The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers

Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.

Copper Chaperone for Superoxide Dismutase Subtypes as a Prognostic Marker in Luminal B Breast Cancer

Background

Copper chaperone for superoxide dismutase (CCS) is an essential component of the oxidation-reduction system. In breast cancer cells, CCS expression is highly up-regulated, which contributes to cellular proliferation and migration. Breast cancer is a multifaceted disease with different tumor prognoses and responses to clinical treatments, which may be associated with multiple molecular subtypes of CCS.

Methods

The CCS expression patterns in breast cancer were investigated by TNMplot, cBioPortal, and HPA network database. The correlation of CCS expression with clinicopathological parameters was analyzed using the UALCAN database. The Cancer Genome Atlas (TCGA) data set was used to analyze the Clinical characteristics of CCS in luminal B patients. The bc-GenExMiner database was used to analyze the effects of BReast-CAncer susceptibility gene (BRCA)1/2, TP53 mutation status, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER) expression on CCS expression. The survival curves and prognostic value of CCS in luminal B breast cancer were performed through Kaplan-Meier curves, univariate and multivariate Cox regression using the PrognoScan, bc-GenExMiner, and Clinical bioinformatics analysis platform.

Results

We found that CCS expression was associated with patient age, race, ER, and PR status. We also discovered that BRCA1/2 mutations had an effect on CCS expression. The luminal B subtype had the highest CCS expression, which was linked to poor survival compared with other subtypes. In addition, Kaplan-Meier curve analysis showed that luminal B patients with high CCS mRNA expression showed a poor survival and the CCS gene is an independent predictor of outcome in patients with luminal B breast cancer by univariate and multivariate Cox regression.

Conclusions

Our findings emphasize the significant expression of CCS in luminal B breast cancer and its potential as an autonomous prognostic determinant for this specific molecular subtype. These findings suggest that CCS holds promise as a prospective marker for the treatment of luminal B breast cancer.

Circular RNA CCT3 is a unique molecular marker in bladder cancer

This study surveyed circular RNA CCT3 in bladder cancer (BCa). We recruited 85 BCa patients and 40 normal controls (Normal) and collected clinical specimens for analysis. circRNA CCT3 expression was analyzed by RT-qPCR, diagnostic accuracy was calculated by ROC curves, and survival outcomes were evaluated by survival curves. CircRNA CCT3 was overexpressed or knocked down in cells, thereafter to observe the changes in cell malignant phenotypes. The downstream molecules of circRNA CCT3 were detected. Our data suggest that circRNA CCT3 was upregulated in human BCa and was associated with poor survival outcomes of BCa patients. In cell experiments, overexpressing circRNA CCT3 promoted BCa cell malignancy, whereas silencing circRNA CCT3 did the opposite. In addition, circRNA CCT3 modulated PP2A expression by miR-135a-5p. This study demonstrates that circRNA CCT3 is a diagnostic and prognostic biomarker in BCa patients and is a tumor promoter in BCa.

CPNE1 mediates glycolysis and metastasis of breast cancer through activation of PI3K/AKT/HIF-1α signaling

CPNE1 regulates multiple signaling pathways and can stimulate cell proliferation and differentiation by activating the AKT-mTOR signaling pathway. In addition, CPNE1 is associated with various cancers; however, its role in breast cancer, particularly in TNBC, has not been fully elucidated. Our study aimed to reveal the impact of the CPNE1/PI3K/AKT/HIF-1α axis on TNBC. We first measured the expression of CPNE1 in the tumor tissues of TNBC patients and examined its prognostic value. Subsequently, we used sh-CPNE1 and overexpression vectors to transfect TNBC cell lines and analyzed cell viability, migration, and invasive abilities using colony formation and CCK-8 assays. Metabolites were analyzed through metabolomics. We found that higher expression of CPNE1 predicted poor prognosis in TNBC patients. Knockdown of CPNE1 reduced the viability, migration, invasion, and proliferation capabilities of TNBC cells. Furthermore, metabolomics analysis showed that glucose metabolism was the most dominant pathway, and knockdown of CPNE1 significantly limited the glycolytic activity of TNBC cells. We verified these conclusions in mouse models. Additionally, we overexpressed CPNE1 and treated TNBC cell lines with a PI3K inhibitor (LY294002). The results indicated that CPNE1 promoted aerobic glycolysis in TNBC cells through the PI3K/AKT/HIF-1α signaling pathway. This suggests that CPNE1 regulates cell glycolysis and participates in the development of TNBC. Our study may provide a new therapeutic target for TNBC treatment.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Gastric juice non-coding RNAs as potential biomarkers for gastric cancer

Gastric cancer (GC), being one of the most common malignant human tumors, occupies the second position in the structure of mortality in men and women. High rates of morbidity and mortality in this pathology determine its extremely high clinical and social significance. Diagnosis and timely treatment of precancerous pathology is the main way to reduce morbidity and mortality, and early detection of GC and its adequate treatment improve prognosis. The ability to accurately predict the development of GC and start treatment on time, as well as the ability to determine the stage of the disease if the diagnosis is confirmed - non-invasive biomarkers can become the key to solving these and many other problems of modern medicine. One of the promising biomarkers being studied are non-coding RNAs, namely, miсroRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). They are involved in a wide range of processes, including apoptosis, proliferation, differentiation, angiogenesis, which play a critical role in the development of GC oncogenesis. In addition, they are quite specific and stable due to their carriers (extracellular vesicles or Argonaute 2 protein) and can be detected in various human biological fluids, in particular gastric juice. Thus, miRNAs, lncRNAs, and circRNAs isolated from the gastric juice of GC patients are promising preventive, diagnostic and prognostic non-invasive biomarkers. This review article presents the characteristics of circulating or extracellular miRNAs, lncRNAs, and circRNAs in gastric juice, allowing their use in the GC preventive, diagnosis, prognosis and monitoring therapy.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

