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Predictive value of angiogenesis-related gene profiling in patients with HER2-negative metastatic breast cancer treated with bevacizumab and weekly paclitaxel

Author
Mendiola, Marta; Martínez-Marín, Virginia; Herranz Barrera, Jesúsuntranslated; Heredia, Victoria; Yébenes, Laura; Zamora, Pilar; Castelo Fernández, Beatrizuntranslated; Pinto, Álvaro; Miguel, María; Díaz, Esther; Gámez, Angelo; Fresno, Juan Ángel; Ramírez de Molina, Ana; Hardisson Hernáez, David Alonsountranslated; Espinosa Arranz, Enriqueuntranslated; Redondo Sánchez, Andrésuntranslated
Entity
UAM. Departamento de Anatomía Patológica; UAM. Departamento de Medicina; Instituto de Investigación Sanitaria Hospital Universitario de La Paz (IdiPAZ)
Publisher
Impact Journals LLC
Date
2016-03-16
Citation
Oncotarget 7.17 (2016): 24217-24227
 
 
 
ISSN
1949-2553
Funded by
This work was supported by a grant from the Independent Clinical Research Program (EC10-342), Independent Clinical Research Program (EC10-342), ISCIII (Instituto de Salud Carlos III), Spanish Ministry of Health, and funded also from Roche Farma S.A.U.
Subjects
Metastatic breast carcinoma; Bevacizumab and weekly paclitaxel; Predictive; Angiogenesis; Gene expression; Medicina
URI
http://hdl.handle.net/10486/677937

Abstract

Bevacizumab plus weekly paclitaxel improves progression-free survival (PFS) in HER2-negative metastatic breast cancer (mBC), but its use has been questioned due to the absence of a predictive biomarker, lack of benefit in overall survival (OS) and increased toxicity. We examined the baseline tumor angiogenic-related gene expression of 60 patients with mBC with the aim of finding a signature that predicts benefit from this drug. Multivariate analysis by Lasso-penalized Cox regression generated two predictive models: one, named G-model, including 11 genes, and the other one, named GCmodel, including 13 genes plus 5 clinical covariates. Both models identified patients with improved PFS (HR (Hazard Ratio) 2.57 and 4.04, respectively) and OS (HR 3.29 and 3.43, respectively). The G-model distinguished low and high risk patients in the first 6 months, whereas the GC-model maintained significance over time
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Google™ Scholar:Mendiola, Marta - Martínez-Marín, Virginia - Herranz Barrera, Jesús - Heredia, Victoria - Yébenes, Laura - Zamora, Pilar - Castelo Fernández, Beatriz - Pinto, Álvaro - Miguel, María - Díaz, Esther - Gámez, Angelo - Fresno, Juan Ángel - Ramírez de Molina, Ana - Hardisson Hernáez, David Alonso - Espinosa Arranz, Enrique - Redondo Sánchez, Andrés

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  • Producción científica en acceso abierto de la UAM [17777]

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