Brook Preloader

New Study Reveals Potential Breakthrough in Triple-Negative Breast Cancer Prognosis

New Study Reveals Potential Breakthrough in Triple-Negative Breast Cancer Prognosis

A recent study has investigated the prognostic and predictive potential of ARIADNE, a transcriptomic test for triple-negative breast cancer (TNBC) developed by researchers at the Center for Complexity and Biosystems of the University of Milan and commercialized by the spinoff Complexdata. The study was conducted by Caterina La Porta, from the Department of Environmental and Policy of the University of Milan and by Stefano Zapperi from the Department of Physics “Aldo Pontremoli” of the same University. Their findings have been published in the International Journal of Molecular Sciences and have the potential to change the way TNBC is diagnosed and treated in the future.

TNBC is a highly invasive and heterogeneous subtype of breast cancer that often has a high recurrence rate and poor outcome. The study found that ARIADNE was more effective than other common pathological indicators, such as grade, stage, and nodal status, in stratifying TNBC patients into groups with different disease-free survival statistics. Additionally, the study found that the classification provided by ARIADNE led to statistically significant differences in the rates of pathological complete response within the groups.

These findings offer hope for improved treatment and outcomes for TNBC patients. The development of prognostic and predictive markers like ARIADNE could lead to more targeted and effective treatment plans, ultimately improving the quality of life for those with TNBC.

“Triple-negative breast cancer is a challenging disease to treat, and we need better tools to predict patient outcomes and response to treatment. Our study suggests that ARIADNE may be a useful tool for clinicians in stratifying TNBC patients and selecting the most appropriate treatment plan for each individual” explains Caterina La Porta who coordinated the study. “The development of ARIADNE involved a unique combination of computational algorithms and expert knowledge in the field of breast cancer. We believe that our approach could be extended to other cancer subtypes and ultimately lead to the development of more accurate and personalized cancer diagnostics” – concludes Stefano Zapperi

Read the paper at