Mathematical modeling of BCG-based bladder cancer treatment using socio-demographics

Savchenko, Elizaveta; Rosenfeld, Ariel; Bunimovich-Mendrazitsky, Svetlana

doi:10.1038/s41598-023-45581-7

Cited by 3 publications

(3 citation statements)

References 64 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, cancer cells continue to mutate over time which causes a wide range of outcomes and can significantly alter the course of metastasis formation. Third, taking into consideration additional clinical data about the patient such as gender, background diseases, smoking, and others, it will be possible to obtain more accurate the location of cancer metastases ( 50 ). Forth, this study was limited by its sample size (of 10 patients) and the heterogeneity of the patient population.…”

Section: Discussionmentioning

confidence: 99%

Predicting lung cancer's metastats' locations using bioclinical model

Lazebnik,

Bunimovich-Mendrazitsky

2024

Front. Med.

Self Cite

View full text Add to dashboard Cite

BackgroundLung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches.MethodsThis study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients.FindingsThe validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment.InterpretationThis study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting lung cancer's metastats' locations using bioclinical model

Lazebnik,

Bunimovich-Mendrazitsky

2024

Front. Med.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similar work has been undertaken with PARP inhibitors for the treatment of ovarian cancer ( 14 ). In bladder cancer, ODE models have been used to understand immunotherapy response ( 15 – 17 ). We previously analyzed a model of FGFR3 mutation in bladder cancer, considering the therapeutic efficacy of combination ICI and a small molecule inhibitor (SMI) of FGFR3 ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model

Bergman,

Wang,

Trujillo

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

Bladder cancer is an increasingly prevalent global disease that continues to cause morbidity and mortality despite recent advances in treatment. Immune checkpoint inhibitors (ICI) and fibroblast growth factor receptor (FGFR)-targeted therapeutics have had modest success in bladder cancer when used as monotherapy. Emerging data suggests that the combination of these two therapies could lead to improved clinical outcomes, but the optimal strategy for combining these agents remains uncertain. Mathematical models, specifically agent-based models (ABMs), have shown recent successes in uncovering the multiscale dynamics that shape the trajectory of cancer. They have enabled the optimization of treatment methods and the identification of novel therapeutic strategies. To assess the combined effects of anti-PD-1 and anti-FGFR3 small molecule inhibitors (SMI) on tumor growth and the immune response, we built an ABM that captures key facets of tumor heterogeneity and CD8+ T cell phenotypes, their spatial interactions, and their response to therapeutic pressures. Our model quantifies how tumor antigenicity and FGFR3 activating mutations impact disease trajectory and response to anti-PD-1 antibodies and anti-FGFR3 SMI. We find that even a small population of weakly antigenic tumor cells bearing an FGFR3 mutation can render the tumor resistant to combination therapy. However, highly antigenic tumors can overcome therapeutic resistance mediated by FGFR3 mutation. The optimal therapy depends on the strength of the FGFR3 signaling pathway. Under certain conditions, ICI alone is optimal; in others, ICI followed by anti-FGFR3 therapy is best. These results indicate the need to quantify FGFR3 signaling and the fitness advantage conferred on bladder cancer cells harboring this mutation. This ABM approach may enable rationally designed treatment plans to improve clinical outcomes.

show abstract

“…Hospitals and other healthcare service providers (HSPs) constantly endeavor to provide the highest quality of care to patients in a complex business and economic context ( 1 , 2 ). Personalized medicine emerging as a promising approach to achieving this goal since it tailors treatments to individual patients based on their unique properties such as genetic makeup ( 3 ), lifestyle ( 4 ), socio-demographic status ( 5 ), offering the potential to optimize therapeutic outcomes and minimize adverse effects ( 6 ). However, the implementation of personalized medicine in resource-constrained hospitals poses significant challenges ( 7 – 9 ).…”

Section: Introductionmentioning

confidence: 99%

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

Savchenko,

Bunimovich-Mendrazitsky

2024

Front. Med.

View full text Add to dashboard Cite

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

show abstract

Mathematical modeling of BCG-based bladder cancer treatment using socio-demographics

Cited by 3 publications

References 64 publications

Predicting lung cancer's metastats' locations using bioclinical model

Predicting lung cancer's metastats' locations using bioclinical model

Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

Contact Info

Product

Resources

About