E. coli Secretome Metabolically Modulates MDA-MB-231 Breast Cancer Cells' Energy Metabolism

Breast cancer (BC) is commonly diagnosed in women. BC cells are associated with altered metabolism, which is essential to support their energetic requirements, cellular proliferation, and continuous survival. The altered metabolism of BC cells is a result of the genetic abnormalities of BC cells. Risk factors can also enhance it, including age, lifestyle, hormone disturbances, etc. Other unknown BC-promoting risk factors are under scientific investigation. One of these investigated factors is the microbiome. However, whether the breast microbiome found in the BC tissue microenvironment can impact BC cells has not been studied. We hypothesized that E. coli, part of a normal breast microbiome with more presence in BC tissue, secretes metabolic molecules that could alter BC cells' metabolism to maintain their survival. Thus, we directly examined the impact of the E. coli secretome on the metabolism of BC cells in vitro. MDA-MB-231 cells, an in vitro model of aggressive triple-negative BC cells, were treated with the E. coli secretome at different time points, followed by untargeted metabolomics analyses via liquid chromatography-mass spectrometry to identify metabolic alterations in the treated BC cell lines. MDA-MB-231 cells that were not treated were used as controls. Moreover, metabolomic analyses were performed on the E. coli secretome to profile the most significant bacterial metabolites affecting the metabolism of the treated BC cell lines. The metabolomics results revealed about 15 metabolites that potentially have indirect roles in cancer metabolism that were secreted from E. coli in the culture media of MDA-MB-231 cells. The cells treated with the E. coli secretome showed 105 dysregulated cellular metabolites compared to controls. The dysregulated cellular metabolites were involved in the metabolism of fructose and mannose, sphingolipids, amino acids, fatty acids, amino sugar, nucleotide sugar, and pyrimidine, which are vital pathways required for the pathogenesis of BC. Our findings are the first to show that the E. coli secretome modulates the BC cells' energy metabolism, highlighting insights into the possibility of altered metabolic events in BC tissue in the actual BC tissue microenvironment that are potentially induced by the local bacteria. Our study provides metabolic data that could be as a basis for future studies searching for the underlying mechanisms mediated by bacteria and their secretome to alter the metabolism of BC cells.

Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning

We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice.

Circulating metabolites in the early stage of breast cancer were not related to cancer stage or subtypes but associated with ki67 level. Promising statistical discrimination from controls

It was documented that the presence of malignancy in an organism causes metabolomic alterations in blood plasma which applies also to breast cancer. Breast cancer is a heterogeneous disease and there are only limited known relations of plasma metabolomic signatures with the tumour characteristics in early BC and knowing them would be of great advantage in noninvasive diagnostics. In this study, we focused on the metabolic alterations in early BC in blood plasma with the aim to identify metabolomic characteristics of BC subtypes. We used 50 early BC patients (FIGO stage I and II), where no additional metabolomic changes from metastatically changed remote organs were to be expected. We compared plasma levels of metabolites against controls and among various molecular and histological BC subtypes. BC patients showed decreased plasma levels of branched-chain amino acids BCAAs (and related keto-acids), histidine pyruvate and alanine balanced with an increased level of 3-hydroxybutyrate. The levels of circulating metabolites were not related to BC molecular subtypes (luminal A/luminal B), histological finding or grade, eventually stage, which indicate that in early BC, the BC patients share common metabolomics fingerprint in blood plasma independent of grade, stage or molecular subtype of BC. We observed statistically significant correlations between tumour proliferation marker Ki-67 level and circulating metabolites: alanine, citrate, tyrosine, glutamine, histidine and proline. This may point out the metabolites those levels could be associated with tumour growth, and conversely, the rate of tumour proliferation could be potentially estimated from plasma metabolites. When analyzing metabolomic changes in BC, we concluded that some of them could be associated with the metabolomic features of cancer cells, but the other observed alterations in blood plasma are the results of the complex mutual biochemical pathways in the comprehensive inter-organ metabolic exchange and communication. In the end, statistical discrimination against controls performed with AUC >0.91 showed the very promising potential of plasma metabolomics in the search for biomarkers for oncologic diseases.

Metabolomics of Breast Cancer: A Review

Breast cancer is the most commonly diagnosed cancer in women worldwide. Major advances have been made towards breast cancer prevention and treatment. Unfortunately, the incidence of breast cancer is still increasing globally. Metabolomics is the field of science which studies all the metabolites in a cell, tissue, system, or organism. Metabolomics can provide information on dynamic changes occurring during cancer development and progression. The metabolites identified using cutting-edge metabolomics techniques will result in the identification of biomarkers for the early detection, diagnosis, and treatment of cancers. This review briefly introduces the metabolic changes in cancer with particular focus on breast cancer.

Evaluation of Untargeted Metabolomic Strategy for the Discovery of Biomarker of Breast Cancer

Discovery of disease biomarker based on untargeted metabolomics is informative for pathological mechanism studies and facilitates disease early diagnosis. Numerous of metabolomic strategies emerge due to different sample properties or experimental purposes, thus, methodological evaluation before sample analysis is essential and necessary. In this study, sample preparation, data processing procedure and metabolite identification strategy were assessed aiming at the discovery of biomarker of breast cancer. First, metabolite extraction by different solvents, as well as the necessity of vacuum-dried and re-dissolution, was investigated. The extraction efficiency was assessed based on the number of eligible components (components with MS/MS data acquired), which was more reasonable for metabolite identification. In addition, a simplified data processing procedure was proposed involving the OPLS-DA, primary screening for eligible components, and secondary screening with constraints including VIP, fold change and p value. Such procedure ensured that only differential candidates were subjected to data interpretation, which greatly reduced the data volume for database search and improved analysis efficiency. Furthermore, metabolite identification and annotation confidence were enhanced by comprehensive consideration of mass and MS/MS errors, isotope similarity, fragmentation match, and biological source confirmation. On this basis, the optimized strategy was applied for the analysis of serum samples of breast cancer, according to which the discovery of differential metabolites highly encouraged the independent biomarkers/indicators used for disease diagnosis and chemotherapy evaluation clinically. Therefore, the optimized strategy simplified the process of differential metabolite exploration, which laid a foundation for biomarker discovery and studies of disease mechanism.

Metabolomics-Microbiome Crosstalk in the Breast Cancer Microenvironment

Breast cancer, the most frequent cancer diagnosed among females, is associated with a high mortality rate worldwide. Alterations in the microbiota have been linked with breast cancer development, suggesting the possibility of discovering disease biomarkers. Metabolomics has emerged as an advanced promising analytical approach for profiling metabolic features associated with breast cancer subtypes, disease progression, and response to treatment. The microenvironment compromises non-cancerous cells such as fibroblasts and influences cancer progression with apparent phenotypes. This review discusses the role of metabolomics in studying metabolic dysregulation in breast cancer caused by the effect of the tumor microenvironment on multiple cells such as immune cells, fibroblasts, adipocytes, etc. Breast tumor cells have a unique metabolic profile through the elevation of glycolysis and the tricarboxylic acid cycle metabolism. This metabolic profile is highly sensitive to microbiota activity in the breast tissue microenvironment. Metabolomics shows great potential as a tool for monitoring metabolic dysregulation in tissue and associating the findings with microbiome expression.

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.

Relationship between the n-3 index, serum metabolites and breast cancer risk

The present study aimed to investigate the relationship between the n-3 index, serum metabolites and breast cancer risk. A total of 104 newly diagnosed breast cancer patients and 70 healthy controls were recruited. The erythrocyte phospholipid fatty acid composition was determined by gas-liquid chromatography, and the n-3 index was calculated with the percentage of eicosapentaenoic acid plus docosahexaenoic acid in total fatty acids. Serum metabolomic profiles were analyzed by UHPLC-Q-Exactive Orbitrap/MS. The results showed that the erythrocyte phospholipid n-3 index was significantly lower in breast cancer patients than in healthy controls, and it was inversely associated with breast cancer risk (OR = 0.60; 95% CI: 0.36-0.84). Metabolomics analyses showed that serum 16α-hydroxy dehydroepiandrosterone (DHEA) 3-sulfate, lysophatidylethanolamines (LPE) 22:0/0:0 and hexanoylcarnitine were significantly higher, while thromboxane B₃, prostaglandin E₃ (PGE₃) and 18β-glycyrrhetinic acid were significantly lower in breast cancer patients than those in healthy controls. In addition, serum 16α-hydroxy DHEA 3-sulfate was inversely correlated with the n-3 index (r = -0.412, p = 0.036). In conclusion, our findings suggest that the lack of n-3 PUFAs might be a potential risk factor for breast cancer, and the serum metabolite 16α-hydroxy DHEA 3-sulfate may play an important role in linking n-3 PUFA deficiency and breast disease etiology.

Metabolomics in cancer research and emerging applications in clinical oncology

Cancer has myriad effects on metabolism that include both rewiring of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment, and changes in normal tissue metabolism. With the recognition that fluorodeoxyglucose-positron emission tomography imaging is an important tool for the management of many cancers, other metabolites in biological samples have been in the spotlight for cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecule metabolites that like other -omics technologies can provide critical information about the cancer state that are otherwise not apparent. Here, the authors review how cancer and cancer therapies interact with metabolism at the cellular and systemic levels. An overview of metabolomics is provided with a focus on currently available technologies and how they have been applied in the clinical and translational research setting. The authors also discuss how metabolomics could be further leveraged in the future to improve the management of patients with cancer.

Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics

Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbi et al. J. Chromatogr. B. 2019, 1105, 26-37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895-0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).

