Identification of metastasis-associated metabolic profiles in tumors using 1H-HR-MAS-MRS

Tumors develop an abnormal microenvironment during growth, and similar to the metastatic phenotype, the metabolic phenotype of cancer cells is tightly linked to characteristics of the tumor microenvironment (TME). In this study, we explored relationships between metabolic profile, metastatic propensity, and hypoxia in experimental tumors in an attempt to identify metastasis-associated metabolic profiles. Two human melanoma xenograft lines (A-07, R-18) showing different TMEs were used as cancer models. Metabolic profile was assessed by proton high resolution magic angle spinning magnetic resonance spectroscopy (1H-HR-MAS-MRS). Tumor hypoxia was detected in immunostained histological preparations by using pimonidazole as a hypoxia marker. Twenty-four samples from 10 A-07 tumors and 28 samples from 10 R-18 tumors were analyzed. Metastasis was associated with hypoxia in both A-07 and R-18 tumors, and 1H-HR-MAS-MRS discriminated between tissue samples with and tissue samples without hypoxic regions in both models, primarily because hypoxia was associated with high lactate resonance peaks in A-07 tumors and with low lactate resonance peaks in R-18 tumors. Similarly, metastatic and non-metastatic R-18 tumors showed significantly different metabolic profiles, but not metastatic and non-metastatic A-07 tumors, probably because some samples from the metastatic A-07 tumors were derived from tumor regions without hypoxic tissue. This study suggests that 1H-HR-MAS-MRS may be a valuable tool for evaluating the role of hypoxia and lactate in tumor metastasis as well as for identification of metastasis-associated metabolic profiles

Identification of Metastasis-Associated Metabolic Profiles of Tumors by 1H-HR-MAS-MRS

Tumors develop an abnormal microenvironment during growth, and similar to the metastatic phenotype, the metabolic phenotype of cancer cells is tightly linked to characteristics of the tumor microenvironment (TME). In this study, we explored relationships between metabolic profile, metastatic propensity, and hypoxia in experimental tumors in an attempt to identify metastasis-associated metabolic profiles. Two human melanoma xenograft lines (A-07, R-18) showing different TMEs were used as cancer models. Metabolic profile was assessed by proton high resolution magic angle spinning magnetic resonance spectroscopy (1H-HR-MAS-MRS). Tumor hypoxia was detected in immunostained histological preparations by using pimonidazole as a hypoxia marker. Twenty-four samples from 10 A-07 tumors and 28 samples from 10 R-18 tumors were analyzed. Metastasis was associated with hypoxia in both A-07 and R-18 tumors, and 1H-HR-MAS-MRS discriminated between tissue samples with and tissue samples without hypoxic regions in both models, primarily because hypoxia was associated with high lactate resonance peaks in A-07 tumors and with low lactate resonance peaks in R-18 tumors. Similarly, metastatic and non-metastatic R-18 tumors showed significantly different metabolic profiles, but not metastatic and non-metastatic A-07 tumors, probably because some samples from the metastatic A-07 tumors were derived from tumor regions without hypoxic tissue. This study suggests that 1H-HR-MAS-MRS may be a valuable tool for evaluating the role of hypoxia and lactate in tumor metastasis as well as for identification of metastasis-associated metabolic profiles

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

Introduction 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 analyzed using gene expression microarrays. Expression data of genes encoding proteins in the choline metabolism pathway were analyzed 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. Conclusion 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

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

Introduction: 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 analyzed using gene expression microarrays. Expression data of genes encoding proteins in the choline metabolism pathway were analyzed 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. Conclusion: 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