Diagnostic markers of breast cancer treatment and progression and methods of use thereof

摘要

To maximize both the life expectancy and quality of life of patients with operable breast cancer, it is important to predict adjuvant treatment outcome and likelihood of progression before treatment. The instant invention details the usage of a machine-learning based method to develop a cross-validated model to predict the outcome of adjuvant treatment, particularly chemotherapy treatment outcome, and likelihood of progression before treatment. The model includes standard clinicopathological features, as well as molecular markers collected using standard immunohistochemistry and fluorescence in situ hybridization. The model significantly outperformed the St. Gallen Consensus guidelines and the Nottingham Prognostic Index and has the potential to provide a clinically useful and cost-effective prognostic for breast cancer patients.