Magnetic resonance metabolic profiling of breast cancer tissue obtained with core needle biopsy for predicting pathologic response to neoadjuvant chemotherapy

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer

BACKGROUND

Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.

METHODS

Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.

RESULTS

The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.

CONCLUSION

We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Cancer insights from magnetic resonance spectroscopy of cells and excised tumors

Multinuclear ex vivo magnetic resonance spectroscopy (MRS) of cancer cells, xenografts, human cancer tissue, and biofluids is a rapidly expanding field that is providing unique insights into cancer. Starting from the 1970s, the field has continued to evolve as a stand-alone technology or as a complement to in vivo MRS to characterize the metabolome of cancer cells, cancer-associated stromal cells, immune cells, tumors, biofluids and, more recently, changes in the metabolome of organs induced by cancers. Here, we review some of the insights into cancer obtained with ex vivo MRS and provide a perspective of future directions. Ex vivo MRS of cells and tumors provides opportunities to understand the role of metabolism in cancer immune surveillance and immunotherapy. With advances in computational capabilities, the integration of artificial intelligence to identify differences in multinuclear spectral patterns, especially in easily accessible biofluids, is providing exciting advances in detection and monitoring response to treatment. Metabolotheranostics to target cancers and to normalize metabolic changes in organs induced by cancers to prevent cancer-induced morbidity are other areas of future development.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

The crosstalk of the human microbiome in breast and colon cancer: A metabolomics analysis

The human microbiome's role in colon and breast cancer is described in this review. Understanding how the human microbiome and metabolomics interact with breast and colon cancer is the chief area of this study. First, the role of the gut and distal microbiome in breast and colon cancer is investigated, and the direct relationship between microbial dysbiosis and breast and colon cancer is highlighted. This work also focuses on the many metabolomic techniques used to locate prospective biomarkers, make an accurate diagnosis, and research new therapeutic targets for cancer treatment. This review clarifies the influence of anti-tumor medications on the microbiota and the proactive measures that can be taken to treat cancer using a variety of therapies, including radiotherapy, chemotherapy, next-generation biotherapeutics, gene-based therapy, integrated omics technology, and machine learning.

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.

Phosphatidylcholine-Derived Lipid Mediators: The Crosstalk Between Cancer Cells and Immune Cells

To become resistant, cancer cells need to activate and maintain molecular defense mechanisms that depend on an energy trade-off between resistance and essential functions. Metabolic reprogramming has been shown to fuel cell growth and contribute to cancer drug resistance. Recently, changes in lipid metabolism have emerged as an important driver of resistance to anticancer agents. In this review, we highlight the role of choline metabolism with a focus on the phosphatidylcholine cycle in the regulation of resistance to therapy. We analyze the contribution of phosphatidylcholine and its metabolites to intracellular processes of cancer cells, both as the major cell membrane constituents and source of energy. We further extended our discussion about the role of phosphatidylcholine-derived lipid mediators in cellular communication between cancer and immune cells within the tumor microenvironment, as well as their pivotal role in the immune regulation of therapeutic failure. Changes in phosphatidylcholine metabolism are part of an adaptive program activated in response to stress conditions that contribute to cancer therapy resistance and open therapeutic opportunities for treating drug-resistant cancers.

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.

Metabolomic Impact of Lidocaine on a Triple Negative Breast Cancer Cell Line

Background: Metabolomics and onco-anesthesia are two emerging research fields in oncology. Metabolomics (metabolites analysis) is a new diagnostic and prognostic tool that can also be used for predicting the therapeutic or toxic responses to anticancer treatments. Onco-anesthesia studies assess the impact of anesthesia on disease-free and overall survival after cancer surgery. It has been shown that local anesthetics (LA), particularly lidocaine (LIDO), exert antitumor properties both in vitro and in vivo and may alter the biologic fingerprints of cancer cells. As LA are known to impair mitochondrial bioenergetics and byproducts, the aim of the present study was to assess the impact of LIDO on metabolomic profile of a breast cancer cell line.

