Breast cancer is the most frequent form of cancer in women and improved diagnostic methods are desirable. Malignant cells have altered metabolism and metabolic mapping might become a tool in cancer diagnostics. High-resolution magic angle spinning (HR MAS) MR spectroscopy of tissue biopsies provides detailed information on metabolic composition. The 600 MHz 1H HR MAS spectra were acquired of breast cancer tissue from 85 patients and adjacent non-involved tissue from 18 of these patients. Tissue specimens were investigated by microscopy after MR analysis. The resulting spectra were examined by three different approaches. Relative intensities of glycerophosphocholine (GPC), phosphocholine (PC) and choline were compared for cancerous and non-involved specimens. Eight metabolites, choline, creatine, beta-glucose, GPC, glycine, myo-inositol, PC and taurine, were quantified from the recorded spectra and compared with tumor histological type and size, patient's lymph node status and tissue composition of sample. The spectra were also compared with tumor histological type and size, lymph node status and tissue composition of samples using principal component analysis (PCA). Tumor samples could be distinguished from non-involved samples (82% sensitivity, 100% specificity) based on relative intensities of signals from GPC, PC and choline in 1H HR MAS spectra. Tissue concentrations of metabolites showed few differences between groups of samples, which can be caused by limitations in the quantification procedure. Choline and glycine concentrations were found to be significantly higher in tumors larger than 2 cm compared with smaller tumors. PCA of MAS spectra from patients with invasive ductal carcinomas indicated a possible prediction of spread to axillary lymph nodes. Metabolite estimates and PCA of MAS spectra were influenced by the percentage of tumor cells in the investigated specimens.

High-resolution magic angle spinning (HR MAS) may develop into a new diagnostic tool for studying intact tissue samples, and several types of cancer have been investigated with promising results. In this study HR MAS spectra of breast cancer tissue from 10 patients have been compared to conventional high-resolution spectra of perchloric acid extracts of the same tissue type. The HR MAS spectra show resolution comparable to spectra of extracts, and two-dimensional techniques lead to identification of a majority of the constituents. More than 30 different metabolites have been detected and assigned. To our knowledge this is the most detailed assignment of biochemical components in intact human breast tissue. The spectra of intact breast cancer tissue differ from perchloric acid extracts by the presence of lipids and fewer signals in the low field region. HR MAS analysis of intact breast tissue specimens is a rapid method, providing spectra with resolution where relative quantification of the majority of the detected metabolites is possible.

The purpose of the study was to evaluate the use of metabolic phenotype, described by high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS), as a tool for prediction of histological grade, hormone status, and axillary lymphatic spread in breast cancer patients. Biopsies from breast cancer (n = 91) and adjacent non-involved tissue (n = 48) were excised from patients (n = 77) during surgery. HR MAS MR spectra of intact samples were acquired. Multivariate models relating spectral data to histological grade, lymphatic spread, and hormone status were designed. The multivariate methods applied were variable reduction by principal component analysis (PCA) or partial least-squares regression-uninformative variable elimination (PLS-UVE), and modelling by PLS, probabilistic neural network (PNN), or cascade correlation neural network. In the end, model verification by prediction of blind samples (n = 12) was performed. Validation of PNN training resulted in sensitivity and specificity ranging from 83 to 100% for all predictions. Verification of models by blind sample testing showed that hormone status was well predicted by both PNN and PLS (11 of 12 correct), lymphatic spread was best predicted by PLS (8 of 12), whereas PLS-UVE PNN was the best approach for predicting grade (9 of 12 correct). MR-determined metabolic phenotype may have a future role as a supplement for clinical decision-making-concerning adjuvant treatment and the adaptation to more individualised treatment protocols.

Absolute quantitative measures of breast cancer tissue metabolites can increase our understanding of biological processes. Electronic REference To access In vivo Concentrations (ERETIC) was applied to high resolution magic angle spinning MR spectroscopy (HR MAS MRS) to quantify metabolites in intact breast cancer samples. The ERETIC signal was calibrated using solutions of creatine and TSP. The largest relative errors of the ERETIC method were 8.4%, compared to 4.4% for the HR MAS MRS method using TSP as a standard. The same MR experimental procedure was applied to intact tissue samples from breast cancer patients with clinically defined good (n ¼ 13) and poor (n ¼ 16) prognosis. All samples were examined by histopathology for relative content of different tissue types and proliferation index (MIB-1) after MR analysis. The resulting spectra were analyzed by quantification of tissue metabolites (b-glucose, lactate, glycine, myo-inositol, taurine, glycerophosphocholine, phosphocholine, choline and creatine), by peak area ratios and by principal component analysis. We found a trend toward lower concentrations of glycine in patients with good prognosis (1.1 mmol/g) compared to patients with poor prognosis (1.9 mmol/g, p ¼ 0.067). Tissue metabolite concentrations (except for b-glucose) were also found to correlate to the fraction of tumor, connective, fat or glandular tissue by Pearson correlation analysis. Tissue concentrations of b-glucose correlated to proliferation index (MIB-1) with a negative correlation factor (S0.45, p ¼ 0.015), consistent with increased energy demand in proliferating tumor cells. By analyzing several metabolites simultaneously, either in ratios or by metabolic profiles analyzed by PCA, we found that tissue metabolites correlate to patients' prognoses and health status five years after surgery. This study shows that the diagnostic and prognostic potential in MR metabolite analysis of breast cancer tissue is greater when combining multiple metabolites (MR Metabolomics).

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.