Please use this identifier to cite or link to this item:http://hdl.handle.net/20.500.12105/6527
Title
In vivo phosphoproteomics reveals kinase activity profiles that predict treatment outcome in triple-negative breast cancer
Author(s)
Zagorac, Ivana CNIO | Fernandez-Gaitero, Sara | Penning, Renske | Post, Harm | Bueno Verdejo, Maria Jose CNIO | Mouron, Silvana Andrea CNIO | Manso, Luis | Morente Gallego, Manuel M CNIO | Alonso, Soledad | Serra, Violeta | Muoz Peralta, Javier CNIO | Gomez Lopez, Gonzalo CNIO | Lopez-Acosta, Jose Francisco | Jimenez-Renard, Veronica | Gris-Oliver, Albert | Al-Shahrour , Fatima CNIO | Piñeiro-Yañez, Elena | Montoya-Suarez, Jose Luis | Apala, Juan V | Moreno-Torres, Amalia | Colomer, Ramon | Dopazo, Ana CNIC | Heck, Albert J R | Altelaar, Maarten | Quintela Fandino, Miguel Angel CNIO
Date issued
2018-08-29
Citation
Nat Commun. 2018; 9(1): 3501.
Language
Inglés
Abstract
Triple-negative breast cancer (TNBC) lacks prognostic and predictive markers. Here, we use high-throughput phosphoproteomics to build a functional TNBC taxonomy. A cluster of 159 phosphosites is upregulated in relapsed cases of a training set (n = 34 patients), with 11 hyperactive kinases accounting for this phosphoprofile. A mass-spectrometry-to-immunohistochemistry translation step, assessing 2 independent validation sets, reveals 6 kinases with preserved independent prognostic value. The kinases split the validation set into two patterns: one without hyperactive kinases being associated with a >90% relapse-free rate, and the other one showing ≥1 hyperactive kinase and being associated with an up to 9.5-fold higher relapse risk. Each kinase pattern encompasses different mutational patterns, simplifying mutation-based taxonomy. Drug regimens designed based on these 6 kinases show promising antitumour activity in TNBC cell lines and patient-derived xenografts. In summary, the present study elucidates phosphosites and kinases implicated in TNBC and suggests a target-based clinical classification system for TNBC.
Subject
SET ENRICHMENT ANALYSIS | C-KIT | PEPTIDE IDENTIFICATION | THERAPEUTIC TARGETS | SOMATIC MUTATIONS | TUMOR XENOGRAFTS | INHIBITION | EXPRESSION | LANDSCAPE
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