Methods: Breast cancer MDA-MB-231 cells were exposed for 4 h to 0.5 mM LIDO or vehicle (n = 4). The metabolomic fingerprint was characterized by high resolution magic angle spinning NMR spectroscopy (HRMAS). The multivariate technique using the Algorithm to Determine Expected Metabolite Level Alteration (ADEMA) (Cicek et al., PLoS Comput. Biol., 2013, 9, e1002859), based on mutual information to identify expected metabolite level changes with respect to a specific condition, was used to determine the metabolites variations caused by LIDO.

Results: LIDO modulates cell metabolites levels. Several pathways, including glutaminolysis, choline, phosphocholine and total choline syntheses were significantly downregulated in the LIDO group.

Discussion: This is the first study assessing the impact of LIDO on metabolomic fingerprint of breast cancer cells. Among pathways downregulated by LIDO, many metabolites are reported to be associated with adverse prognosis when present at a high titer in breast cancer patients. These results fit with the antitumor properties of LIDO and suggest its impact on metabolomics profile of cancer cells. These effects of LIDO are of clinical significance because it is widely used for local anesthesia with cutaneous infiltration during percutaneous tumor biopsy. Future in vitro and preclinical studies are necessary to assess whether metabolomics analysis requires modification of local anesthetic techniques during tumor biopsy.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

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).

Fecal Metabolic Profiling of Breast Cancer Patients during Neoadjuvant Chemotherapy Reveals Potential Biomarkers

Breast cancer (BC) is the most common form of cancer among women worldwide. Despite the huge advancements in its treatment, the exact etiology of breast cancer still remains unresolved. There is an increasing interest in the role of the gut microbiome in modulating the anti-cancer therapeutic response. It seems that alteration of the microbiome-derived metabolome potentially promotes carcinogenesis. Taken together, metabolomics has arisen as a fascinating new omics field to screen promising metabolic biomarkers. In this study, fecal metabolite profiling was performed using NMR spectroscopy, to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy (NAC) for breast cancer. Metabolic profiles of feces from patients (n = 8) following chemotherapy treatment cycles were studied. Interestingly, amino acids were found to be upregulated, while lactate and fumaric acid were downregulated in patients under the second and third cycles compared with patients before treatment. Furthermore, short-chain fatty acids (SCFAs) were significantly differentiated between the studied groups. These results strongly suggest that chemotherapy treatment plays a key role in modulating the fecal metabolomic profile of BC patients. In conclusion, we demonstrate the feasibility of identifying specific fecal metabolic profiles reflecting biochemical changes that occur during the chemotherapy treatment. These data give an interesting insight that may complement and improve clinical tools for BC monitoring.

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.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.

Meta-Analysis of Quantitative Dynamic Contrast-Enhanced MRI for the Assessment of Neoadjuvant Chemotherapy in Breast Cancer

The purpose of this meta-analysis was to determine the value of quantitative dynamic contrast-enhanced (DCE) MRI (DCE-MRI) in evaluating the response of breast cancer to neoadjuvant chemotherapy (NAC). PubMed, Embase, and Cochrane Library databases (from building to July 31, 2018) were searched to collect articles about the therapeutic evaluation of NAC using the quantitative DCE-MRI in patients with breast cancer. The sensitivities and specificities of quantitative DCE-MRI in the evaluation of NAC for breast cancer were extracted from the articles. Meta-DiSc1.4 was applied to evaluate the efficacy of the sensitivity and specificity; forest figure and summary receiver operating characteristics (SROC) were created. A total of 356 articles were enrolled in this study, including 739 cases in total, in which 218 cases were effective and the other 521 cases were ineffective to NAC, considering the pathological results as the gold standard. The sensitivity and specificity in the included 14 articles of quantitative DCE-MRI ( Ktrans, kep, and ve) in comprehensively evaluating NAC for breast cancer were 84 per cent (95% confidence interval (CI): 78–88%) and 83 per cent (95% CI: 79–86%), respectively. The area under SROC was 0.899 (95% CI: 0.867–0.943). The sensitivity and specificity in the three articles of Ktrans evaluating NAC for breast cancer were 84.1 per cent (95% CI: 71.0–92.1%) and 81.3 per cent (95% CI: 70.5%-88.5%), respectively. The area under SROC was 0.899 (95% CI: 0.834–0.962). Our study confirmed that the quantitative DCE-MRI is able to monitor NAC treatment for breast cancer because of its high sensitivity and specificity. However, there is a high degree of heterogeneity in published studies, highlighting the lack of standardization in the field.

