RT Journal Article
SR Electronic
T1 Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data-independent acquisition proteomics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.09.21.306654
DO 10.1101/2020.09.21.306654
A1 Silvia García-Adrián
A1 Lucía Trilla-Fuertes
A1 Angelo Gámez-Pozo
A1 Cristina Chiva
A1 Rocío López-Vacas
A1 Elena López-Camacho
A1 Guillermo Prado-Vázquez
A1 Andrea Zapater-Moros
A1 María I. Lumbreras-Herrera
A1 David Hardisson
A1 Laura Yébenes
A1 Pilar Zamora
A1 Eduard Sabidó
A1 Juan Ángel Fresno Vara
A1 Enrique Espinosa
YR 2020
UL http://biorxiv.org/content/early/2020/09/24/2020.09.21.306654.abstract
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