title: Individualized survival and treatment response predictions for breast cancers using phospho-EGFR, phospho-ER, phospho-HER2/neu, phospho-IGF-IR/In, phospho-MAPK, and phospho-p70S6K proteins.
abstract: The development and progression of breast cancer involves the activation of numerous protein kinases, and the change in phosphorylation is a hallmark of protein kinase activation. In this study, we identified a comprehensive profile to predict individual breast cancer patients' survival and treatment responses using the Random Committee algorithm. The profile incorporated a subset of phosphorylated signal protein expressions and several selected clinical factors of breast cancer. The parameters of our profile were identified by supervised feature selection algorithms, Gain Ratio Attribute Evaluation and Relief. The results showed that the overall accuracy of survival prediction reached 92.3% for individual breast cancer patients with the use of the expression profiles of phospho-EGFR, phospho-ER, phospho-HER2/neu, phospho-IGFIR/In, phospho-MAPK, and phospho-p70S6K plus the selected clinical factors. The results also indicated that the overall accuracy of treatment response prediction was 92.6% with the use of the level of phospho-EGFR, phospho-ER, phospho-HER2/neu, phospho-MAPK, and phospho-p70S6K plus the selected clinical information. The prediction system combines multiple signal protein activation profiles and relevant clinical information, and provides a unique guideline to aid individualized decision-making in the clinical management of breast cancer.
authors: MBR Cancer Center/Department of Community Medicine, West Virginia University, Morgantown, West Virginia 26505, USA. lguo@hsc.wvu.edu
authors: L
authors: L
authors: Guo
authors: J
authors: J
authors: Abraham
authors: D C
authors: DC
authors: Flynn
authors: V
authors: V
authors: Castranova
authors: X
authors: X
authors: Shi
authors: Y
authors: Y
authors: Qian
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000912:Antibodies, Neoplasm / Q000032:analysis; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000276:immunology / Q000473:pathology / Q000628:therapy*; D015331:Cohort Studies; D019295:Computational Biology; D018572:Disease-Free Survival; D066246:ErbB Receptors / Q000032:analysis*; D005260:Female; D006801:Humans; D008875:Middle Aged; D020928:Mitogen-Activated Protein Kinases / Q000032:analysis*; D010750:Phosphoproteins / Q000032:analysis*; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000032:analysis*; D011960:Receptors, Estrogen / Q000032:analysis*; D038762:Ribosomal Protein S6 Kinases, 70-kDa / Q000032:analysis*; D016896:Treatment Outcome
chemical_list: D000912:Antibodies, Neoplasm; D014408:Biomarkers, Tumor; D066246:ErbB Receptors; D020928:Mitogen-Activated Protein Kinases; D010750:Phosphoproteins; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D038762:Ribosomal Protein S6 Kinases, 70-kDa