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.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Breast Cancer Metabolomics Using NMR

Continued progress is being made in understanding the breast cancer metabolism using analytical magnetic resonance (MR)-based methods like nuclear magnetic resonance (NMR) and in-vivo MR spectroscopy (MRS). Analyses using these methods have enhanced the knowledge of altered biochemical pathways associated with breast cancer progression, regression, and pathogenesis. Comprehensive metabolic profiling of biological samples like tissues, cell lines, fine needle aspirate, and biofluids such as sera and urine enables identification of new biomarkers and abnormalities in biochemical pathways. These methods are not only useful for diagnosis, therapy monitoring, disease progression, and staging of cancer but also for the identification of new therapeutic targets and designing new treatment strategies. Additionally, in-vivo MRS studies have established choline-containing compounds (tCho) as biomarkers of malignancy, which is useful for enhancing the diagnostic specificity of magnetic resonance imaging (MRI). Recent technological developments related to in-vivo MRS such as increased magnetic field strength, multichannel phased array breast coils, and absolute quantification of tCho have provided a better understanding of the tumor heterogeneity, metabolism, and pathogenesis. This chapter focuses on providing the experimental aspects of in-vitro, ex-vivo, and in-vivo MR spectroscopy methods used for metabolomics studies of breast cancer.

Correlation of SOCS-1 gene with onset and prognosis of breast cancer

The aim of the present study is to study the expressions of suppressor of cytokine signaling (SOCS)-1 in the tumor tissues and adjacent normal tissues of patients with breast cancer. The study was also planned to investigate the association of SOCS-1 gene expression with patients' clinical pathology, molecular subtype and prognosis. A total of 60 cases of frozen and paraffin-embedded specimens of tumor tissues and corresponding adjacent normal tissues of patients with breast cancer were selected. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of SOCS-1 messenger RNA (mRNA) in the patients' tumor tissues and adjacent normal tissues. The immunohistochemical method was applied to detect the expressions of SOCS-1 proteins in the patients' breast cancer tissues and adjacent normal tissues. Moreover, the correlations of SOCS-1 protein expressions in breast cancer tissues with patients' pathological parameters, molecular subtypes and prognosis were analyzed in combination with the clinical data. The results of RT-qPCR showed that the SOCS-1 mRNA expression in breast cancer tissues was significantly lower than that in adjacent normal tissues (p<0.01). The immunohistochemical results indicated that the positive expression rate of the SOCS-1 proteins in breast cancer tissues (23.33%) was remarkably lower than that in adjacent normal tissues (88.33%) (p<0.01). The low expression of SOCS-1 in breast cancer tissues was related to lymph node metastasis and clinical staging. The positive expression rates of the luminal A SOCS-1 proteins were the highest (47.62%) (p<0.01). The 5-year overall survival rate of the breast cancer patients was 63.33% (38/60). The univariate survival analysis revealed that the patients with low expression of SOCS-1 had poorer prognosis. In conclusion, the low expression of SOCS-1 plays a key role in the pathogenesis of breast cancer; in particular, it is associated with the lymph node metastasis and clinical staging of the tumor; so, the SOCS-1 expression in breast cancer tissues can be regarded as an important reference for the prognostic estimation of breast cancer.

