Impact of intratumoral heterogeneity of breast cancer tissue on quantitative metabolomics using high-resolution magic angle spinning 1 H NMR spectroscopy

High-resolution magic angle spinning NMR for intact biological specimen analysis: Initial discovery, recent developments, and future directions

High-resolution magic angle spinning (HRMAS) NMR, an approach for intact biological material analysis discovered more than 25 years ago, has been advanced by many technical developments and applied to many biomedical uses. This article provides a history of its discovery, first by explaining the key scientific advances that paved the way for HRMAS NMR's invention, and then by turning to recent developments that have profited from applying and advancing the technique during the last 5 years. Developments aimed at directly impacting healthcare include HRMAS NMR metabolomics applications within studies of human disease states such as cancers, brain diseases, metabolic diseases, transplantation medicine, and adiposity. Here, the discussion describes recent HRMAS NMR metabolomics studies of breast cancer and prostate cancer, as well as of matching tissues with biofluids, multimodality studies, and mechanistic investigations, all conducted to better understand disease metabolic characteristics for diagnosis, opportune windows for treatment, and prognostication. In addition, HRMAS NMR metabolomics studies of plants, foods, and cell structures, along with longitudinal cell studies, are reviewed and discussed. Finally, inspired by the technique's history of discoveries and recent successes, future biomedical arenas that stand to benefit from HRMAS NMR-initiated scientific investigations are presented.

The Relationship between Histological Composition and Metabolic Profile in Breast Tumors and Peritumoral Tissue Determined with 1H HR-MAS NMR Spectroscopy

Breast tumors constitute the complex entities composed of cancer cells and stromal components. The compositional heterogeneity should be taken into account in bulk tissue metabolomics studies. The aim of this work was to find the relation between the histological content and ¹H HR-MAS (high-resolution magic angle spinning nuclear magnetic resonance) metabolic profiles of the tissue samples excised from the breast tumors and the peritumoral areas in 39 patients diagnosed with invasive breast carcinoma. The total number of the histologically verified specimens was 140. The classification accuracy of the OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) model differentiating the cancerous from non-involved samples was 87% (sensitivity of 72.2%, specificity of 92.3%). The metabolic contents of the epithelial and stromal compartments were determined from a linear regression analysis of the levels of the evaluated compounds against the cancer cell fraction in 39 samples composed mainly of cancer cells and intratumoral fibrosis. The correlation coefficients between the levels of several metabolites and a tumor purity were found to be dependent on the tumor grade (I vs II/III). The comparison of the levels of the metabolites in the intratumoral fibrosis (obtained from the extrapolation of the regression lines to 0% cancer content) to those levels in the fibrous connective tissue beyond the tumors revealed a profound metabolic reprogramming in the former tissue. The joint analysis of the metabolic profiles of the stromal and epithelial compartments in the breast tumors contributes to the increased understanding of breast cancer biology.

Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.

Impact of intratumoral heterogeneity on the metabolic profiling of breast cancer tissue using high-resolution magic angle spinning magnetic resonance spectroscopy

High-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) is a useful metabolic profiling technique for human tissue. However, the impact of intratumoral heterogeneity on the metabolite levels of breast cancers is not yet established. The purpose of this prospective study was to investigate whether the tumor cell fraction of core needle biopsy (CNB) specimens of breast cancers affect metabolic profiles assessed with HR-MAS MRS. From June 2015 to December 2016, 46 patients with 47 breast cancers were enrolled. HR-MAS MRS was used for the metabolic profiling of 285 CNB specimens from the 47 cancers. Multiple CNB samples (range 2-8) for the HR-MAS MRS experiment were obtained from surgical specimens under ultrasound guidance following surgical removal of the tumor. Tumor cell fraction was expressed as a percentage of the tumor cell volume relative to the total tumor volume contained in each CNB sample. Metabolite quantification levels were compared according to primary tumor characteristics using the t-test. Multivariate analyses were performed including primary tumor characteristics and tumor cell percentages as variables. Correlations between tumor cell percentage and metabolite levels in the CNB specimens were assessed according to the immunohistochemical status of the primary tumor. In univariate analysis, levels of choline-containing compounds, glutamate, glutamine, glycine, serine, and taurine were correlated with primary tumor characteristics. In multivariate analysis, most metabolite levels were not affected by tumor cell percentage. Tumor cell percentage showed poor correlation with metabolite levels in hormone receptor-positive cancer and triple-negative cancer, and poor to fair correlation with metabolite levels in HER2-positive cancer. This study showed that differences in the tumor cell fraction of CNB samples do not affect predictions on the primary cancer from which the samples are obtained.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism

A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.

