Abstract: Predicting response to adjuvant therapy or predicting disease progression in breast cancer is realized by (1) first obtaining a breast cancer test sample from a subject; (2) second obtaining clinicopathological data from said breast cancer test sample; (3) analyzing the obtained breast cancer test sample for presence or amount of (a) one or more molecular markers of hormone receptor status, one or more growth factor receptor markers, (b) one or more tumor suppression/apoptosis molecular markers; and (c) one or more additional molecular markers both proteomic and non-proteomic that are indicative of breast cancer disease processes; and then (4) correlating (a) the presence or amount of said molecular markers and, with (b) clinicopathological data from said tissue sample other than the molecular markers of breast cancer disease processes.

Abstract: 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. A machine-learning based method is used to develop a cross-validated model to predict (1) the outcome of adjuvant treatment, particularly endocrine treatment outcome, and (2) likelihood of cancer 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 outperforms the St. Gallen Consensus guidelines and the Nottingham Prognostic Index, thus providing a clinically useful and cost-effective prognostic for breast cancer patients.