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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
Page last reviewed: April 12, 2019
Content source: National Institute for Occupational Safety and Health Education and Information Division