Metabolic Adaptations in an Endocrine-Related Breast Cancer Mouse Model Unveil Potential Markers of Tumor Response to Hormonal Therapy

Breast cancer (BC) is the most common type of cancer in women and, in most cases, it is hormone-dependent (HD), thus relying on ovarian hormone activation of intracellular receptors to stimulate tumor growth. Endocrine therapy (ET) aimed at preventing hormone receptor activation is the primary treatment strategy, however, about half of the patients, develop resistance in time. This involves the development of hormone independent tumors that initially are ET-responsive (HI), which may subsequently become resistant (HIR). The mechanisms that promote the conversion of HI to HIR tumors are varied and not completely understood. The aim of this work was to characterize the metabolic adaptations accompanying this conversion through the analysis of the polar metabolomes of tumor tissue and non-compromised mammary gland from mice implanted subcutaneously with HD, HI and HIR tumors from a medroxyprogesterone acetate (MPA)-induced BC mouse model. This was carried out by nuclear magnetic resonance (NMR) spectroscopy of tissue polar extracts and data mining through multivariate and univariate statistical analysis. Initial results unveiled marked changes between global tumor profiles and non-compromised mammary gland tissues, as expected. More importantly, specific metabolic signatures were found to accompany progression from HD, through HI and to HIR tumors, impacting on amino acids, nucleotides, membrane percursors and metabolites related to oxidative stress protection mechanisms. For each transition, sets of polar metabolites are advanced as potential markers of progression, including acquisition of resistance to ET. Putative biochemical interpretation of such signatures are proposed and discussed.

Metabolic Profiling of Thymic Epithelial Tumors Hints to a Strong Warburg Effect, Glutaminolysis and Precarious Redox Homeostasis as Potential Therapeutic Targets

Simple Summary

Thymomas and thymic carcinomas (TCs) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. This is the first metabolomics investigation on thymic epithelial tumors employing nuclear magnetic resonance spectroscopy of tissue samples. We could detect and quantify up to 37 metabolites in the major tumor subtypes, including acetylcholine that was not previously detected in other non-endocrine cancers. A metabolite-based cluster analysis distinguished three clinically relevant tumor subgroups, namely indolent and aggressive thymomas, as well as TCs. A metabolite-based metabolic pathway analysis also gave hints to activated metabolic pathways shared between aggressive thymomas and TCs. This finding was largely backed by enrichment of these pathways at the transcriptomic level in a large, publicly available, independent TET dataset. Due to the differential expression of metabolites in thymic epithelial tumors versus normal thymus, pathways related to proline, cysteine, glutathione, lactate and glutamine appear as promising therapeutic targets. From these findings, inhibitors of glutaminolysis and of the downstream TCA cycle are anticipated to be rational therapeutic strategies. If our results can be confirmed in future, sufficiently powered studies, metabolic signatures may contribute to the identification of new therapeutic options for aggressive thymomas and TCs.

Abstract

Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs (n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs (n = 115) were investigated in the public “The Cancer Genome Atlas” (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong “Warburg effect”, glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)

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Automatic assignment of metabolites of 2D-TOCSY NMR spectra.

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Semi-supervised learning for metabolic profiling.

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Deconvolution and metabolic profiling of 2D NMR spectra using Machine Learning.

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Accurate Automatic multicomponent assignment of 2D NMR spectrum.