Metabolomics in breast cancer: A decade in review

Breast cancer (BC) is a heterogeneous disease which has been characterised and stratified by many platforms such as clinicopathological risk factors, genomic assays, computer generated models, and various "-omic" technologies. Genomic, proteomic and transcriptomic analysis in breast cancer research is well established, and metabolomics, which can be considered a downstream manifestation of the former disciplines, is of growing interest. The past decade has seen significant progress made within the field of clinical metabolomic BC research, with several groups demonstrating results with significant promise in the setting of BC screening and biological characterisation, as well as future potential for prognostic metabolomic biomarkers.

Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry

Reports of the metabolomic characteristics of esophageal cancer are limited. In the present study, we thus conducted metabolome analysis of paired tumor tissues (Ts) and non-tumor esophageal tissues (NTs) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). The Ts and surrounding NTs were surgically excised pair-wise from 35 patients with esophageal cancer. Following tissue homogenization and metabolite extraction, a total of 110 compounds were absolutely quantified by CE-TOFMS. We compared the concentrations of the metabolites between Ts and NTs, between pT1 or pT2 (pT1-2) and pT3 or pT4 (pT3-4) stage, and between node-negative (pN-) and node-positive (pN+) samples. Principal component analysis and hierarchical clustering analysis revealed clear metabolomic differences between Ts and NTs. Lactate and citrate levels in Ts were significantly higher (P=0.001) and lower (P<0.001), respectively, than those in NTs, which corroborated with the Warburg effect in Ts. The concentrations of most amino acids apart from glutamine were higher in Ts than in NTs, presumably due to hyperactive glutaminolysis in Ts. The concentrations of malic acid (P=0.015) and citric acid (P=0.008) were significantly lower in pT3-4 than in pT1-2, suggesting the downregulation of tricarboxylic acid (TCA) cycle activity in pT3-4. On the whole, in this study, we demonstrate significantly different metabolomic characteristics between tumor and non-tumor tissues and identified a novel set of metabolites that were strongly associated with the degree of tumor progression. A further understanding of cancer metabolomics may enable the selection of more appropriate treatment strategies, thereby contributing to individualized medicine.

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 (HR MAS) nuclear magnetic resonance (NMR) spectroscopy is increasingly being used to study metabolite levels in human breast cancer tissue, assessing, for instance, correlations with prognostic factors, survival outcome or therapeutic response. However, the impact of intratumoral heterogeneity on metabolite levels in breast tumor tissue has not been studied comprehensively. More specifically, when biopsy material is analyzed, it remains questionable whether one biopsy is representative of the entire tumor. Therefore, multi-core sampling (n = 6) of tumor tissue from three patients with breast cancer, followed by lipid (0.9- and 1.3-ppm signals) and metabolite quantification using HR MAS ¹ H NMR, was performed, resulting in the quantification of 32 metabolites. The mean relative standard deviation across all metabolites for the six tumor cores sampled from each of the three tumors ranged from 0.48 to 0.74. This was considerably higher when compared with a morphologically more homogeneous tissue type, here represented by murine liver (0.16-0.20). Despite the seemingly high variability observed within the tumor tissue, a random forest classifier trained on the original sample set (training set) was, with one exception, able to correctly predict the tumor identity of an independent series of cores (test set) that were additionally sampled from the same three tumors and analyzed blindly. Moreover, significant differences between the tumors were identified using one-way analysis of variance (ANOVA), indicating that the intertumoral differences for many metabolites were larger than the intratumoral differences for these three tumors. That intertumoral differences, on average, were larger than intratumoral differences was further supported by the analysis of duplicate tissue cores from 15 additional breast tumors. In summary, despite the observed intratumoral variability, the results of the present study suggest that the analysis of one, or a few, replicates per tumor may be acceptable, and supports the feasibility of performing reliable analyses of patient tissue.

Metabolomic prediction of treatment outcome in pancreatic ductal adenocarcinoma patients receiving gemcitabine

PURPOSE

Resistance to gemcitabine remains a key challenge in the treatment of pancreatic ductal adenocarcinoma (PDAC), necessitating the constant search for effective strategies for a priori prediction of clinical outcome. While the existing studies focused on aberration of drug disposition genes and proteins as molecular predictors of gemcitabine treatment outcomes, the metabolic aberration associated with chemoresistance in clinical PDAC has been neglected. This exploratory study investigated the potential role of tissue metabolomics in characterizing the clinical treatment outcome of gemcitabine therapy.

