Machine learning based anoikis signature predicts personalized treatment strategy of breast cancer

Background: Breast cancer remains a leading cause of mortality among women worldwide, underscoring the critical need for innovative prognostic tools to enhance treatment strategies. Anoikis, a specialized form of programmed cell death that prevents metastasis, is a key process in breast cancer progression.
Methods: This study presents the Artificial Intelligence-Derived Anoikis Signature (AIDAS), a novel machine learning-based prognostic model designed to identify anoikis-related gene patterns in breast cancer. AIDAS was developed using multi-cohort transcriptomic datasets and validated through immunohistochemical analysis of clinical samples, ensuring robustness and wide applicability.
Results: AIDAS demonstrated superior performance ARS-853 compared to existing prognostic models in predicting breast cancer outcomes with high accuracy. Patients with low AIDAS scores exhibited enhanced responsiveness to immunotherapies, including PD-1/PD-L1 inhibitors, whereas those with high AIDAS scores showed greater sensitivity to chemotherapeutic agents like methotrexate.
Conclusions: These findings underscore the pivotal role of anoikis in breast cancer prognosis and highlight the potential of AIDAS to inform personalized treatment strategies. By combining machine learning with biological insights, AIDAS offers a transformative approach to personalized oncology. Its nuanced understanding of the anoikis regulatory network provides a foundation for the development of targeted therapies, paving the way for significant improvements in patient outcomes.