Individualized Survival and Treatment Response Predictions in Breast Cancer Patients: Involvements of Phospho-EGFR and Phospho-Her2/neu Proteins.

ABSTRACT: Our robust prediction system for individual breast cancer patients combines three well-known machine-learning classifiers to provide stable and accurate clinical outcome prediction (N=269). The average performance of the selected classifiers is used as the evaluation criterion in breast cancer outcome predictions. A profile (incorporating histology, lymph node status, tumor grade, tumor stage, ER, PR, Her2/neu, patient's age and smoking status) generated over 95% accuracy in individualized disease-free survival and treatment response predictions. Furthermore, our analysis demonstrated that the measurement of phospho-EGFR and phospho-Her2/neu is more powerful in breast cancer survival prediction than that of total EGFR and total Her2/neu (p < 0.05). The incorporation of hormon

SUBMITTER: Guo L 

PROVIDER: S-EPMC4282131 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature