Predictive value of angiogenesis-related gene profiling in patients with HER2-negative metastatic breast cancer treated with bevacizumab and weekly paclitaxel
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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
Andrés Redondo, e-mail: firstname.lastname@example.org
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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