The Adipose Microenvironment Dysregulates the Mammary Myoepithelial Cells and Could Participate to the Progression of Breast Cancer

Breast cancer is the most common cancer among women worldwide. Overweight and obesity are now recognized as established risk factors for this pathology in postmenopausal women. These conditions are also believed to be responsible for higher recurrence and mortality rates. Reciprocal interactions have been described between adipose and cancer cells. An adipose microenvironment favors a greater proliferation of cancer cells, their invasion and even resistance to anti-cancer treatments. In addition, the chronic low-grade inflammation observed in obese individuals is believed to amplify these processes. Among the cell types present in the breast, myoepithelial cells (MECs), located at the interface of the epithelial cells and the stroma, are considered "tumor suppressor" cells. During the transition from ductal carcinoma in situ to invasive cancer, disorganization or even the disappearance of MECs is observed, thereby enhancing the ability of the cancer cells to migrate. As the adipose microenvironment is now considered as a central actor in the progression of breast cancer, our objective was to evaluate if it could be involved in MEC functional modifications, leading to the transition of in situ to invasive carcinoma, particularly in obese patients. Through a co-culture model, we investigated the impact of human adipose stem cells from women of normal weight and obese women, differentiated or not into mature adipocytes, on the functionality of the MECs by measuring changes in viability, apoptosis, gene, and miRNA expressions. We found that adipose cells (precursors and differentiated adipocytes) could decrease the viability of the MECs, regardless of the original BMI. The adipose cells could also disrupt the expression of the genes involved in the maintenance of the extracellular matrix and to amplify the expression of leptin and inflammatory markers. miR-122-5p and miR-132-3p could also be considered as targets for adipose cells. The metabolite analyses revealed specific profiles that may be involved in the growth of neoplastic cells. All of these perturbations could thus be responsible for the loss of tumor suppressor status of MECs and promote the transition from in situ to invasive carcinoma.

Identification of Novel Diagnostic Biomarkers in Breast Cancer Using Targeted Metabolomic Profiling

INTRODUCTION

Breast cancer (BC) is the most common cancer in women, with a high disease burden, especially in the advanced disease stages. Our study investigated the metabolomic profile of breast cancer patients' serum with the aim of identifying novel diagnostic biomarkers that could be used, especially for early disease detection.

MATERIALS AND METHODS

Using targeted metabolomic serum profiling based on high-performance liquid chromatography mass spectrometry, women with BC (n = 39) and a control group (n = 21) were examined for 232 endogenous metabolites.

RESULTS

The top performing biomarkers included acylcarnitines (ACs) and 9,12-linoleic acid. A combined panel of the top 4 biomarkers achieved 83% sensitivity and 81% specificity, with an area under the curve (AUC) of 0.839 (95% confidence interval, 0.811-0.867). Individual markers also provided significant predictive values: AC 12:0, sensitivity of 72%, specificity of 67%, and AUC of 0.71; AC 14:2, sensitivity of 74%, specificity of 71%, and AUC of 0.73; AC 14:0: sensitivity of 67%, specificity of 81%, and AUC of 0.73; and 9,12-linoleic acid, sensitivity of 69%, specificity of 67%, and AUC of 0.71. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination.

CONCLUSION

Using mass spectrometry-targeted metabolomic profiling, ACs and 9,12-linoleic acid were identified as potential diagnostic biomarkers for breast cancer. Additionally, these identified metabolites could provide additional insight into cancer cell metabolism.

The application of metabolomics in ovarian cancer management: a systematic review

Metabolomics, the global analysis of metabolites in a biological specimen, could potentially provide a fast method of biomarker identification for ovarian cancer. This systematic review aims to examine findings from studies that apply metabolomics to the diagnosis, prognosis, treatment, and recurrence of ovarian cancer. A systematic search of English language publications was conducted on PubMed, Science Direct, and SciFinder. It was augmented by a snowball strategy, whereby further relevant studies are identified from reference lists of included studies. Studies in humans with ovarian cancer which focus on metabolomics of biofluids and tumor tissue were included. No restriction was placed on the time of publication. A separate review of targeted metabolomic studies was conducted for completion. Qualitative data were summarized in a comprehensive table. The studies were assessed for quality and risk of bias using the ROBINS-I tool. 32 global studies were included in the main systematic review. Most studies applied metabolomics to diagnosing ovarian cancer, within which the most frequently reported metabolite changes were a down-regulation of phospholipids and amino acids: histidine, citrulline, alanine, and methionine. Dysregulated phospholipid metabolism was also reported in the separately reviewed 18 targeted studies. Generally, combinations of more than one significant metabolite as a panel, in different studies, achieved a higher sensitivity and specificity for diagnosis than a single metabolite; for example, combinations of different phospholipids. Widespread metabolite differences were observed in studies examining prognosis, treatment, and recurrence, and limited conclusions could be drawn. Cellular processes of proliferation and invasion may be reflected in metabolic changes present in poor prognosis and recurrence. For example, lower levels of lysine, with increased cell invasion as an underlying mechanism, or glutamine dependency of rapidly proliferating cancer cells. In conclusion, this review highlights potential metabolites and biochemical pathways which may aid the clinical care of ovarian cancer if further validated.

The Function and Mechanism of Lipid Molecules and Their Roles in The Diagnosis and Prognosis of Breast Cancer

Lipids are essential components of cell structure and play important roles in signal transduction between cells and body metabolism. With the continuous development and innovation of lipidomics technology, many studies have shown that the relationship between lipids and cancer is steadily increasing, involving cancer occurrence, proliferation, migration, and apoptosis. Breast cancer has seriously affected the safety and quality of life of human beings worldwide and has become a significant public health problem in modern society, with an especially high incidence among women. Therefore, the issue has inspired scientific researchers to study the link between lipids and breast cancer. This article reviews the research progress of lipidomics, the biological characteristics of lipid molecules, and the relationship between some lipids and cancer drug resistance. Furthermore, this work summarizes the lipid molecules related to breast cancer diagnosis and prognosis, and then it clarifies their impact on the occurrence and development of breast cancer The discussion revolves around the current research hotspot long-chain non-coding RNAs (lncRNAs), summarizes and explains their impact on tumor lipid metabolism, and provides more scientific basis for future cancer research studies.

Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer

Breast cancer (BC) contributes the highest global cancer mortality in women. BC tumors are highly heterogeneous, so subtyping by cell-surface markers is inadequate. Omics-driven tumor stratification is urgently needed to better understand BC and tailor therapies for personalized medicine. We used unsupervised k-means and partition around medoids (pam) to cluster metabolomics data from two data sets. The first comprised 271 BC tumors (data set 1) that were estrogen receptor (ER) positive (ER+, n = 204) or negative (ER-, n = 67) with 162 identified and validated metabolites. The second data set contained 67 BC samples (data set 2; ER+, n = 33; ER-, n = 34) and 352 known metabolites. Significance Analysis of Microarrays (SAM) was used to identify the most significant metabolites among these clusters, which were then reassigned into new clusters using prediction analysis of microarrays (PAM). Generally, metabolome-defined BC subtypes identified from either data set 1 or data set 2 were different from the well-known receptor- or transcriptome-defined subtypes. Metabolomics-directed clustering of data set 2 identified distinctive BC tumors characterized by metabolome profiles that associated with DNA methylation (p-value = 0.000 048, χ² test). Pathway analysis of cluster metabolites revealed that nitrogen metabolism and aminoacyl-tRNA biosynthesis were highly related to BC subtyping. The pipeline may be run from GitHub: https://github.com/FADHLyemen/Metabolomics_signature. Our proposed bioinformatics pipeline analyzed metabolomics data from BC tumors, revealing clusters characterized by unique metabolic signatures that may potentially stratify BC patients and tailor precision treatment.

Study on Urinary Candidate Metabolome for the Early Detection of Breast Cancer

A metabolomic study for determination of certain urinary metabolomes, 1-methyladenosine (1-MA), 1-methylguanosine (1-MG), and 8-hydroxy-2' deoxyguanosine (8-OHdG) in urine specimens of breast cancer patients. The accuracy of these metabolites and their combined score with cancer antigen 15-3 (CA15-3) was developed to improve the early detection of breast cancer. This study recruited 52 healthy individuals, 47 benign breast tumors, and 167 malignant breast tumor patients. Urine samples were handled to adjust the creatinine concentrations to 8 mg/dL (0.7 mmol/L) and analyzed using GC-MS to detect and quantify the selected urinary metabolomes in urine samples of all participants. The accuracy of individual urinary metabolomes and their combination with CA15-3 were evaluated using multivariate statistical analysis. The cutoff value of CA15-3 was 32.5 U/mL. Cutoff values of 1-MA, 1-MG, and 8-OHdG were 2.19, 2.1, and 7.3 µmol/mmol creatinine, respectively. The concentrations of 1-MA, 1-MG, and 8-OHdG were significantly higher in breast cancer patients, especially in the early-stage. The combination of three urinary metabolomes with CA15-3 improves the diagnostic sensitivity of breast cancer. For the combined score, the area under the curve (AUC) value of combined score ranged from 0.820 to 0.950, with high accuracy, ranged from 77.0 to 95.5%. The most significant AUC (0.973), sensitivity (90.1%), selectivity (94.0%) was recorded at comparing the healthy control with the early-stage of malignant breast cancer. In conclusion, the combination of three urinary metabolomes with serum CA15-3 improves the diagnostic sensitivity of breast cancer.

Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.

Blood Metabolites Associate with Prognosis in Endometrial Cancer

Endometrial cancer has a high prevalence among post-menopausal women in developed countries. We aimed to explore whether certain metabolic patterns could be related to the characteristics of aggressive disease and poorer survival among endometrial cancer patients in Western Norway. Patients with endometrial cancer with short survival (n = 20) were matched according to FIGO (International Federation of Gynecology and Obstetrics, 2009 criteria) stage, histology, and grade, with patients with long survival (n = 20). Plasma metabolites were measured on a multiplex system including 183 metabolites, which were subsequently determined using liquid chromatography-mass spectrometry. Partial least square discriminant analysis, together with hierarchical clustering, was used to identify patterns which distinguished short from long survival. A proposed signature of metabolites related to survival was suggested, and a multivariate receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.820–0.965 (p ≤ 0.001). Methionine sulfoxide seems to be particularly strongly associated with poor survival rates in these patients. In a subgroup with preoperative contrast-enhanced computed tomography data, selected metabolites correlated with the estimated abdominal fat distribution parameters. Metabolic signatures may predict prognosis and be promising supplements when evaluating phenotypes and exploring metabolic pathways related to the progression of endometrial cancer. In the future, this may serve as a useful tool in cancer management.

Plasma metabolites as possible biomarkers for diagnosis of breast cancer

Metabolomic approaches have been used to identify new diagnostic biomarkers for various types of cancers, including breast cancer. In this study, we aimed to identify potential biomarkers of breast cancer using plasma metabolic profiling. Furthermore, we analyzed whether these biomarkers had relationships with clinicopathological characteristics of breast cancer. Our study used two liquid chromatography-mass spectrometry sets: a discovery set (40 breast cancer patients and 30 healthy controls) and a validation set (30 breast cancer patients and 16 healthy controls). All breast cancer patients were randomly selected from among stage I–III patients who underwent surgery between 2011 and 2016. First, metabolites distinguishing cancer patients from healthy controls were identified in the discovery set. Then, consistent and reproducible metabolites were evaluated in terms of their utility as possible biomarkers of breast cancer. Receiver operating characteristic (ROC) analysis was applied to the discovery set, and ROC cut-off values for the identified metabolites derived therein were applied to the validation set to determine their diagnostic performance. Ultimately, four candidate biomarkers (L-octanoylcarnitine, 5-oxoproline, hypoxanthine, and docosahexaenoic acid) were identified. L-octanoylcarnitine showed the best diagnostic performance, with a 100.0% positive predictive value. Also, L-octanoylcarnitine levels differed according to tumor size and hormone receptor expression. The plasma metabolites identified in this study show potential as biomarkers allowing early diagnosis of breast cancer. However, the diagnostic performance of the metabolites needs to be confirmed in further studies with larger sample sizes.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

