English
norskSubtype-specific response to bevacizumab is reflected in the metabolome and transcriptome of breast cancer xenografts
Permanent link
https://hdl.handle.net/10037/13411
Date
2012-10-23Type
Journal articleTidsskriftartikkel
Peer reviewed
Abstract
Antiangiogenic therapy with bevacizumab has shown varying results in breast cancer clinical trials. Identifying robust biomarkers for selecting patients who may benefit from such treatment and for monitoring response is important for the future use of bevacizumab.
Two established xenograft models representing basal‐like and luminal‐like breast cancer were used to study bevacizumab treatment response on the metabolic and gene expression levels. Tumor samples were obtained from mice treated with bevacizumab, doxorubicin or a combination of the two drugs, and high resolution magic angle spinning magnetic resonance spectroscopy and gene expression microarray analysis was performed.
Combination treatment with bevacizumab showed the strongest growth inhibiting effect in basal‐like tumors, and this was reflected by a significant change in the metabolomic and transcriptomic profiles. In the luminal‐like xenografts, addition of bevacizumab did not improve the effect of doxorubicin. On the global transcriptomic level, the largest gene expression changes were observed for the most efficient treatment in both models. Glycerophosphocholine showed opposite response in the treated xenografts compared with untreated controls; lower in basal‐like and higher in luminal‐like tumors. Comparing combination therapy with doxorubicin monotherapy in basal‐like xenografts, 14 genes showed significant differential expression, including very low density lipoprotein receptor (VLDLR) and hemoglobin, theta 1 (HBQ1). Bevacizumab‐treated tumors were associated with a more hypoxic phenotype, while no evidence was found for associations between bevacizumab treatment and vascular invasion or tumor grade.
This study underlines the importance of characterizing biological differences between subtypes of breast cancer to identify personalized biomarkers for improved patient stratification and evaluation of response to therapy.