Novel Mathematical Modeling Offers Hope for Breast Cancer Patients Resistance to Treatment


A groundbreaking study led by Monash University scientists is utilizing mathematical modeling to predict the effectiveness of new combination therapies for breast cancer patients who no longer respond to conventional treatments. The research, conducted by the Monash Biomedicine Discovery Institute (BDI) and published in npj Precision Oncology, focused on breast cancer driven by a specific protein called PI3K and explored how new combination therapies could effectively inhibit its activity.

The study, titled “Integrative modeling uncovers p21-driven drug resistance and prioritizes therapies for PIK3CA-mutant breast cancer,” involved the development of computational models that simulate the behavior of PI3K and its downstream targets. PI3K pathway mutations are observed in approximately 30% of breast cancer patients, contributing to resistance against primary anti-cancer treatments.

Using this mathematical approach, the researchers were able to predict novel combination therapies that were subsequently confirmed to be more effective in combating PI3K-mutant breast cancer cells than single-target approaches. The findings highlight the potential of computational models as powerful tools in predicting and prioritizing combination therapies for cancer treatment.

Co-senior author Associate Professor Lan Nguyen emphasized the significance of the study, stating, “Our research not only sheds light on the complex mechanisms underlying therapeutic resistance but also provides a computational approach for systematically prioritizing combination therapies in an unbiased manner. This could significantly accelerate the discovery of effective treatments and serve as a valuable framework for future oncology research.”

The study also identified additional proteins that, when inhibited, restored sensitivity to a PI3K inhibitor called alpelisib and halted the proliferation of resistant cells. This represents a promising strategy for overcoming drug resistance and improving the effectiveness of targeted therapies.

Dr. Antonella Papa, co-senior author of the study, emphasized the potential impact of these findings on breast cancer treatment. “As drug resistance is a common cause of treatment failure, our research may lead to the testing and approval of new therapies that maintain their effectiveness for longer periods, ultimately improving survival rates and quality of life for patients,” she stated.

Looking ahead, the researchers plan to conduct further preclinical evaluations of the identified drug combinations. Successful preclinical studies will pave the way for initiating clinical trials, which will be vital in confirming the safety and efficacy of these novel combination therapies in humans.

Dr. Papa noted the progress made in preclinical validation, stating, “Previous studies have demonstrated that similar treatments effectively reduce tumor growth using mouse models. If preclinical validation progresses further, initial clinical trials could commence within a few years.” Close collaboration between researchers, clinicians, and regulatory bodies will be crucial in expediting this process.

The study’s authors acknowledged the collaborative nature of the research and highlighted the importance of interdisciplinary approaches in addressing complex medical challenges such as cancer. By leveraging mathematical modeling and interdisciplinary collaboration, this study brings hope for breast cancer patients who face resistance to existing treatments and paves the way for the development of more effective and personalized therapies.

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