Metabolomics is an expanding field of medical diagnostics since many diseases cause metabolic reprogramming alteration. Additionally, the metabolic point of view offers an insight into the molecular mechanisms of diseases. Due to the complexity of metabolic assignment dependent on the 1D NMR spectral analysis, 2D NMR techniques are preferred because of spectral resolution issues. Thus, in this work, we introduce an automated metabolite identification and assignment from ¹H-¹H TOCSY (total correlation spectroscopy) using real breast cancer tissue. The new approach is based on customized and extended semi-supervised classifiers: KNFST, SVM, third (PC3) and fourth (PC4) degree polynomial. In our approach, metabolic assignment is based only on the vertical and horizontal frequencies of the metabolites in the ¹H–¹H TOCSY. KNFST and SVM show high performance (high accuracy and low mislabeling rate) in relatively low size of initially labeled training data. PC3 and PC4 classifiers showed lower accuracy and high mislabeling rates, and both classifiers fail to provide an acceptable accuracy at extremely low size (≤9% of the entire dataset) of initial training data. Additionally, semi-supervised classifiers were implemented to obtain a fully automatic procedure for signal assignment and deconvolution of TOCSY, which is a big step forward in NMR metabolic profiling. A set of 27 metabolites were deduced from the TOCSY, and their assignments agreed with the metabolites deduced from a 1D NMR spectrum of the same sample analyzed by conventional human-based methodology.

Is Elevated Choline on Magnetic Resonance Spectroscopy a Reliable Marker of Breast Lesion Malignancy?

Background

Contemporary magnetic resonance imaging (MRI) of the breast represents a powerful diagnostic modality for cancer detection, with excellent sensitivity and high specificity. Magnetic resonance spectroscopy (MRS) is being explored as an additional tool for improving specificity in breast cancer detection, using multiparametric MRI. The aim of this study was to examine the possibility of 1H-MRS to discriminate malignant from benign breast lesions, using elevated choline (Cho) peak as an imaging biomarker.

Methods

A total of 60 patients were included in this prospective study: 30 with malignant (average age, 55.2 years; average lesion size, 35 mm) and 30 with benign breast lesions (average age, 44.8 years; average lesion size, 20 mm), who underwent multiparametric MRI with multivoxel 3D ¹H-MRS on a 1.5-T scanner in a 3-year period. Three patients with benign breast lesions were excluded from the study. All lesions were histologically verified. Peaks identified on ¹H-MRS were lipid (0.9, 2.3, 2.8, and 5.2 ppm), choline (3.2 ppm), and water peaks (4.7 ppm). Sensitivity and specificity, as well as positive and negative predictive values, were defined using ROC curves. Cohen's Kappa test of inter-test reliability was performed [testing the agreement between ¹H-MRS and histologic finding, and ¹H-MRS and MR mammography (MRM)].

Results

Choline peak was elevated in 24/30 malignant lesions and in 20/27 benign breast lesions. The sensitivity of ¹H-MRS was 0.8, specificity was 0.741, positive predictive value was 0.774, and negative predictive value was 0.769. Area under ROC was 0.77 (CI 0.640-0.871). Inter-test reliability between ¹H-MRS and histologic finding was 0.543 (moderate agreement) and that between ¹H-MRS and MRM was 0.573 (moderate agreement). False-negative findings were most frequently observed in invasive lobular cancers, while false-positive findings were most frequently observed in adenoid fibroadenomas.

Conclusion

Although elevation of the choline peak has a good sensitivity and specificity in breast cancer detection, both are significantly lower than those of multiparametric MRM. Inclusion of spectra located on tumor margins as well as analysis of lipid peaks could aid both sensitivity and specificity. An important ratio of false-positive and false-negative findings in specific types of breast lesions (lobular cancer and adenoid fibroadenoma) suggests interpreting these lesions with a caveat.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

Methodological Advancements for Investigating Intra-tumoral Heterogeneity in Breast Cancer at the Bench and Bedside

There is a major need to overcome therapeutic resistance and metastasis that eventually arises in many breast cancer patients. Therapy resistant and metastatic tumors are increasingly recognized to possess intra-tumoral heterogeneity (ITH), a diversity of cells within an individual tumor. First hypothesized in the 1970s, the possibility that this complex ITH may endow tumors with adaptability and evolvability to metastasize and evade therapies is now supported by multiple lines of evidence. Our understanding of ITH has been driven by recent methodological advances including next-generation sequencing, computational modeling, lineage tracing, single-cell technologies, and multiplexed in situ approaches. These have been applied across a range of specimens, including patient tumor biopsies, liquid biopsies, cultured cell lines, and mouse models. In this review, we discuss these approaches and how they have deepened our understanding of the mechanistic origins of ITH amongst tumor cells, including stem cell-like differentiation hierarchies and Darwinian evolution, and the functional role for ITH in breast cancer progression. While ITH presents a challenge for combating tumor evolution, in-depth analyses of ITH in clinical biopsies and laboratory models hold promise to elucidate therapeutic strategies that should ultimately improve outcomes for breast cancer patients.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

