Ingrid S. Gribbestad

Characterization of neoplastic and normal human breast tissues with in vivo1H MR spectroscopy

The purpose of this study was to evaluate whether the detection of choline‐containing compounds in in vivo 1H magnetic resonance spectroscopy (MRS) of breast lesions is specific for carcinomas, whether a choline peak in in vivo 1H MRS can be detected under physiological conditions of increased metabolism in breast parenchyma, and whether analysis of lipid signals can differentiate between various breast lesions and tissues. Forty patients and volunteers were examined with in vivo 1H MR spectroscopy. Three spectra with identical localization but increasing echo times were obtained. Choline‐containing compounds were detected in 9 of 11 carcinomas and in 2 of 11 benign lesions. A choline signal was also detected in five of seven volunteers who were breast‐feeding at the time of examination, demonstrating that choline compounds can be detected by in vivo 1H MRS in breast tissue under physiological conditions. Analysis of lipid signals did not contribute to differentiation between various breast lesions and tissues. J. Magn. Reson. Imaging 1999;10:159–164.

Triple-negative breast cancer: Present challenges and new perspectives

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.

Evaluation of lactate and alanine as metabolic biomarkers of prostate cancer using 1H HR-MAS spectroscopy of biopsy tissues

The goal of this study was to investigate the use of lactate and alanine as metabolic biomarkers of prostate cancer using 1H high‐resolution magic angle spinning (HR‐MAS) spectroscopy of snap‐frozen transrectal ultrasound (TRUS)‐guided prostate biopsy tissues. A long‐echo‐time rotor‐synchronized Carr‐Purcell‐Meiboom‐Gill (CPMG) sequence including an electronic reference to access in vivo concentrations (ERETIC) standard was used to determine the concentrations of lactate and alanine in 82 benign and 16 malignant biopsies (mean 26.5% ± 17.2% of core). Low concentrations of lactate (0.61 ± 0.28 mmol/kg) and alanine (0.14 ± 0.06 mmol/kg) were observed in benign prostate biopsies, and there was no significant difference between benign predominantly glandular (N = 54) and stromal (N = 28) biopsies between patients with (N = 38) and without (N = 44) a positive clinical biopsy. In biopsies containing prostate cancer there was a highly significant (P < 0.0001) increase in lactate (1.59 ± 0.61 mmol/kg) and alanine (0.26 ± 0.07 mmol/kg), and minimal overlap with lactate concentrations in benign biopsies. This study demonstrates for the first time very low concentrations of lactate and alanine in benign prostate biopsy tissues. The significant increase in the concentration of both lactate and alanine in biopsy tissue containing as little as 5% cancer could be exploited in hyperpolarized 13C spectroscopic imaging (SI) studies of prostate cancer patients. Magn Reson Med 60:510–516, 2008.

Multivariate modeling and prediction of breast cancer prognostic factors using MR metabolomics.

Axillary lymph node status together with estrogen and progesterone receptor status are important prognostic factors in breast cancer. In this study, the potential of using MR metabolomics for prediction of these prognostic factors was evaluated. Biopsies from breast cancer patients (n = 160) were excised during surgery and analyzed by high resolution magic angle spinning MR spectroscopy (HR MAS MRS). The spectral data were preprocessed and variable stability (VAST) scaled, and training and test sets were generated using the Kennard-Stone and SPXY sample selection algorithms. The data were analyzed by partial least-squares discriminant analysis (PLS-DA), probabilistic neural networks (PNNs) and Bayesian belief networks (BBNs), and blind samples (n = 50) were predicted for verification. Estrogen and progesterone receptor status was successfully predicted from the MR spectra, and were best predicted by PLS-DA with a correct classification of 44 of 50 and 39 of 50 samples, respectively. Lymph node status was best predicted by BBN with 34 of 50 samples correctly classified, indicating a relationship between metabolic profile and lymph node status. Thus, MR profiles contain prognostic information that may be of benefit in treatment planning, and MR metabolomics may become an important tool for diagnosis of breast cancer patients.

