The Open Clinical Cancer Journal
2008, 2 : 18-31Published online 2008 May 27. DOI: 10.2174/1874189400802010018
Publisher ID: TOCCJ-2-18
RESEARCH ARTICLE
Individualized Survival and Treatment Response Predictions in Breast Cancer Patients: Involvements of Phospho-EGFR and Phospho-Her2/Neu Proteins
2 Department of Medicine and Division of Hematology/Oncology, West Virginia University, WV 26506-9300, USA
3 MBR Cancer Center/Department of Microbiology, Immunology, and Cell Biology, School of Medicine, West Virginia University, Morgantown, WV 26506-9300, USA
4 The Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505, USA
* Address correspondence to these authors at the MBR Cancer Cen- ter/Department of Community Medicine, School of Medicine, West Virginia University, Morgantown, WV 26506-9300, USA; Fax: (304) 293-4667; Tel: (304) 293-6455; E-mail: lguo@hsc.wvu.edu
ABSTRACT
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