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.

Triple-negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined 'omics' approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi-dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

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The goal of this study was to investigate the use of lactate and alanine as metabolic biomarkers of prostate cancer using 1

Separating indolent from aggressive prostate cancer is an important clinical challenge for identifying patients eligible for active surveillance, thereby reducing the risk of overtreatment. The purpose of this study was to assess prostate cancer aggressiveness by metabolic profiling of prostatectomy tissue and to identify specific metabolites as biomarkers for aggressiveness. Prostate tissue samples (n = 158, 48 patients) with a high cancer content (mean: 61.8%) were obtained using a new harvesting method, and metabolic profiles of samples representing different Gleason scores (GS) were acquired by high resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS). Multivariate analysis (PLS, PLS-DA) and absolute quantification (LCModel) were used to examine the ability to predict cancer aggressiveness by comparing low grade (GS = 6, n = 30) and high grade (GS≥7, n = 81) cancer with normal adjacent tissue (n = 47). High grade cancer tissue was distinguished from low grade cancer tissue by decreased concentrations of spermine (p = 0.0044) and citrate (p = 7.73·10−4), and an increase in the clinically applied (total choline+creatine+polyamines)/citrate (CCP/C) ratio (p = 2.17·10−4). The metabolic profiles were significantly correlated to the GS obtained from each tissue sample (r = 0.71), and cancer tissue could be distinguished from normal tissue with sensitivity 86.9% and specificity 85.2%. Overall, our findings show that metabolic profiling can separate aggressive from indolent prostate cancer. This holds promise for the benefit of applying in vivo magnetic resonance spectroscopy (MRS) within clinical MR imaging investigations, and HR-MAS analysis of transrectal ultrasound-guided biopsies has a potential as an additional diagnostic tool.

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).