PT  - JOURNAL ARTICLE
AU  - Silvia García-Adrián
AU  - Lucía Trilla-Fuertes
AU  - Angelo Gámez-Pozo
AU  - Cristina Chiva
AU  - Rocío López-Vacas
AU  - Elena López-Camacho
AU  - Guillermo Prado-Vázquez
AU  - Andrea Zapater-Moros
AU  - María I. Lumbreras-Herrera
AU  - David Hardisson
AU  - Laura Yébenes
AU  - Pilar Zamora
AU  - Eduard Sabidó
AU  - Juan Ángel Fresno Vara
AU  - Enrique Espinosa
TI  - Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data-independent acquisition proteomics
AID  - 10.1101/2020.09.21.306654
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.09.21.306654
4099  - http://biorxiv.org/content/early/2020/09/24/2020.09.21.306654.short
4100  - http://biorxiv.org/content/early/2020/09/24/2020.09.21.306654.full
AB  - Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.AGCauto-gain controlBICBayesian information criterionCNScentral nervous systemDDAdata-dependent acquisitionDFSdisease-free survivalDIAdata-independent acquisitionFDRFalse discovery rateFFPEformalin-fixed paraffin-embeddedHCDhigher-energy collisional dissociationHCLhierarchical clusterPGMProbabilistic graphical modelSAMSignificance analysis of microarraysTNBCtriple negative breast cancer