Dataset Information

Interplay of choline metabolites and genes in patient-derived breast cancer xenografts

ABSTRACT: Dysregulated choline metabolism is a well-known feature of breast cancer, but the underlying mechanisms are not fully understood. In this study, the metabolomic and transcriptomic characteristics of a large panel of human breast cancer xenograft models were mapped, with focus on choline metabolism. Methods: Tumor specimens from 34 patient-derived xenograft models were collected and divided in two. One part was examined using high-resolution magic angle spinning (HR-MAS) MR spectroscopy while another part was analysed using gene expression microarrays. Expression data of genes encoding proteins in the choline metabolism pathway were analysed and correlated to the levels of choline (Cho), phosphocholine (PCho) and glycerophosphocholine (GPC) using Pearson’s correlation analysis. For comparison purposes, metabolic and gene expression data were collected from human breast tumors belonging to corresponding molecular subgroups. Results: Most of the xenograft models were classified as basal-like (N=19) or luminal B (N=7). These two subgroups showed significantly different choline metabolic and gene expression profiles. The luminal B xenografts were characterized by a high PCho/GPC ratio while the basal-like xenografts were characterized by highly variable PCho/GPC ratio. Also, Cho, PCho and GPC levels were correlated to expression of several genes encoding proteins in the choline metabolism pathway, including choline kinase alpha (CHKA) and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5). These characteristics were similar to those found in human tumor samples. Discussion: The higher PCho/GPC ratio found in luminal B compared with most basal-like breast cancer xenograft models and human tissue samples do not correspond to results observed from in vitro studies. It is likely that microenvironmental factors play a role in the in vivo regulation of choline metabolism. Cho, PCho and GPC were correlated to different choline pathway-encoding genes in luminal B compared with basal-like xenografts, suggesting that regulation of choline metabolism may vary between different breast cancer subgroups. The concordance between the metabolic and gene expression profiles from xenograft models with breast cancer tissue samples from patients indicates that these xenografts are representative models of human breast cancer and represent relevant models to study tumor metabolism in vivo.

ORGANISM(S): Homo sapiens

PROVIDER: GSE44666 | GEO | 2014/01/16

SECONDARY ACCESSION(S): PRJNA190871

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Dysregulated choline metabolism is a well-known feature of breast cancer, but the underlying mechanisms are not fully understood. In this study, the metabolomic and transcriptomic characteristics of a large panel of human breast cancer xenograft models were mapped, with focus on choline metabolism. Methods: Tumor specimens from 34 patient-derived xenograft models were collected and divided in two. One part was examined using high-resolution magic angle spinning (HR-MAS) MR spectroscopy while another part was analysed using gene expression microarrays. Expression data of genes encoding proteins in the choline metabolism pathway were analysed and correlated to the levels of choline (Cho), phosphocholine (PCho) and glycerophosphocholine (GPC) using Pearson’s correlation analysis. For comparison purposes, metabolic and gene expression data were collected from human breast tumors belonging to corresponding molecular subgroups. Results: Most of the xenograft models were classified as basal-like (N=19) or luminal B (N=7). These two subgroups showed significantly different choline metabolic and gene expression profiles. The luminal B xenografts were characterized by a high PCho/GPC ratio while the basal-like xenografts were characterized by highly variable PCho/GPC ratio. Also, Cho, PCho and GPC levels were correlated to expression of several genes encoding proteins in the choline metabolism pathway, including choline kinase alpha (CHKA) and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5). These characteristics were similar to those found in human tumor samples. Discussion: The higher PCho/GPC ratio found in luminal B compared with most basal-like breast cancer xenograft models and human tissue samples do not correspond to results observed from in vitro studies. It is likely that microenvironmental factors play a role in the in vivo regulation of choline metabolism. Cho, PCho and GPC were correlated to different choline pathway-encoding genes in luminal B compared with basal-like xenografts, suggesting that regulation of choline metabolism may vary between different breast cancer subgroups. The concordance between the metabolic and gene expression profiles from xenograft models with breast cancer tissue samples from patients indicates that these xenografts are representative models of human breast cancer and represent relevant models to study tumor metabolism in vivo. Gene expression was measured in 30 human breast cancer xenografts, one sample from each model

Project description:The VEGF targeted antiangiogenic drug bevacizumab has shown varying results in clinical trials of breast cancer. Identifying robust biomarkers for selecting patients that may benefit from bevacizumab treatment and for monitoring of response is important for the future use of this drug. 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. Mice given no treatment or treated with bevacizumab, doxorubicin or the combination of these two drugs were sacrificed at day 3 or 10. High resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) and gene expression microarray analysis was performed on all tumor samples. Combination treatment with bevacizumab had the strongest growth inhibiting effect in the basal-like tumors, and this was reflected in a significant response 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 changes in gene expression were observed for the most efficient treatment in each of the two xenograft models. The metabolite glycerophosphocholine (GPC) showed opposite response in the treated xenografts compared with untreated controls: lower in basal-like tumors and higher in luminal-like tumors. Lower levels of creatine, taurine and glycine were observed in the basal-like xenografts given bevacizumab as monotherapy compared with untreated xenografts. Comparing combination therapy with doxorubicin monotherapy in basal-like xenografts, 14 genes showed significant differential expression, including higher expression of very low density lipoprotein receptor (VLDLR), and lower expression of hemoglobin, theta 1 (HBQ1). Using published gene expression signatures, bevacizumab treated tumors were associated with a more hypoxic phenotype, while no evidence was found for associations between bevacizumab treatment and vascular invasion or increasing tumor grade. This study underlines the importance of characterizing biological differences between subtypes of breast cancer to identify personalized biomarkers for selecting patients for bevacizumab treatment and evaluating response to therapy. 60 samples are analyzed. For each of the two xenograft models, tumors were collected from animals that were untreated or treated with bevacizumab (5 mg/kg), doxorubicin (8 mg/kg) or a combination of the two therapies (n = 6 tumors for each group). Bevacizumab treatment was repeated at day 4 and day 7. Animals were sacrificed and tissue harvested at either day 3 or 10 after treatment, in triplicates within each treatment group. This resulted in 24 tumor samples from each of the models. In addition, untreated and bevacizumab treated luminal-like xenografts not fed with estradiol were included for comparison (n = 12). Note that the untreated controls are overlapping with the samples in the GEO series with accession number: GSE25915.

Dataset Information

Interplay of choline metabolites and genes in patient-derived breast cancer xenografts

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets