Research Papers:

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

Marta Mendiola, Virginia Martínez-Marin, Jesús Herranz, Victoria Heredia, Laura Yébenes, Pilar Zamora, Beatriz Castelo, Álvaro Pinto, María Miguel, Esther Díaz, Angelo Gámez, Juan Ángel Fresno, Ana Ramírez de Molina, David Hardisson, Enrique Espinosa and Andrés Redondo _

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Oncotarget. 2016; 7:24217-24227. https://doi.org/10.18632/oncotarget.8128

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Marta Mendiola1, Virginia Martínez-Marin2,3, Jesús Herranz4, Victoria Heredia1, Laura Yébenes5, Pilar Zamora2,3, Beatriz Castelo2,3, Álvaro Pinto2,3, María Miguel1, Esther Díaz3, Angelo Gámez3, Juan Ángel Fresno3, Ana Ramírez de Molina4, David Hardisson1,5, Enrique Espinosa2,3, Andrés Redondo2,3

1Molecular Pathology and Therapeutic Targets Group, La Paz University Hospital – IdiPAZ, Madrid, Spain

2Department of Medical Oncology, La Paz University Hospital, Madrid, Spain

3Translational Oncology Group, La Paz University Hospital – IdiPAZ, Madrid, Spain

4IMDEA, Campus de Excelencia Internacional CEI (UAM-CSIC), Madrid, Spain

5Department of Pathology, La Paz University Hospital, Madrid, Spain

Correspondence to:

Andrés Redondo, e-mail: aredondos@uam.es

Keywords: metastatic breast carcinoma, bevacizumab and weekly paclitaxel, predictive, angiogenesis, gene expression

Received: November 29, 2015     Accepted: February 25, 2016     Published: March 16, 2016


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 GC-model, 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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