METHODS

Surgically resected tumors from PDAC patients who underwent gemcitabine-based adjuvant chemotherapy (n = 25) were subjected to metabotyping using gas chromatography/time-of-flight mass spectrometry (GC/TOFMS).

RESULTS

A partial least-squares discriminant analysis (PLS-DA) model clearly distinguished patients who had favorable survival [overall survival (OS) > 24 months] from those who exhibited poorer survival (OS < 16 months) (Q ² = 0.302). Receiver-operating characteristic analysis demonstrated the robustness of the PLS-DA model with an area under the curve of 1. PLS-DA revealed 19 marker metabolites (e.g., lactic acid, proline, and pyroglutamate) that shed insights into the chemoresistance of gemcitabine in PDAC. Particularly, tissue levels of lactic acid complemented transcript expression levels of human equilibrative nucleoside transporter 1 in distinguishing patients according to their overall survival.

CONCLUSION

This work established proof-of-principle for GC/TOFMS-based global metabotyping of PDAC and laid the foundation for future discovery of metabolic biomarkers predictive of gemcitabine resistance in PDAC chemotherapy.

Applications of high-resolution magic angle spinning MRS in biomedical studies II-Human diseases

High-resolution magic angle spinning (HRMAS) MRS is a powerful method for gaining insight into the physiological and pathological processes of cellular metabolism. Given its ability to obtain high-resolution spectra of non-liquid biological samples, while preserving tissue architecture for subsequent histopathological analysis, the technique has become invaluable for biochemical and biomedical studies. Using HRMAS MRS, alterations in measured metabolites, metabolic ratios, and metabolomic profiles present the possibility to improve identification and prognostication of various diseases and decipher the metabolomic impact of drug therapies. In this review, we evaluate HRMAS MRS results on human tissue specimens from malignancies and non-localized diseases reported in the literature since the inception of the technique in 1996. We present the diverse applications of the technique in understanding pathological processes of different anatomical origins, correlations with in vivo imaging, effectiveness of therapies, and progress in the HRMAS methodology.

The relationship between MMP-2 and MMP-9 expression levels with breast cancer incidence and prognosis

The relationship between the expression levels of matrix metalloproteinase-2 (MMP-2) and MMP-9 and breast cancer prognosis was studied. Two breast cancer cell lines (MDA-MB-231 and MCF-7) and one human normal breast cell line (HS578Bst) were investigated. Fluorescence real-time reverse transcription-polymerase chain reaction (RT-PCR) and western blotting were used to detect cellular mRNA and protein MMP-2 and MMP-9 expression levels. Breast cancer tissue samples from 80 patients and tumor-adjacent normal tissue samples from 40 patients were collected, and MMP-2 and MMP-9 expression in these samples were examined using immunohistochemistry (IHC). The relationship of MMP-2 and MMP-9 expression levels with breast cancer patient clinicopathological parameters and prognosis was analyzed. RT-PCR and western blot results showed that MMP-2 and MMP-9 mRNA and protein expression levels were significantly higher in MDA-MB-231 and MCF-7 cells than in HS578Bst cells. A high expression of MMP-2 and MMP-9 was found in 83.75% (67/80) and 78.75% (63/80) of breast cancer tissue samples, respectively. MMP-2 and MMP-9 expression in breast cancer tissues were significantly different from that in tumor-adjacent normal tissues (p<0.01). MMP-2 and MMP-9 expression levels in breast cancer tissues were correlated with lymph node metastasis and tumor staging. Single factor survival analysis showed that MMP-2 and MMP-9 were factors influencing breast cancer prognosis. MMP-2 and MMP-9 are highly expressed in breast cancer tissues and are closely related to lymph node metastasis and tumor staging. MMP-2 and MMP-9 can be used as reference indices for guiding breast cancer prognosis and treatment.

