Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics

Gámez Pozo, Angelo; Trilla Fuertes, Lucía; Prado Vázquez, Guillermo; Chiva, Cristina; López Vacas, Rocío; Nanni, Paolo; Berges Soria, Julia; Grossmann, Jonas; Díaz Almirón, Mariana; Ciruelos, Eva; Sabidó Aguadé, Eduard, 1981-; Espinosa Arranz, Enrique; Fresno Vara, Juan Ángel

doi:http://dx.doi.org/10.1371/journal.pone.0178296

Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics

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Gámez-Pozo A, Trilla-Fuertes L, Prado-Vázquez G, Chiva C, López-Vacas R, Nanni P et al. Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics. PLoS One. 2017 Jun 8;12(6):e0178296. DOI: 10.1371/journal.pone.0178296

Abstract

BACKGROUND: Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomics-based biomarkers that predict the response to standard adjuvant chemotherapy, so that patients at are not going to benefit from it can be offered therapeutic alternatives. METHODS: We analyzed the proteome of a retrospective series of formalin-fixed, paraffin-embedded TNBC tissue applying high-throughput label-free quantitative proteomics. We identified several protein signatures with predictive value, which were validated with quantitative targeted proteomics in an independent cohort of patients and further evaluated in publicly available transcriptomics data. RESULTS: Using univariate Cox analysis, a panel of 18 proteins was significantly associated with distant metastasis-free survival of patients (p<0.01). A reduced 5-protein profile with prognostic value was identified and its prediction performance was assessed in an independent targeted proteomics experiment and a publicly available transcriptomics dataset. Predictor P5 including peptides from proteins RAC2, RAB6A, BIEA and IPYR was the best performance protein combination in predicting relapse after adjuvant chemotherapy in TNBC patients. CONCLUSIONS: This study identified a protein combination signature that complements histopathological prognostic factors in TNBC treated with adjuvant chemotherapy. The protein signature can be used in paraffin-embedded samples, and after a prospective validation in independent series, it could be used as predictive clinical test in order to recommend participation in clinical trials or a more exhaustive follow-up.