Individualized survival and treatment response predictions in breast cancer patients: involvements of phospho-EGFR and phospho-Her2/neu proteins.
Guo L; Abraham J; Flynn DC; Castranova V; Shi X; Qian Y
Open Clin Cancer J 2008 Jan; 2:18-31
Our robust prediction system for individual breast cancer patients combines three well-known machinelearning 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 hormone receptor status, Her2/neu, patient's age and smoking status into the traditional pathologic markers creates a powerful standard to perform individualized survival and treatment outcome predictions for breast cancer patients.
Cancer-rates; Breast-cancer; Statistical-analysis; Mathematical-models; Treatment; Clinical-diagnosis; Clinical-tests; Clinical-techniques
Yong Qian, Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia 26505
The Open Clinical Cancer Journal