Development of a gas chromatography-mass spectrometry method for breast cancer diagnosis based on nucleoside metabolomes 1-methyl adenosine, 1-methylguanosine and 8-hydroxy-2'-deoxyguanosine

Metabolomes are small molecule metabolites (<1000 Da) produced by cellular processes. Metabolomes are close counterparts to the genome, transcriptome and proteome. The aim of this study was to develop a method to detect and quantify candidate nucleoside metabolomes 1-methyl adenosine (1-MA), 1-methylguanosine (1-MG) and 8-hydroxy-2'-deoxyguanosine (8-OHdG) in the urine of patients with breast cancer using gas chromatography-mass spectrometry (GC-MS). The method was applied to urine specimens from patients with breast cancer (n = 56) and benign breast tumors (n = 22), as well as from healthy females (n = 20). The relative standard deviations of precision and repeatability analysis were <10%, and recoveries ranged from 88.5 to 105.6%. Limits of detection were 0.014, 0.012, and 0.018 mg/L for 1-MA, 1-MG and 8-OHdG, respectively. The lower limits of quantitation were 0.056, 0.048 and 0.072 mg/L, respectively. There were significant differences in concentrations of candidate metabolomes between patients with cancer and the healthy individuals, especially for those in the early stages of the disease (p < 0.001). No significant differences were observed between the benign and healthy groups. In conclusion, a reliable GC-MS method for the detection and quantification of 1-MA, 1-MG, and 8-OHdG metabolomes in urine has been developed.

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.

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.

The decrease of some serum free amino acids can predict breast cancer diagnosis and progression

This study was targeted on a metabolomic approach to compare the blood serum free amino acid profiles and concentration of confirmed breast cancer (stages I-III) patients to healthy controls in order to establish reliable biomarkers of early detection and prediction of breast cancer. The ultra-high-performance liquid chromatography coupled with mass spectrometry using positive ionization electrospray was applied for the picoline-derivatized serum free amino acids using the EZ:faastTM kit. Multivariate statistical analysis principal component analysis, partial least squares discrimination analysis and univariate analysis were applied in order to discriminate between patient groups and putative amino acid biomarkers for breast cancer. A significant decrease of amino acid concentrations between the breast cancer group and the control group was positively correlated with breast cancer progression. Arginine, Alanine, Isoleucine, Tyrosine and Tryptophan were identified as being good potential discriminants (AUROC ≥0.85) and suitable candidates to diagnose and predict the breast cancer progression.

NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer

Background

Combination of metabolomics and epidemiological approaches opens new perspectives for ground-breaking discoveries. The aim of the present study was to investigate for the first time whether plasma untargeted metabolomic profiles, established from a simple blood draw from healthy women, could contribute to predict the risk of developing breast cancer within the following decade and to better understand the aetiology of this complex disease.

Methods

A prospective nested case-control study was set up in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, including 206 breast cancer cases diagnosed during a 13-year follow-up and 396 matched controls. Untargeted nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples. Multivariable conditional logistic regression models were computed for each individual NMR variable and for combinations of variables derived by principal component analysis.

Results

Several metabolomic variables from 1D NMR spectroscopy were associated with breast cancer risk. Women characterized by higher fasting plasma levels of valine, lysine, arginine, glutamine, creatine, creatinine and glucose, and lower plasma levels of lipoproteins, lipids, glycoproteins, acetone, glycerol-derived compounds and unsaturated lipids had a higher risk of developing breast cancer. P-values ranged from 0.00007 [odds ratio (OR)T3vsT1=0.37 (0.23-0.61) for glycerol-derived compounds] to 0.04 [ORT3vsT1=1.61 (1.02-2.55) for glutamine].

Conclusion

This study highlighted associations between baseline NMR plasma metabolomic signatures and long-term breast cancer risk. These results provide interesting insights to better understand complex mechanisms involved in breast carcinogenesis and evoke plasma metabolic disorders favourable for carcinogenesis initiation. This study may contribute to develop screening strategies for the identification of at-risk women for breast cancer well before symptoms appear.

A plasma metabolite panel as biomarkers for early primary breast cancer detection

In recent years, metabolites have attracted substantial attention as promising novel biomarkers of various diseases. However, breast cancer plasma metabolite studies are still in their infancy. Here, we investigated the potential of metabolites to serve as minimally invasive, early detection markers of primary breast cancer. We profiled metabolites extracted from the plasma of primary breast cancer patients and healthy controls using tandem mass spectrometry (UHPLC-MS/MS and FIA-MS/MS). Two metabolites were found to be upregulated, while 16 metabolites were downregulated in primary breast cancer patients compared to healthy controls in both the training and validation cohorts. A panel of seven metabolites was selected by LASSO regression analysis. This panel could differentiate primary breast cancer patients from healthy controls, with an AUC of 0.87 (95% CI: 0.81 ~ 0.92) in the training cohort and an AUC of 0.80 (95% CI: 0.71 ~ 0.87) in the validation cohort. These significantly differentiated metabolites are mainly involved in the amino acid metabolism and breast cancer cell growth pathways. In conclusion, using a metabolomics approach, we identified metabolites that have potential value for development of a multimarker blood-based test to complement and improve early breast cancer detection. The panel identified herein might be part of a prescreening tool, especially for younger women or for closely observing women with certain risks, to facilitate decision making regarding which individuals should undergo further diagnostic tests. In the future, the combination of metabolites and other blood-based molecular marker sets, such as DNA methylation, microRNA, and cell-free DNA mutation markers, will be an attractive option.

