Structural impact due to PPQEE deletion in multiple cancer associated protein - Integrin αV: An In silico exploration

Cell-Level Spatio-Temporal Model for a Bacillus Calmette–Guérin-Based Immunotherapy Treatment Protocol of Superficial Bladder Cancer

Bladder cancer is one of the most widespread types of cancer. Multiple treatments for non-invasive, superficial bladder cancer have been proposed over the last several decades with a weekly Bacillus Calmette–Guérin immunotherapy-based therapy protocol, which is considered the gold standard today. Nonetheless, due to the complexity of the interactions between the immune system, healthy cells, and cancer cells in the bladder’s microenvironment, clinical outcomes vary significantly among patients. Mathematical models are shown to be effective in predicting the treatment outcome based on the patient’s clinical condition at the beginning of the treatment. Even so, these models still have large errors for long-term treatments and patients that they do not fit. In this work, we utilize modern mathematical tools and propose a novel cell-level spatio-temporal mathematical model that takes into consideration the cell–cell and cell–environment interactions occurring in a realistic bladder’s geometric configuration in order to reduce these errors. We implement the model using the agent-based simulation approach, showing the impacts of different cancer tumor sizes and locations at the beginning of the treatment on the clinical outcomes for today’s gold-standard treatment protocol. In addition, we propose a genetic-algorithm-based approach to finding a successful and time-optimal treatment protocol for a given patient’s initial condition. Our results show that the current standard treatment protocol can be modified to produce cancer-free equilibrium for deeper cancer cells in the urothelium if the cancer cells’ spatial distribution is known, resulting in a greater success rate.

PDE based geometry model for BCG immunotherapy of bladder cancer

BCG immunotherapy has shown significant success for bladder cancer treatment, but due to the complexity of the interaction between immunity and cancer, clinical outcomes vary significantly between patients. A possible approach to overcome this difficulty may be to develop new methodologies for personally predicting the results of therapy by integrating patient data with dynamic mathematical model. We present a model describing a BCG immunotherapy dynamic taking into consideration an approximation of the bladder's geometry using PDE. We show that the proposed model takes into account the initial distribution of the cancer cells in the geometry of the bladder and as such can provide more customized treatment by providing tumor polyp depth in the urothelium. In addition, time optimal treatment protocol for the average case and recover-rate optimal, personalized treatment protocol based on initial tumor distribution have been analyzed.