Math model developed to personalize cancer treatments

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By Ramona Leitao

Researchers at UW, Dr. Michelle Przedborski and Mohammad Konandel, designed an advanced mathematical model that granted greater insight into interactions between the immune system and Cancer stem cells (CSCs), which trigger tumour growth.

In addition, the model demonstrates the inefficacy of applying traditional cancer treatments like chemotherapy and radiotherapy independently in targeting specific CSCs. 

This model shifted treatments toward an immunotherapy-based approach, which uses the body’s immune system to combat cancer in addition to traditional therapy. 

Przedborski, a postdoctoral fellow in Waterloo’s Department of Applied Mathematics, said that the preliminary step was better comprehending the underlying interactions among different cell types.  

Przedborski further added that garnering relevant experimental data was important for effective model calibration. Experimental data has been very helpful in determining the general or patient-specific variables that shed valuable information as to how a particular patient is likely to respond to different methods of treatments.

“Using different experimental measures to capture the patient-specific tumour micro-environment parameters is crucial to achieving the personalized cancer treatment mechanisms”, Dr. Przedborski said.

In other words, the model requires an ample selection of patients’ attributes that are biologically meaningful and have clinical applicability. 

That said, if the model captures too many of a patient’s biological parameters for instance, protein level interactions, the large number of variables introduce uncertainties into the system leading to undesirable outcomes. 

Therefore, it is imperative to balance having enough parameters to capture all the essential information, and at the same time ensure not to feed the model with extra parameters that have little to no role in providing a platform for more specific cancer treatments.

Przedborski mentioned the significance of collaboration with doctors, clinicians, biologists and immunologists, as well as the importance of how experts from various disciplines, more often than not, work in tandem for the development of predictive mathematical models.

One of the key challenges is to thoroughly comprehend the patients’ responses to treatments and tailor the dosages and schedules to the optimum level. 

The developed prototype is expected to play a vital role in bridging the gap between patients’ responsiveness and optimal treatments.

When asked about the future enhancements to the current model, Przedborski emphasized that the model’s performance could be elevated by incorporating all types of immune cell populations including cytokines, a broad category of proteins that help the immune cells differentiate into more specialized cells and also by taking into account the spatial localization of the tumor or immune cell population.

The developed model allows UW researchers to gain a much deeper understanding of the different treatment methods for patients under the given circumstances and the interactions among different cell types. This could very well lead to a robust personalized cancer treatment strategy in the near future.

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