Alterations in arginine and energy metabolism, structural and signalling lipids in metastatic breast cancer in mice detected in plasma by targeted metabolomics and lipidomics

Background

The early detection of metastasis based on biomarkers in plasma may improve cancer prognosis and guide treatment. The aim of this work was to characterize alterations in metabolites of the arginine pathway, energy metabolism, and structural and signalling lipids in plasma in the early and late stages of murine breast cancer metastasis.

Methods

Mice were orthotopically inoculated with 4T1 metastatic breast cancer cells, and plasma was analysed along the pulmonary metastasis progression using LC-MS/MS-based targeted metabolomics and lipidomics.

Results

Based on primary tumour growth and pulmonary metastases, 1–2 weeks after 4T1 cancer cell inoculation was defined as an early metastatic stage, and 3–4 weeks after 4T1 cancer cell inoculation was defined as a late metastatic stage. Early metastasis was featured in plasma by a shift of L-arginine metabolism towards arginase (increased ornithine/arginine ratio) and polyamine synthesis (increased putrescine). Late metastasis was reflected in plasma by further progression of changes in the arginine pathway with an additional increase in asymmetric dimethylarginine plasma concentration, as well as by a profound energy metabolism reprogramming towards glycolysis, an accelerated pentose phosphate pathway and a concomitant decrease in tricarboxylic cycle rate (“Warburg effect”). The late but not the early phase of metastasis was also characterized by a different lipid profile pattern in plasma, including a decrease in total phosphatidylcholines, a decrease in diester-bound phospholipid fraction and an increase in lysophospholipids associated with an increase in total sphingomyelins.

Conclusions

The early phase of metastasis in murine 4T1 metastatic breast cancer was associated with plasma metabolome changes characteristic of arginase activation and polyamine synthesis. The late metastasis was reflected in plasma not only by the alterations in arginine pathways but also by a shift towards glycolysis and the pentose pathway, remodelling of structural lipids and activation of lipid signalling, all of which coincided with metastasis progression.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1075-y) contains supplementary material, which is available to authorized users.

Plasma metabolomic profiles differ at the time of artificial insemination based on pregnancy outcome, in Bos taurus beef heifers

Infertility remains the most prevalent reason for cattle being removed from production environments. We utilized metabolomic profiling to identify metabolites in the blood plasma that may be useful in identifying infertile heifers at the time of artificial insemination (AI). Prior to AI, phenotypic parameters including body condition, weight, and reproductive organ measurements were collected. These were determined not effective at differentiating between fertile and infertile heifers. Analysis of the resulting metabolomic profiles revealed 15 metabolites at significantly different levels (T-test P ≤ 0.05), with seven metabolites having a greater than 2-fold difference (T-test P ≤ 0.05, fold change ≥2, ROC-AUC ≥ 0.80) between infertile and fertile heifers. We further characterized the utility of using the levels of these metabolites in the blood plasma to discriminate between fertile and infertile heifers. Finally, we investigated the potential role inflammation may play by comparing the expression of inflammatory cytokines in the white blood cells of infertile heifers to that of fertile heifers. We found significantly higher expression in infertile heifers of the proinflammatory markers tumor necrosis factor alpha (TNFα), interleukin 6 (IL6), and the C-X-C motif chemokine 5 (CXCL5). Our work offers potentially valuable information regarding the diagnosis of fertility problems in heifers undergoing AI.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

A characteristic biosignature for discrimination of gastric cancer from healthy population by high throughput GC-MS analysis

Early diagnosis of gastric cancer is crucial to improve patient′ outcome. A good biomarker will function in early diagnosis for gastric cancer. In order to find practical and cost-effective biomarkers, we used gas chromatography combined mass spectrometer (GC-MS) to profile urinary metabolites on 293 urine samples. Ninety-four samples are taken as training set, others for validating study. Orthogonal partial least squares discriminant analysis (OPLS-DA), significance analysis of microarray (SAM) and Mann-Whitney U test are used for data analysis. The diagnostic value of urinary metabolites was evaluated by ROC curve. As results, Seventeen metabolites are significantly different between patients and healthy controls in training set. Among them, 14 metabolites show diagnostic value better than classic blood biomarkers by quantitative assay on validation set. Ten of them are amino acids and four are organic metabolites. Importantly, proline, p-cresol and 4-hydroxybenzoic acid disclose outcome-prediction value by means of survival analysis. Therefore, the examination of urinary metabolites is a promising noninvasive strategy for gastric cancer screening.

