| 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. 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 ( Figure 1 ), as well as molecular markers collected using standard immunohistochemistry and fluorescence in situ hybridization ( Figure 3 ). The model significantly outperforms the St. Gallen Consensus guidelines and the Nottingham Prognostic Index ( Figure 11 ), thus providing a clinically useful and cost-effective prognostic for breast cancer patients. We describe a method of predicting response to endocrine therapy or predicting disease progression in breast cancer characterized in that a breast cancer test sample is obtained from a subject; clinicopathological data is obtained from said breast test sample; the obtained breast cancer test sample is analyzed for presence or amount of (1) one or more molecular markers of hormone receptor status, one or more growth factor receptor maskers, and one or more tumor suppression/apoptosis molecular markers; (2) one or more additional molecular markers both proteomic and non-proteomic that are indicative of breats cancer disease processes from a group consisting essentially of angiogenesis, apoptosis, catenin/cadherin proliferation/differentiation, cell cycle processes, cell surface processes, cell-cell interaction, cell migration, centrosomal processes, cellular adhesion, cellular proliferation, cellular metastasis, invasion, cytoskeletal processes, ERBB2 interactions, estrogen co-receptors, growth factors and receptors, membrane/integrin/signal transduction, metastasis, oncogenes, proliferation, proliferation oncogenes, signal transduction, surface antigens and transcription factor molecular markers; and then (1) a presence or amount of said molecular markers is correlated with (2) clinicopathological data from said tissue sample other than the molecular markers of breast cancer disease processes, in order to deduce a probability of response to endocrine therapy of future risk of disease progression in breast cancer for the subject. |
