Mathematical modeling approach of cancer immunoediting reveals new insights in targeted-therapy and timing plan of cancer treatment

2021 ◽  
Vol 152 ◽  
pp. 111349
Author(s):  
Mojtaba Ghanizadeh ◽  
Seyed Peyman Shariatpanahi ◽  
Bahram Goliaei ◽  
Curzio Rüegg
2020 ◽  
Vol 20 (2) ◽  
pp. 130-145 ◽  
Author(s):  
Keywan Mortezaee ◽  
Masoud Najafi ◽  
Bagher Farhood ◽  
Amirhossein Ahmadi ◽  
Dheyauldeen Shabeeb ◽  
...  

Cancer is one of the most complicated diseases in present-day medical science. Yearly, several studies suggest various strategies for preventing carcinogenesis. Furthermore, experiments for the treatment of cancer with low side effects are ongoing. Chemotherapy, targeted therapy, radiotherapy and immunotherapy are the most common non-invasive strategies for cancer treatment. One of the most challenging issues encountered with these modalities is low effectiveness, as well as normal tissue toxicity for chemo-radiation therapy. The use of some agents as adjuvants has been suggested to improve tumor responses and also alleviate normal tissue toxicity. Resveratrol, a natural flavonoid, has attracted a lot of attention for the management of both tumor and normal tissue responses to various modalities of cancer therapy. As an antioxidant and anti-inflammatory agent, in vitro and in vivo studies show that it is able to mitigate chemo-radiation toxicity in normal tissues. However, clinical studies to confirm the usage of resveratrol as a chemo-radioprotector are lacking. In addition, it can sensitize various types of cancer cells to both chemotherapy drugs and radiation. In recent years, some clinical studies suggested that resveratrol may have an effect on inducing cancer cell killing. Yet, clinical translation of resveratrol has not yielded desirable results for the combination of resveratrol with radiotherapy, targeted therapy or immunotherapy. In this paper, we review the potential role of resveratrol for preserving normal tissues and sensitization of cancer cells in combination with different cancer treatment modalities.


2020 ◽  
Author(s):  
Valerie M. Carlberg ◽  
Olivia M. T. Davies ◽  
Heather A. Brandling‐Bennett ◽  
Sarah E. S. Leary ◽  
Jennifer T. Huang ◽  
...  

2021 ◽  
pp. 107140
Author(s):  
Iulia Martina Bulai ◽  
Ana Cristina Esteves ◽  
Fernanda Lima ◽  
Ezio Venturino

2021 ◽  
Author(s):  
Javier C. Urcuyo ◽  
Susan Christine Massey ◽  
Andrea Hawkins-Daarud ◽  
Bianca-Maria Marin ◽  
Danielle M. Burgenske ◽  
...  

AbstractGlioblastoma is the most malignant primary brain tumor with significant heterogeneity and a limited number of effective therapeutic options. Many investigational targeted therapies have failed in clinical trials, but it remains unclear if this results from insensitivity to therapy or poor drug delivery across the blood-brain barrier. Using well-established EGFR-amplified patient-derived xenograft (PDX) cell lines, we investigated this question using an EGFR-directed therapy. With only bioluminescence imaging, we used a mathematical model to quantify the heterogeneous treatment response across the three PDX lines (GBM6, GBM12, GBM39). Our model estimated the primary cause of intracranial treatment response for each of the lines, and these findings were validated with parallel experimental efforts. This mathematical modeling approach can be used as a useful complementary tool that can be widely applied to many more PDX lines. This has the potential to further inform experimental efforts and reduce the cost and time necessary to make experimental conclusions.Author summaryGlioblastoma is a deadly brain cancer that is difficult to treat. New therapies often fail to surpass the current standard of care during clinical trials. This can be attributed to both the vast heterogeneity of the disease and the blood-brain barrier, which may or may not be disrupted in various regions of tumors. Thus, while some cancer cells may develop insensitivity in the presence of a drug due to heterogeneity, other tumor areas are simply not exposed to the drug. Being able to understand to what extent each of these is driving clinical trial results in individuals may be key to advancing novel therapies. To address this challenge, we used mathematical modeling to study the differences between three patient-derived tumors in mice. With our unique approach, we identified the reason for treatment failure in each patient tumor. These results were validated through rigorous and time-consuming experiments, but our mathematical modeling approach allows for a cheaper, quicker, and widely applicable way to come to similar conclusions.


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