Magnetic Resonance Metabolomics of Intact Tissue: A Biotechnological Tool in Cancer Diagnostics and Treatment Evaluation

Personalized medicine is increasingly important in cancer treatment for its role in staging and its potential to improve stratification of patients. Different types of molecules, genes, proteins, and metabolites are being extensively explored as potential biomarkers. This review discusses the major findings and potential of tissue metabolites determined by high-resolution magic angle spinning magnetic resonance spectroscopy for cancer detection, characterization, and treatment monitoring.

Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE-MRI

To evaluate dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) as a tool for early prediction of response to neoadjuvant chemotherapy (NAC) and 5‐year survival in patients with locally advanced breast cancer.

Distinct choline metabolic profiles are associated with differences in gene expression for basal-like and luminal-like breast cancer xenograft models

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.

Merging transcriptomics and metabolomics - advances in breast cancer profiling

BackgroundCombining gene expression microarrays and high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) of the same tissue samples enables comparison of the transcriptional and metabolic profiles of breast cancer. The aim of this study was to explore the potential of combining these two different types of information.MethodsBreast cancer tissue from 46 patients was analyzed by HR MAS MRS followed by gene expression microarrays. Two strategies were used to combine the gene expression and metabolic data; first using multivariate analyses to identify different groups based on gene expression and metabolic data; second correlating levels of specific metabolites to transcripts to suggest new hypotheses of connections between metabolite levels and the underlying biological processes. A parallel study was designed to address experimental issues of combining microarrays and HR MAS MRS.ResultsIn the first strategy, using the microarray data and previously reported molecular classification methods, the majority of samples were classified as luminal A. Three subgroups of luminal A tumors were identified based on hierarchical clustering of the HR MAS MR spectra. The samples in one of the subgroups, designated A2, showed significantly lower glucose and higher alanine levels than the other luminal A samples, suggesting a higher glycolytic activity in these tumors. This group was also enriched for genes annotated with Gene Ontology (GO) terms related to cell cycle and DNA repair. In the second strategy, the correlations between concentrations of myo-inositol, glycine, taurine, glycerophosphocholine, phosphocholine, choline and creatine and all transcripts in the filtered microarray data were investigated. GO-terms related to the extracellular matrix were enriched among the genes that correlated the most to myo-inositol and taurine, while cell cycle related GO-terms were enriched for the genes that correlated the most to choline. Additionally, a subset of transcripts was identified to have slightly altered expression after HR MAS MRS and was therefore removed from all other analyses.ConclusionsCombining transcriptional and metabolic data from the same breast carcinoma sample is feasible and may contribute to a more refined subclassification of breast cancers as well as reveal relations between metabolic and transcriptional levels.See Commentary: http://www.biomedcentral.com/1741-7015/8/73

HR MAS MR Spectroscopy in Metabolic Characterization of Cancer

One of the central hallmarks of cancer is the rapid and infinite cellular proliferation. In order to cope with increased requirement for building blocks and energy, cancer cells develop abnormal metabolic properties. Detailed assessment of cancer cell metabolism can provide biological information for use in both drug discovery and development of personalized cancer therapy. Analysis of intact tissue using high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) gives qualitative and quantitative metabolite measures with minimal sample preparation. Multivariate statistical methods are important tools for analysis of complex MR data and have in recent years been used for analysis of HR MAS data from intact tissue. HR MAS analysis of intact tissue allows combination of metabolomic data with genomic or proteomic data, and can therefore be used both for exploring the molecular biology of cancer and for clinical improvements in cancer diagnostics, prognostics and treatment planning. In this review, the basic concepts of HR MAS are presented, and its use in characterisation of cancer metabolism is discussed with specific focus on selected pathways such as choline metabolism and glycolysis. The use of HR MAS in analysis of amino acids and lipid metabolism in cancer is also reviewed. Finally, the expected role of HR-MAS in metabolic characterisation in the near future is discussed.

External standard method for the in vivo quantification of choline‐containing compounds in breast tumors by proton MR spectroscopy at 1.5 Tesla

Quantification of choline‐containing compounds observed with 1H MRS of breast tumors is of interest since such compounds have been linked to malignancy. Experiments were performed at 1.5 T with an external standard containing phosphocholine for calibration. In phantom studies, good precision was achieved after correction for T1/T2 effects. T2 values for choline were estimated for two breast cancer patients. A choline concentration of 2.0 mM was calculated for a third patient, a result comparable to in vitro findings. Magn Reson Med 46:189–192, 2001.