In vivo phosphoproteomics reveals kinase activity profiles that predict treatment outcome in triple-negative breast cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Ivana Zagorac, Sara Fernandez-Gaitero, Renske Penning, Harm Post, Maria J. Bueno, Silvana Mouron, Luis Manso, Manuel M. Morente, Soledad Alonso, Violeta Serra, Javier Muñoz, Gonzalo Gómez-López, Jose Francisco Lopez-Acosta, Veronica Jimenez-Renard, Albert Gris-Oliver, Fatima Al-Shahrour, Elena Piñeiro-Yañez, Jose Luis Montoya-Suarez, Juan V. Apala, Amalia Moreno-Torres, Ramon Colomer, Ana Dopazo, Albert J. R. Heck, Maarten Altelaar, Miguel Quintela-Fandino

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. More... »

PAGES

3501

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41467-018-05742-z

    DOI

    http://dx.doi.org/10.1038/s41467-018-05742-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1106343632

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30158526


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