Modeling Myxofibrosarcoma: Where Do We Stand and What Is Missing?
Abstract
:Simple Summary
Abstract
1. Introduction
1.1. Clinical Description
1.2. Modeling Myxofibrosarcoma: Where Do We Stand?
- Biological Complexity: MFS exhibits intricate biological behavior, including tumor heterogeneity, microenvironment interactions, and genetic variability. Models should aim to capture this complexity to provide insights that mirror real-world scenarios.
- Histopathological Features: MFS has distinct histopathological features. Models should incorporate these features, such as the presence of myxoid and fibrous components, to accurately simulate the tumor’s appearance and behavior.
- Genetic and Molecular Characteristics: Understanding the genetic and molecular underpinnings of MFS is crucial. Models should integrate genomic data to simulate the mutations and molecular pathways driving tumor growth and progression.
- Tumor Microenvironment: It is important to consider the interactions between tumor cells and their microenvironment, including immune cells, blood vessels, and extracellular matrix components. Modeling the tumor microenvironment can shed light on immune responses and potential therapeutic targets.
- Invasion and Metastasis: MFS is known for its infiltrative behavior and potential for metastasis. Models should replicate these aspects to understand how the tumor invades surrounding tissues and spreads to distant sites.
- Long-Term Behavior and Recurrence: It is important to incorporate dynamics that account for tumor growth over time, potential recurrence, and regrowth after treatment. Long-term modeling can help assess treatment strategies and disease management.
- Clinical Data Integration: Utilizing clinical data to validate and refine models is essential. Patient data, such as treatment responses, outcomes, and demographic information, can enhance the relevance of the simulations.
- Therapeutic Interventions: This involves incorporating various treatment modalities, including surgery, radiation, and chemotherapy. Models can help predict the efficacy of different treatments and optimize therapeutic strategies.
- Patient-Specific Modeling: This entails exploring ways to personalize models using individual patient data. Patient-specific modeling can guide treatment decisions and predict outcomes based on a patient’s unique characteristics.
- Validation and Benchmarking: Developing protocols for validating and benchmarking the accuracy of models can be used to compare model predictions with clinical observations and experimental data to ensure reliability.
- Interdisciplinary Collaboration: Modeling MFS requires collaboration among researchers with diverse expertise, including oncology, pathology, biology, mathematics, physics, and computational sciences. A multidisciplinary approach ensures a comprehensive understanding of the disease.
- Ethical Considerations: It is important to acknowledge ethical concerns related to patient data usage, model transparency, and potential clinical applications and ensure that the models adhere to ethical standards and guidelines.
- Model Interpretability: This involves striving for models that are interpretable and can provide actionable insights for clinicians and researchers. Transparent models can help bridge the gap between computational predictions and clinical decision-making.
