230 172

Cited 25 times in

Functional proteomics characterization of residual triple-negative breast cancer after standard neoadjuvant chemotherapy

 J. Sohn  ;  K. A. Do  ;  S. Liu  ;  H. Chen  ;  G. B. Mills  ;  G. N. Hortobagyi  ;  F. Meric-Bernstam  ;  A. M. Gonzalez-Angulo 
 ANNALS OF ONCOLOGY, Vol.24(10) : 2522-2526, 2013 
Journal Title
Issue Date
Adult ; Aged ; Anthracyclines/therapeutic use ; Antineoplastic Agents/therapeutic use ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Biomarkers, Tumor/metabolism* ; Bridged-Ring Compounds/therapeutic use ; Disease-Free Survival ; Female ; Humans ; Insulin-Like Growth Factor Binding Protein 2/metabolism ; Middle Aged ; Neoadjuvant Therapy* ; Protein-Serine-Threonine Kinases/metabolism ; Proteomics ; Proto-Oncogene Proteins c-akt/metabolism ; Ribosomal Protein S6/metabolism ; Signal Transduction ; Stathmin/metabolism ; Survival ; Taxoids/therapeutic use ; Triple Negative Breast Neoplasms/mortality* ; Triple Negative Breast Neoplasms/therapy*
molecular characterization ; neoadjuvant chemotherapy ; residual disease ; resistance ; triple receptor-negative breast cancer
BACKGROUND: In this study, we used functional proteomics to determine the molecular characteristics of residual triple receptor-negative breast cancer (TNBC) patients after neoadjuvant systemic chemotherapy (NCT) and their relationship with patient outcomes in order to identify potential targets for therapy. PATIENTS AND METHODS: Protein was extracted from 54 residual TNBCs, and 76 proteins related to breast cancer signaling were measured by reverse phase protein arrays (RPPAs). Univariable and multivariable Cox proportional hazard models were fitted for each protein. Survival outcomes were estimated by the Kaplan-Meier product limit method. Training and cross validation were carried out. The coefficients estimated from the multivariable Cox model were used to calculate a risk score (RS) for each sample. RESULTS: Multivariable analysis using the top 25 proteins from univariable analysis at a false discovery rate (FDR) of 0.3 showed that AKT, IGFBP2, LKB1, S6 and Stathmin were predictors of recurrence-free survival (RFS). The cross-validation model was reproducible. The RS model calculated based on the multivariable analysis was -1.1086 × AKT + 0.2501 × IGFBP2 - 0.6745 × LKB1+1.0692 × S6 + 1.4086 × stathmin with a corresponding area under the curve, AUC = 0.856. The RS was an independent predictor of RFS (HR = 3.28, 95%CI = 2.07-5.20, P < 0.001). CONCLUSIONS: We found a five-protein model that independently predicted RFS risk in patients with residual TNBC disease. The PI3 K pathway may represent potential therapeutic targets in this resistant disease.
Files in This Item:
T201303690.pdf Download
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Sohn, Joo Hyuk(손주혁) ORCID logo https://orcid.org/0000-0002-2303-2764
사서에게 알리기


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.