Hormone-Independent Mouse Mammary Adenocarcinomas with Different Metastatic Potential Exhibit Different Metabolic Signatures

The metabolic characteristics of metastatic and non-metastatic breast carcinomas remain poorly studied. In this work, untargeted Nuclear Magnetic Resonance (NMR) metabolomics was used to compare two medroxyprogesterone acetate (MPA)-induced mammary carcinomas lines with different metastatic abilities. Different metabolic signatures distinguished the non-metastatic (59-2-HI) and the metastatic (C7-2-HI) lines, with glucose, amino acid metabolism, nucleotide metabolism and lipid metabolism as the major affected pathways. Non-metastatic tumours appeared to be characterised by: (a) reduced glycolysis and tricarboxylic acid cycle (TCA) activities, possibly resulting in slower NADH biosynthesis and reduced mitochondrial transport chain activity and ATP synthesis; (b) glutamate accumulation possibly related to reduced glutathione activity and reduced mTORC1 activity; and (c) a clear shift to lower phosphoscholine/glycerophosphocholine ratios and sphingomyelin levels. Within each tumour line, metabolic profiles also differed significantly between tumours (i.e., mice). Metastatic tumours exhibited marked inter-tumour changes in polar compounds, some suggesting different glycolytic capacities. Such tumours also showed larger intra-tumour variations in metabolites involved in nucleotide and cholesterol/fatty acid metabolism, in tandem with less changes in TCA and phospholipid metabolism, compared to non-metastatic tumours. This study shows the valuable contribution of untargeted NMR metabolomics to characterise tumour metabolism, thus opening enticing opportunities to find metabolic markers related to metastatic ability in endocrine breast cancer.

Imaging breast cancer using hyperpolarized carbon-13 MRI

Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2-), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2- invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.

Clinical applications of breast cancer metabolomics using high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS): systematic scoping review

INTRODUCTION

Breast cancer is a heterogeneous disease with different prognoses and responses to systemic treatment depending on its molecular characteristics, which makes it imperative to develop new biomarkers for an individualized diagnosis and personalized oncological treatment. Ex vivo high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS) is the most common technique for metabolic quantification in human surgical and biopsy tissue specimens.

OBJECTIVE

To perform a review of the current available literature on the clinical applications of HRMAS 1H MRS metabolic analysis in tissue samples of breast cancer patients.

METHODS

This systematic scoping review included original research papers published in the English language in peer-reviewed journals. Study selection was performed independently by two reviewers and preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were followed.

RESULTS

The literature search returned 159 studies and 26 papers were included as part of this systematic review. There was considerable variation regarding tissue type, aims, and statistical analysis methods across the different studies. To facilitate the interpretation of the results, the included studies were grouped according to their aims or main outcomes into: feasibility and tumor diagnosis (n = 6); tumor heterogeneity (n = 2); correlation with proteomics/transcriptomics (n = 3); correlation with prognostic factors (n = 11); and response evaluation to NAC (n = 4).

CONCLUSION

There is a lot of potential in including metabolic information of breast cancer tissue obtained with HRMAS 1H MRS. To date, studies show that metabolic concentrations quantified by this technique can be related to the diagnosis, prognosis, and treatment response in breast cancer patients.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Breast Cancer Metabolomics: From Analytical Platforms to Multivariate Data Analysis. A Review

Cancer is a major health issue worldwide for many years and has been increasing significantly. Among the different types of cancer, breast cancer (BC) remains the leading cause of cancer-related deaths in women being a disease caused by a combination of genetic and environmental factors. Nowadays, the available diagnostic tools have aided in the early detection of BC leading to the improvement of survival rates. However, better detection tools for diagnosis and disease monitoring are still required. In this sense, metabolomic NMR, LC-MS and GC-MS-based approaches have gained attention in this field constituting powerful tools for the identification of potential biomarkers in a variety of clinical fields. In this review we will present the current analytical platforms and their applications to identify metabolites with potential for BC biomarkers based on the main advantages and advances in metabolomics research. Additionally, chemometric methods used in metabolomics will be highlighted.

