Comparative Proteome Analysis Revealing an 11-Protein Signature for Aggressive Triple-Negative Breast Cancer

Ning Qing Liu(Radboud University Medical Center), Christoph Stingl(Cancer Genomics Centre), Maxime P. Look(Radboud University Medical Center), Marcel Smid(Cancer Genomics Centre), René B. H. Braakman(Karolinska Institutet), Tommaso De Marchi(GZA Ziekenhuizen Campus Sint-Augustinus), Anieta M. Sieuwerts(Karolinska Institutet), Paul N. Span(Radboud University Medical Center), Fred C.G.J. Sweep(Netherlands Metabolomics Centre), Barbro Linderholm(Netherlands Metabolomics Centre), Anita Mangia(Radboud University Medical Center), Angelo Paradiso(Radboud University Nijmegen), Luc Dirix(Sahlgrenska University Hospital), Steven Van Laere(Karolinska Institutet), Theo M. Luider(Netherlands Metabolomics Centre), John W.M. Martens(Netherlands Metabolomics Centre), John A. Foekens(GZA Ziekenhuizen Campus Sint-Augustinus), Arzu Umar(Sahlgrenska University Hospital)
JNCI Journal of the National Cancer Institute
January 7, 2014
Cited by 101Open Access
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Abstract

BACKGROUND: Clinical outcome of patients with triple-negative breast cancer (TNBC) is highly variable. This study aims to identify and validate a prognostic protein signature for TNBC patients to reduce unnecessary adjuvant systemic therapy. METHODS: Frozen primary tumors were collected from 126 lymph node-negative and adjuvant therapy-naive TNBC patients. These samples were used for global proteome profiling in two series: an in-house training (n = 63) and a multicenter test (n = 63) set. Patients who remained free of distant metastasis for a minimum of 5 years after surgery were defined as having good prognosis. Cox regression analysis was performed to develop a prognostic signature, which was independently validated. All statistical tests were two-sided. RESULTS: An 11-protein signature was developed in the training set (median follow-up for good-prognosis patients = 117 months) and subsequently validated in the test set (median follow-up for good-prognosis patients = 108 months) showing 89.5% sensitivity (95% confidence interval [CI] = 69.2% to 98.1%), 70.5% specificity (95% CI = 61.7% to 74.2%), 56.7% positive predictive value (95% CI = 43.8% to 62.1%), and 93.9% negative predictive value (95% CI = 82.3% to 98.9%) for poor-prognosis patients. The predicted poor-prognosis patients had higher risk to develop distant metastasis than the predicted good-prognosis patients in univariate (hazard ratio [HR] = 13.15; 95% CI = 3.03 to 57.07; P = .001) and multivariable (HR = 12.45; 95% CI = 2.67 to 58.11; P = .001) analysis. Furthermore, the predicted poor-prognosis group had statistically significantly more breast cancer-specific mortality. Using our signature as guidance, more than 60% of patients would have been exempted from unnecessary adjuvant chemotherapy compared with conventional prognostic guidelines. CONCLUSIONS: We report the first validated proteomic signature to assess the natural course of clinical TNBC.


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