Magnetic resonance metabolic profiling of estrogen receptor-positive breast cancer: correlation with currently used molecular markers

Estrogen receptor (ER)-positive breast cancers overall have a good prognosis, however, some patients suffer relapses and do not respond to endocrine therapy. The purpose of this study was to determine whether there are any correlations between high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) metabolic profiles of core needle biopsy (CNB) specimens and the molecular markers currently used in patients with ER-positive breast cancers. The metabolic profiling of CNB samples from 62 ER-positive cancers was performed by HR-MAS MRS. Metabolic profiles were compared according to human epidermal growth factor receptor 2 (HER2) and Ki-67 status, and luminal type, using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). In univariate analysis, the HER2-positive group was shown to have higher levels of glycine and glutamate, compared to the HER2-negative group (P<0.01, and P <0.01, respectively). The high Ki-67 group showed higher levels of glutamate than the low Ki-67 group without statistical significance. Luminal B cancers showed higher levels of glycine (P=0.01) than luminal A cancers. In multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles showed visible discrimination between the subgroups according to HER2 and Ki-67 status, and luminal type. This study showed that the metabolic profiles of CNB samples assessed by HR-MAS MRS can be used to detect potential prognostic biomarkers as well as to understand the difference in metabolic mechanism among subtypes of ER-positive breast cancer.

Breast Tissue Metabolism by Magnetic Resonance Spectroscopy

Metabolic alterations are known to occur with oncogenesis and tumor progression. During malignant transformation, the metabolism of cells and tissues is altered. Cancer metabolism can be studied using advanced technologies that detect both metabolites and metabolic activities. Identification, characterization, and quantification of metabolites (metabolomics) are important for metabolic analysis and are usually done by nuclear magnetic resonance (NMR) or by mass spectrometry. In contrast to the magnetic resonance imaging that is used to monitor the tumor morphology during progression of the disease and during therapy, in vivo NMR spectroscopy is used to study and monitor tumor metabolism of cells/tissues by detection of various biochemicals or metabolites involved in various metabolic pathways. Several in vivo, in vitro and ex vivo NMR studies using ¹H and ³¹P magnetic resonance spectroscopy (MRS) nuclei have documented increased levels of total choline containing compounds, phosphomonoesters and phosphodiesters in human breast cancer tissues, which is indicative of altered choline and phospholipid metabolism. These levels get reversed with successful treatment. Another method that increases the sensitivity of substrate detection by using nuclear spin hyperpolarization of ¹³C-lableled substrates by dynamic nuclear polarization has revived a great interest in the study of cancer metabolism. This review discusses breast tissue metabolism studied by various NMR/MRS methods.

The Role of High-Resolution Magic Angle Spinning 1H Nuclear Magnetic Resonance Spectroscopy for Predicting the Invasive Component in Patients with Ductal Carcinoma In Situ Diagnosed on Preoperative Biopsy

The purpose of this study was to evaluate the role of high-resolution magic angle spinning (HR-MAS) 1H nuclear magnetic resonance (NMR) spectroscopy in patients with ductal carcinoma in situ (DCIS) diagnosed on preoperative biopsy. We investigated whether the metabolic profiling of tissue samples using HR-MAS 1H NMR spectroscopy could be used to distinguish between DCIS lesions with or without an invasive component. Our institutional review board approved this combined retrospective and prospective study. Tissue samples were collected from 30 patients with pure DCIS and from 30 with DCIS accompanying invasive carcinoma. All patients were diagnosed with DCIS by preoperative core-needle biopsy and underwent surgical resection. The metabolic profiling of tissue samples was performed by HR-MAS 1H NMR spectroscopy. All observable metabolite signals were identified and quantified in all tissue samples. Metabolite intensity normalized by total spectral intensities was compared according to the tumor type using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). By univariate analysis, the metabolite concentrations of choline-containing compounds obtained with HR-MAS 1H NMR spectroscopy did not differ significantly between the pure DCIS and DCIS accompanying invasive carcinoma groups. However, the GPC/PC ratio was higher in the pure DCIS group than in the DCIS accompanying invasive carcinoma group (p = 0.004, Bonferroni-corrected p = 0.064), as well as the concentration of myo-inositol and succinate. By multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles could clearly discriminate between pure DCIS and DCIS accompanying invasive carcinoma. Our preliminary results suggest that HR-MAS MR metabolomics on breast tissue may be able to distinguish between DCIS lesions with or without an invasive component.

