photomedicine.
Collapse
Affiliation(s)
- Bryan Q. Spring
- Translational Biophotonics Cluster, Northeastern University, Boston, MA, United States
- Department of Physics, Northeastern University, Boston, MA, United States
- Department of Bioengineering, Northeastern University, Boston, MA, United States
| | - Ryan T. Lang
- Translational Biophotonics Cluster, Northeastern University, Boston, MA, United States
- Department of Physics, Northeastern University, Boston, MA, United States
| | - Eric M. Kercher
- Translational Biophotonics Cluster, Northeastern University, Boston, MA, United States
- Department of Physics, Northeastern University, Boston, MA, United States
| | - Imran Rizvi
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, United States
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Robert M. Wenham
- Department of Gynecologic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - José R. Conejo-Garcia
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Tayyaba Hasan
- Wellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Robert A. Gatenby
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| |
Collapse
|
10
|
Felder SI, Hoffe SE. Redefining Rectal Cancer Priorities Through a Watch-and-Wait Paradigm: Balancing Cancer Cure and Quality of Life. J Oncol Pract 2019; 15:135-136. [PMID: 30861366 DOI: 10.1200/jop.19.00083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
|