Multiplatform plasma metabolic and lipid fingerprinting of breast cancer: A pilot control-case study in Colombian Hispanic women

Breast cancer (BC) is a highly heterogeneous disease associated with metabolic reprogramming. The shifts in the metabolome caused by BC still lack data from Latin populations of Hispanic origin. In this pilot study, metabolomic and lipidomic approaches were performed to establish a plasma metabolic fingerprint of Colombian Hispanic women with BC. Data from 1H-NMR, GC-MS and LC-MS were combined and compared. Statistics showed discrimination between breast cancer and healthy subjects on all analytical platforms. The differentiating metabolites were involved in glycerolipid, glycerophospholipid, amino acid and fatty acid metabolism. This study demonstrates the usefulness of multiplatform approaches in metabolic/lipid fingerprinting studies to broaden the outlook of possible shifts in metabolism. Our findings propose relevant plasma metabolites that could contribute to a better understanding of underlying metabolic shifts driven by BC in women of Colombian Hispanic origin. Particularly, the understanding of the up-regulation of long chain fatty acyl carnitines and the down-regulation of cyclic phosphatidic acid (cPA). In addition, the mapped metabolic signatures in breast cancer were similar but not identical to those reported for non-Hispanic women, despite racial differences.

On metabolic reprogramming and tumor biology: A comprehensive survey of metabolism in breast cancer

Altered metabolism in tumor cells has been a focus of cancer research for as long as a century but has remained controversial and vague due to an inhomogeneous overall picture. Accumulating genomic, metabolomic, and lastly panomic data as well as bioenergetics studies of the past few years enable a more comprehensive, systems-biologic approach promoting deeper insight into tumor biology and challenging hitherto existing models of cancer bioenergetics. Presenting a compendium on breast cancer-specific metabolome analyses performed thus far, we review and compile currently known aspects of breast cancer biology into a comprehensive network, elucidating previously dissonant issues of cancer metabolism. As such, some of the aspects critically discussed in this review include the dynamic interplay or metabolic coupling between cancer (stem) cells and cancer-associated fibroblasts, the intratumoral and intertumoral heterogeneity and plasticity of cancer cell metabolism, the existence of distinct metabolic tumor compartments in need of separate yet simultaneous therapeutic targeting, the reliance of cancer cells on oxidative metabolism and mitochondrial power, and the role of pro-inflammatory, pro-tumorigenic stromal conditioning. Comprising complex breast cancer signaling networks as well as combined metabolomic and genomic data, we address metabolic consequences of mutations in tumor suppressor genes and evaluate their contribution to breast cancer predisposition in a germline setting, reasoning for distinct personalized preventive and therapeutic measures. The review closes with a discussion on central root mechanisms of tumor cell metabolism and rate-limiting steps thereof, introducing essential strategies for therapeutic targeting.

Metabolic Footprints and Molecular Subtypes in Breast Cancer

Cancer treatment options are increasing. However, even among the same tumor histotype, interpatient tumor heterogeneity should be considered for best therapeutic result. Metabolomics represents the last addition to promising “omic” sciences such as genomics, transcriptomics, and proteomics. Biochemical transformation processes underlying energy production and biosynthetic processes have been recognized as a hallmark of the cancer cell and hold a promise to build a bridge between genotype and phenotype. Since breast tumors represent a collection of different diseases, understanding metabolic differences between molecular subtypes offers a way to identify new subtype-specific treatment strategies, especially if metabolite changes are evaluated in the broader context of the network of enzymatic reactions and pathways. Here, after a brief overview of the literature, original metabolomics data in a series of 92 primary breast cancer patients undergoing surgery at the Istituto Nazionale dei Tumori of Milano are reported highlighting a series of metabolic differences across various molecular subtypes. In particular, the difficult-to-treat luminal B subgroup represents a tumor type which preferentially relies on fatty acids for energy, whereas HER2 and basal-like ones show prevalently alterations in glucose/glutamine metabolism.

Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges

The metabolome, although very dynamic, is sufficiently stable to provide specific quantitative traits related to health and disease. Metabolomics requires balanced use of state-of-the-art study design, chemical analytics, biostatistics, and bioinformatics to deliver meaningful answers to contemporary questions in human disease research. The technology is now frequently employed for biomarker discovery and for elucidating the mechanisms underlying endocrine-related diseases. Metabolomics has also enriched genome-wide association studies (GWAS) in this area by providing functional data. The contributions of rare genetic variants to metabolome variance and to the human phenotype have been underestimated until now.

Does the 1H-NMR plasma metabolome reflect the host-tumor interactions in human breast cancer?

Breast cancer (BC) is the most common diagnosed cancer and the leading cause of cancer death in women worldwide. There is an obvious need for a better understanding of BC biology. Alterations in the serum metabolome of BC patients have been identified but their clinical significance remains elusive. We evaluated by 1H-Nuclear Magnetic Resonance (1H-NMR) spectroscopy, filtered plasma metabolome of 50 early (EBC) and 15 metastatic BC (MBC) patients. Using Principal Component Analysis, Partial Least-Squares Discriminant Analysis and Hierarchical Clustering we show that plasma levels of glucose, lactate, pyruvate, alanine, leucine, isoleucine, glutamate, glutamine, valine, lysine, glycine, threonine, tyrosine, phenylalanine, acetate, acetoacetate, β-hydroxy-butyrate, urea, creatine and creatinine are modulated across patients clusters. In particular lactate levels are inversely correlated with the tumor size in the EBC cohort (Pearson correlation r = -0.309; p = 0.044). We suggest that, in BC patients, tumor cells could induce modulation of the whole patient's metabolism even at early stages. If confirmed in a lager study these observations could be of clinical importance.