2. Available MFS Models
2.1. MFS In Vitro Cell Cultures
2.1.1. Immortalized MFS Cells Grown in Monolayer Cultures
2.1.2. Conclusions and Further Outlook for Immortalized MFS Monolayer Cultures
2.2. MFS Cells in Three-Dimensional MFS Cultures
2.2.1. MFS Spheroids and Sarcospheres
2.2.2. MFS Cells Grown on 3D Scaffolds
2.2.3. Conclusions and Further Outlook for MFS 3D Cultures
2.3. In Vivo MFS Models
2.3.1. Cell Line-Derived Xenograft
Cell Line-Derived Xenograft in CAM
Mouse Models for Superficial MFS
Mouse Model for Deep MFS
MFS Mouse Lung Metastasis Model
2.3.2. Heterotransplanted Tumor
Patient-Derived Xenograft in CAM
Patient-Derived Xenograft (PDX) Mouse Model
Conclusions and Further Outlook for MFS In Vivo Models
3. Veterinary Research
4. What Is Missing in Myxofibrosarcoma Modeling?
5. Conclusions and Final Remarks
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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# | Cell Line | Tumor Characteristics | Gender | Age at the Time of Surgery (years) | (Primary Tumor/ Local Recurrence/ Metastasis) | Cell Morphology | References |
---|---|---|---|---|---|---|---|
1 | SFT85-06 | un. | M | 65 | Primary tumor | Double population | Iwasaki et al., 1992 [47] |
2 | SFT79-08 | un. | M | 80 | Primary tumor | Double population | Iwasaki et al., 1992 [47] |
3 | SFT81-12 | un. | M | 85 | Primary tumor | Double population | Iwasaki et al., 1992 [47] |
4 | OH931 | Deep mass in the thigh grade II and III | F | 73 | Primary tumor | Single population | Krause et al., 1997 [48] |
5 | Nara-F | Mass of the uterus | F | 68 | Primary tumor | Double–single population | Kiyozuka et al., 2001 [49] |
6 | Nara-H | Mass of the uterus | F | 68 | Primary tumor | Double–single population | Kiyozuka et al., 2001 [49] |
7 | NMFH-1 | Supeficial left knee | M | 89 | Recurrence | Double population | Kawashima et al., 2005 [50] |
8 | CNIO-BG | un. | un. | un. | un. | un. | Moneo et al., 2007 [51] |
9 | NMFH-2 | Upper left arm | M | 79 | Primary tumor | Double population | Ariizumi et al., 2009 [52] |
10 | MUG-Myx1 | Thorax | M | 66 | Recurrence | Double population | Lohberger et al., 2013 [53] |
11 | Shef-MFS 01 | Upper limb | M | 73 | un. | Single population | Salawu et al., 2016 [54] |
12 | Shef-MFS 02 | Upper limb | M | 73 | un. | Single population | Salawu et al., 2016 [54] |
13 | MXF8500 | un. | un. | un. | un. | Single population | Okada et al., 2016 [55] |
14 | MXF2734 | un. | un. | un. | un. | Single population | Okada et al., 2016 [55] |
15 | MXF800 | un. | un. | un. | un. | un. | Okada et al., 2016 [55] |
16 | MXF9100 | un. | un. | un. | un. | Single population | Okada et al., 2016 [55] |
17 | MUG-Myx2a | G3 | F | 94 | Primary tumor | Single population | Lohberger et al., 2017 [13] |
18 | MUG-Myx2b | G3 | F | 94 | Primary tumor | Single population | Lohberger et al., 2017 [13] |
19 | MF-1 | G3 thigh | F | 66 | Recurrence | Single population | De Vita et al., 2017 [56] |
20 | MF-2 | G3 of the knee | M | 69 | Recurrence | Single population | De Vita et al., 2017 [56] |
21 | MF-3 | G3 arm | M | 64 | Primary tumor | Single population | De Vita et al., 2017 [56] |
22 | IM-MFS-1 | Knee | M | 69 | Recurrence | Single population | Miserocchi et al., 2018 [57] |
23 | NCC-MFS1-C1 | Subcutaneous forearm | M | 82 | Recurrence | Single population | Kito et al., 2019 [58] |
24 | S57 | un. | un. | un. | un. | Single population | Piano et al., 2020 [59] |
25 | 8000s | un. | un. | un. | un. | un. | Li et al., 2020 [60] |
26 | 8500 | un. | un. | un. | un. | un. | Li et al., 2020 [60] |
27 | 9172 | un. | un. | un. | un. | un. | Li et al., 2020 [60] |
28 | 2734 | un. | un. | un. | un. | un. | Li et al., 2020 [60] |
29 | 3672-3 | un. | un. | un. | un. | un. | Li et al., 2020 [60] |
30 | T60 | Grade 3 | M | 62 | Primary | un. | Lohberger et al., 2020 [61] |
31 | NCC-MFS2-C1 | un. | M | 71 | Primary | Single population | Noguchi et al., 2021 [62] |
32 | NCC-MFS3-C1 | un. | F | 74 | Primary | Single population | Tsuchiya et al., 2021 [63] |
33 | NCC-MFS4-C1 | un. | F | 65 | Recurrence | Single population | Yoshimatsu et al., 2021 [64] |
34 | NCC-MFS5-C1 | un. | M | 60 | Primary | Single population | Tsuchiya et al., 2022 [65] |
35 | NCC-MFS6-C1 | un. | F | 85 | Primary | Single population | Yoshimatsu et al., 2022 [66] |
# | Cell Line | Tumor Characteristics | Gender | Age at the Time of Surgery (years) | (Primary Tumor/Local Recurrence/Metastasis) | Cell Morphology | References |
---|---|---|---|---|---|---|---|
1 | WCM197 | un. | F | un. | Primary tumor | Single population | Pauli et al., 2022 [67] |
2 | USZ20-MFS1 | un. | M | 66 | Recurrence after radiation | Double population | Chen et al., 2023 [68] |
3 | USZ20-MFS1 | un. | M | 71 | Primary tumor after radiation | Single population | Chen et al., 2023 [68] |
4 | MFS-SN1 | un. | un. | un. | un. | un. | Kerkhoff et al., 2023 [69] |
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Lucarelli, E.; De Vita, A.; Bellotti, C.; Frisoni, T.; Vanni, S.; Guerrieri, A.N.; Pannella, M.; Mercatali, L.; Gambarotti, M.; Duchi, S.; et al. Modeling Myxofibrosarcoma: Where Do We Stand and What Is Missing? Cancers 2023, 15, 5132. https://doi.org/10.3390/cancers15215132
Lucarelli E, De Vita A, Bellotti C, Frisoni T, Vanni S, Guerrieri AN, Pannella M, Mercatali L, Gambarotti M, Duchi S, et al. Modeling Myxofibrosarcoma: Where Do We Stand and What Is Missing? Cancers. 2023; 15(21):5132. https://doi.org/10.3390/cancers15215132
Chicago/Turabian StyleLucarelli, Enrico, Alessandro De Vita, Chiara Bellotti, Tommaso Frisoni, Silvia Vanni, Ania Naila Guerrieri, Micaela Pannella, Laura Mercatali, Marco Gambarotti, Serena Duchi, and et al. 2023. "Modeling Myxofibrosarcoma: Where Do We Stand and What Is Missing?" Cancers 15, no. 21: 5132. https://doi.org/10.3390/cancers15215132
APA StyleLucarelli, E., De Vita, A., Bellotti, C., Frisoni, T., Vanni, S., Guerrieri, A. N., Pannella, M., Mercatali, L., Gambarotti, M., Duchi, S., Miserocchi, G., Maioli, M., Liverani, C., & Ibrahim, T. (2023). Modeling Myxofibrosarcoma: Where Do We Stand and What Is Missing? Cancers, 15(21), 5132. https://doi.org/10.3390/cancers15215132