Metabolomics of Breast Cancer Using High-Resolution Magic Angle Spinning Magnetic Resonance Spectroscopy: Correlations with 18F-FDG Positron Emission Tomography-Computed Tomography, Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging MRI

Purpose

Our goal in this study was to find correlations between breast cancer metabolites and conventional quantitative imaging parameters using high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) and to find breast cancer subgroups that show high correlations between metabolites and imaging parameters.

Materials and methods

Between August 2010 and December 2013, we included 53 female patients (mean age 49.6 years; age range 32–75 years) with a total of 53 breast lesions assessed by the Breast Imaging Reporting and Data System. They were enrolled under the following criteria: breast lesions larger than 1 cm in diameter which 1) were suspicious for malignancy on mammography or ultrasound (US), 2) were pathologically confirmed to be breast cancer with US-guided core-needle biopsy (CNB) 3) underwent 3 Tesla MRI with dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) and positron emission tomography-computed tomography (PET-CT), and 4) had an attainable immunohistochemistry profile from CNB. We acquired spectral data by HR-MAS MRS with CNB specimens and expressed the data as relative metabolite concentrations. We compared the metabolites with the signal enhancement ratio (SER), maximum standardized FDG uptake value (SUV max), apparent diffusion coefficient (ADC), and histopathologic prognostic factors for correlation. We calculated Spearman correlations and performed a partial least squares-discriminant analysis (PLS-DA) to further classify patient groups into subgroups to find correlation differences between HR-MAS spectroscopic values and conventional imaging parameters.

Results

In a multivariate analysis, the PLS-DA models built with HR-MAS MRS metabolic profiles showed visible discrimination between high and low SER, SUV, and ADC. In luminal subtype breast cancer, compared to all cases, high SER, ADV, and SUV were more closely clustered by visual assessment. Multiple metabolites were correlated with SER and SUV in all cases. Multiple metabolites showed correlations with SER and SUV in the ER positive, HER2 negative, and Ki-67 negative groups.

Conclusion

High levels of PC, choline, and glycine acquired from HR-MAS MRS using CNB specimens were noted in the high SER group via DCE MRI and the high SUV group via PET-CT, with significant correlations between choline and SER and between PC and SUV. Further studies should investigate whether HR-MAS MRS using CNB specimens can provide similar or more prognostic information than conventional quantitative imaging parameters.

Intratumoral Agreement of High-Resolution Magic Angle Spinning Magnetic Resonance Spectroscopic Profiles in the Metabolic Characterization of Breast Cancer

High-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopy data may serve as a biomarker for breast cancer, with only a small volume of tissue sample required for assessment. However, previous studies utilized only a single tissue sample from each patient. The aim of this study was to investigate whether intratumoral location and biospecimen type affected the metabolic characterization of breast cancer assessed by HR-MAS MR spectroscopy. This prospective study was approved by the institutional review board and informed consent was obtained. Preoperative core-needle biopsies (CNBs), central, and peripheral surgical tumor specimens were prospectively collected under ultrasound (US) guidance in 31 patients with invasive breast cancer. Specimens were assessed with HR-MAS MR spectroscopy. The reliability of metabolite concentrations was evaluated and multivariate analysis was performed according to intratumoral location and biospecimen type. There was a moderate or higher agreement between the relative concentrations of 94.3% (33 of 35) of metabolites in the center and periphery, 80.0% (28 of 35) of metabolites in the CNB and central surgical specimens, and 82.9% (29 of 35) of metabolites between all 3 specimen types. However, there was no significant agreement between the concentrations of phosphocholine (PC) and phosphoethanolamine (PE) in the center and periphery. The concentrations of several metabolites (adipate, arginine, fumarate, glutamate, PC, and PE) had no significant agreement between the CNB and central surgical specimens. In conclusion, most HR-MAS MR spectroscopic data do not differ based on intratumoral location or biospecimen type. However, some metabolites may be affected by specimen-related variables, and caution is recommended in decision-making based solely on metabolite concentrations, particularly PC and PE. Further validation through future studies is needed for the clinical implementation of these biomarkers based on data from a single tissue sample.

