Abstract
BackgroundIncreased concentrations of choline-containing compounds are frequently observed in breast carcinomas, and may serve as biomarkers for both diagnostic and treatment monitoring purposes. However, underlying mechanisms for the abnormal choline metabolism are poorly understood.MethodsThe concentrations of choline-derived metabolites were determined in xenografted primary human breast carcinomas, representing basal-like and luminal-like subtypes. Quantification of metabolites in fresh frozen tissue was performed using high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS).The expression of genes involved in phosphatidylcholine (PtdCho) metabolism was retrieved from whole genome expression microarray analyses.The metabolite profiles from xenografts were compared with profiles from human breast cancer, sampled from patients with estrogen/progesterone receptor positive (ER+/PgR+) or triple negative (ER-/PgR-/HER2-) breast cancer.ResultsIn basal-like xenografts, glycerophosphocholine (GPC) concentrations were higher than phosphocholine (PCho) concentrations, whereas this pattern was reversed in luminal-like xenografts. These differences may be explained by lower choline kinase (CHKA, CHKB) expression as well as higher PtdCho degradation mediated by higher expression of phospholipase A2 group 4A (PLA2G4A) and phospholipase B1 (PLB1) in the basal-like model. The glycine concentration was higher in the basal-like model. Although glycine could be derived from energy metabolism pathways, the gene expression data suggested a metabolic shift from PtdCho synthesis to glycine formation in basal-like xenografts. In agreement with results from the xenograft models, tissue samples from triple negative breast carcinomas had higher GPC/PCho ratio than samples from ER+/PgR+ carcinomas, suggesting that the choline metabolism in the experimental models is representative for luminal-like and basal-like human breast cancer.ConclusionsThe differences in choline metabolite concentrations corresponded well with differences in gene expression, demonstrating distinct metabolic profiles in the xenograft models representing basal-like and luminal-like breast cancer. The same characteristics of choline metabolite profiles were also observed in patient material from ER+/PgR+ and triple-negative breast cancer, suggesting that the xenografts are relevant model systems for studies of choline metabolism in luminal-like and basal-like breast cancer.
Highlights
Increased concentrations of choline-containing compounds are frequently observed in breast carcinomas, and may serve as biomarkers for both diagnostic and treatment monitoring purposes
Animal model The MAS98.12 and MAS98.06 tumor models were established by orthotopic implantation of biopsy tissues from primary mammary carcinomas in SCID mice as previously described [29]. Both the primary carcinomas and the xenograft models have been characterized using gene expression profiling. These analyses demonstrated that the primary carcinomas could be classified as luminal-like and basal-like subtypes of breast cancer, and that these molecular subtypes were retained in the MAS98.06 and MAS98.12 xenografts
The High resolution magic angle spinning (HR MAS) Magnetic resonance spectroscopy (MRS) data was considered to be representative of the metabolite concentrations in the solid tumors
Summary
Increased concentrations of choline-containing compounds are frequently observed in breast carcinomas, and may serve as biomarkers for both diagnostic and treatment monitoring purposes. Optimal treatment of individual breast cancer patients is still a major challenge in oncology. An approach to improve and individualize the treatment beyond the markers and stratification tools used at present, is through molecular subtyping of breast cancer [1]. Based on variation in gene expression profiles, five molecular subtypes have been identified [1,2,3]. The gene expression patterns of these subtypes are similar across multiple samples from the same tumor, shows no treatmentrelated changes and have been reproduced in a number of patient populations [2,3,4,5,6,7]. Further understanding of the differences in biology between the various subtypes is needed in order to predict therapeutic response and provide individual treatment strategies based on gene expression profiles [8]
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