Abstract P3-08-42: Disease-free survival prognostic signature in triple-negative breast cancer based on high-throughput proteomics data

Introduction Triple negative breast cancer (TNBC) accounts for 15-20% of the breast cancer and is characterized by an aggressive phenotype and worst prognosis. TNBC does not benefit from any targeted therapy, so further characterization would be needed to define subgroups with potential therapeutic value. Material and methods 125 TNBC paraffin samples were analyzed using high-throughput proteomics based on SWATH-MS. Survival analyses and a prognostic predictor were done using BRB Array Tools. Proteins related with disease-free survival were established and, then, a prognostic signature was built based on their p-values. Results and discussion Using SWATH-MS, 1,206 proteins were identified in a cohort of 125 TNBC tumors. Of these 1,206 proteins, 29 proteins were related with disease-free survival. In addition, a prognostic signature based on the expression of two proteins, RMB3 and NIPSNAP1, was defined. The predictor split our population into a low-risk and a high-risk group (p=0.0002, HR= 6.519). Multivariate analysis showed that the prognostic signature based on the expression of these two proteins supplied significant information to the clinical parameters. Conclusion SWATH-MS proteomics demonstrates its utility in the analysis of TNBC paraffin samples. Moreover, this proteomics data allows us to build a prognostic signature based on the expression of two proteins (RBM3 and NIPSNAP1). This prognostic signature could be used in the future to identify a population with a high-risk of relapse that may be directed to a clinical trial. Citation Format: Pilar Zamora Aunon, Silvia Garcia Adrian, Lucia Trilla-Fuertes, Angelo Gamez-Pozo, Guillermo Prado-Vazquez, Andrea Zapater-Moros, Mariana Diaz- Almiron, Rocio Lopez Vacas, Cristina Chiva, Cristina Chiva, Eduard Sabido, Enrique Espinosa Arranz, Juan Angel Fresno Vara. Disease-free survival prognostic signature in triple-negative breast cancer based on high-throughput proteomics data [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-42.