Discrimination of Basal Cell Carcinoma from Normal Skin Tissue Using High-Resolution Magic Angle Spinning 1H NMR Spectroscopy

High-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy is a useful tool for investigating the metabolism of various cancers. Basal cell carcinoma (BCC) is the most common skin cancer. However, to our knowledge, data on metabolic profiling of BCC have not been reported in the literature. The objective of the present study was to investigate the metabolic profiling of cutaneous BCC using HR-MAS ¹H NMR spectroscopy. HR-MAS ¹H NMR spectroscopy was used to analyze the metabolite profile and metabolite intensity of histopathologically confirmed BCC tissues and normal skin tissue (NST) samples. The metabolic intensity normalized to the total spectral intensities in BCC and NST was compared, and multivariate analysis was performed with orthogonal partial least-squares discriminant analysis (OPLS-DA). P values < 0.05 were considered statistically significant. Univariate analysis revealed 9 metabolites that showed statistically significant difference between BCC and NST. In multivariate analysis, the OPLS-DA models built with the HR-MAS NMR metabolic profiles revealed a clear separation of BCC from NST. The receiver operating characteristic curve generated from the results revealed an excellent discrimination of BCC from NST with an area under the curve (AUC) value of 0.961. The present study demonstrated that the metabolite profile and metabolite intensity differ between BCC and NST, and that HR-MAS ¹H NMR spectroscopy can be a valuable tool in the diagnosis of BCC.

Impact of Freezing Delay Time on Tissue Samples for Metabolomic Studies

Introduction

Metabolic profiling of intact tumor tissue by high-resolution magic angle spinning (HR MAS) MR spectroscopy (MRS) provides important biological information possibly useful for clinical diagnosis and development of novel treatment strategies. However, generation of high-quality data requires that sample handling from surgical resection until analysis is performed using systematically validated procedures. In this study, we investigated the effect of postsurgical freezing delay time on global metabolic profiles and stability of individual metabolites in intact tumor tissue.

Materials and methods

Tumor tissue samples collected from two patient-derived breast cancer xenograft models (n = 3 for each model) were divided into pieces that were snap-frozen in liquid nitrogen at 0, 15, 30, 60, 90, and 120 min after surgical removal. In addition, one sample was analyzed immediately, representing the metabolic profile of fresh tissue exposed neither to liquid nitrogen nor to room temperature. We also evaluated the metabolic effect of prolonged spinning during the HR MAS experiments in biopsies from breast cancer patients (n = 14). All samples were analyzed by proton HR MAS MRS on a Bruker Avance DRX600 spectrometer, and changes in metabolic profiles were evaluated using multivariate analysis and linear mixed modeling.

Results

Multivariate analysis showed that the metabolic differences between the two breast cancer models were more prominent than variation caused by freezing delay time. No significant changes in levels of individual metabolites were observed in samples frozen within 30 min of resection. After this time point, levels of choline increased, whereas ascorbate, creatine, and glutathione (GS) levels decreased. Freezing had a significant effect on several metabolites but is an essential procedure for research and biobank purposes. Furthermore, four metabolites (glucose, glycine, glycerophosphocholine, and choline) were affected by prolonged HR MAS experiment time possibly caused by physical release of metabolites caused by spinning or due to structural degradation processes.

Conclusion

The MR metabolic profiles of tumor samples are reproducible and robust to variation in postsurgical freezing delay up to 30 min.