Genetic variability of VEGF pathway genes in six randomized phase III trials assessing the addition of bevacizumab to standard therapy View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-10

AUTHORS

Sanne de Haas, Paul Delmar, Aruna T. Bansal, Matthieu Moisse, David W. Miles, Natasha Leighl, Bernard Escudier, Eric Van Cutsem, Peter Carmeliet, Stefan J. Scherer, Celine Pallaud, Diether Lambrechts

ABSTRACT

BACKGROUND: Despite extensive translational research, no validated biomarkers predictive of bevacizumab treatment outcome have been identified. METHODS: We performed a meta-analysis of individual patient data from six randomized phase III trials in colorectal, pancreatic, lung, renal, breast, and gastric cancer to explore the potential relationships between 195 common genetic variants in the vascular endothelial growth factor (VEGF) pathway and bevacizumab treatment outcome. RESULTS: The analysis included 1,402 patients (716 bevacizumab-treated and 686 placebo-treated). Twenty variants were associated (P < 0.05) with progression-free survival (PFS) in bevacizumab-treated patients. Of these, 4 variants in EPAS1 survived correction for multiple testing (q < 0.05). Genotype-by-treatment interaction tests revealed that, across these 20 variants, 3 variants in VEGF-C (rs12510099), EPAS1 (rs4953344), and IL8RA (rs2234671) were potentially predictive (P < 0.05), but not resistant to multiple testing (q > 0.05). A weak genotype-by-treatment interaction effect was also observed for rs699946 in VEGF-A, whereas Bayesian genewise analysis revealed that genetic variability in VHL was associated with PFS in the bevacizumab arm (q < 0.05). Variants in VEGF-A, EPAS1, and VHL were located in expression quantitative loci derived from lymphoblastoid cell lines, indicating that they affect the expression levels of their respective gene. CONCLUSIONS: This large genetic analysis suggests that variants in VEGF-A, EPAS1, IL8RA, VHL, and VEGF-C have potential value in predicting bevacizumab treatment outcome across tumor types. Although these associations did not survive correction for multiple testing in a genotype-by-interaction analysis, they are among the strongest predictive effects reported to date for genetic variants and bevacizumab efficacy. More... »

PAGES

909-920

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10456-014-9438-1

DOI

http://dx.doi.org/10.1007/s10456-014-9438-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020797759

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25012543


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