HR-MAS NMR Based Quantitative Metabolomics in Breast Cancer

High resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy is increasingly used for profiling of breast cancer tissue, delivering quantitative information for approximately 40 metabolites. One unique advantage of the method is that it can be used to analyse intact tissue, thereby requiring only minimal sample preparation. Importantly, since the method is non-destructive, it allows further investigations of the same specimen using for instance transcriptomics. Here, we discuss technical aspects critical for a successful analysis—including sample handling, measurement conditions, pulse sequences for one- and two dimensional analysis, and quantification methods—and summarize available studies, with a focus on significant associations of metabolite levels with clinically relevant parameters.

Alteration in lipid composition differentiates breast cancer tissues: a 1H HRMAS NMR metabolomic study

INTRODUCTION

Breast cancer is the most frequent diagnosed cancer among women with a mortality rate of 15% of all cancer related deaths in women. Breast cancer is heterogeneous in nature and produces plethora of metabolites allowing its early detection using molecular diagnostic techniques like magnetic resonance spectroscopy.

OBJECTIVES

To evaluate the variation in metabolic profile of breast cancer focusing on lipids as triglycerides (TG) and free fatty acids (FFA) that may alter in malignant breast tissues and lymph nodes from adjacent benign breast tissues by HRMAS ¹H NMR spectroscopy.

METHODS

The ¹H NMR spectra recorded on 173 tissue specimens comprising of breast tumor tissues, adjacent tissues, few lymph nodes and overlying skin tissues obtained from 67 patients suffering from breast cancer. Multivariate statistical analysis was employed to identify metabolites acting as major confounders for differentiation of malignancy.

RESULT

Reduction in lipid content were observed in malignant breast tissues along with a higher fraction of FFA. Four small molecule metabolites e.g., choline containing compounds (Chocc), taurine, glycine, and glutamate were also identified as major confounders. The test set for prediction provided sensitivity and specificity of more than 90% excluding the lymph nodes and skin tissues.

CONCLUSION

Fatty acids composition in breast cancer using in vivo magnetic resonance spectroscopy (MRS) is gaining its importance in clinical settings (Coum et al. in Magn Reson Mater Phys Biol Med 29:1-4, 2016). The present study may help in future for precise evaluation of lipid classification including small molecules as a source of early diagnosis of invasive ductal carcinoma by employing in vivo magnetic resonance spectroscopic methods.

Metabolic fingerprinting in breast cancer stages through 1H NMR spectroscopy-based metabolomic analysis of plasma

Breast cancer (BC) is one of the most common malignancies among women worldwide, which is indeed associated with metabolic reprogramming. However, BC is a very complex and heterogeneous disease, which can relate with the changes in metabolic profiles during BC progression. Hence, investigating the metabolic alterations during BC stage progression may reveal the deregulated pathways and useful metabolic signatures of BC. To demonstrate the metabolic insights, we opted ¹H NMR spectroscopy based metabolomics of blood plasma of early and late stage BC (N = 72) with age and gender matched healthy subjects (N = 50). Further, the metabolic profiles were analyzed to delineate the potential signatures of BC by performing multivariate and nonparametric statistical analysis in early and late stages of BC in comparison with healthy subjects. Sixteen metabolites levels were differentially changed (p < 0.05) in the early and late stages of BC from healthy subjects. Among them, the levels of hydroxybutyrate, lysine, glutamate, glucose, N-acetyl glycoprotein, Lactate were highly distinguished in BC stages and showed a good biomarker potential using receiver-operating curves based diagnostic models. Furthermore, the significant modulation and good diagnostic performances of glutamate, N-acetyl glycoprotein and Lactate in LBC as compared to EBC give their significance in the BC progression. In general, our observations demonstrate that these panels of metabolites may act as vital component of the metabolism of early to late stage BC progression. Our results also open new avenue towards early and late stage BC diagnosis and intervention implying metabolomics approaches.