Metabolomic alterations in invasive ductal carcinoma of breast: A comprehensive metabolomic study using tissue and serum samples

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.

Metabolic reprogramming and therapeutic resistance in primary and metastatic breast cancer

Metabolic alterations, a hallmark of cancer, enable tumor cells to adapt to their environment by modulating glucose, lipid, and amino acid metabolism, which fuels rapid growth and contributes to treatment resistance. In primary breast cancer, metabolic shifts such as the Warburg effect and enhanced lipid synthesis are closely linked to chemotherapy failure. Similarly, metastatic lesions often display distinct metabolic profiles that not only sustain tumor growth but also confer resistance to targeted therapies and immunotherapies. The review emphasizes two major aspects: the mechanisms driving metabolic resistance in both primary and metastatic breast cancer, and how the unique metabolic environments in metastatic sites further complicate treatment. By targeting distinct metabolic vulnerabilities at both the primary and metastatic stages, new strategies could improve the efficacy of existing therapies and provide better outcomes for breast cancer patients.

Serum metabolite and metal ions profiles for breast cancer screening

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types-DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients' metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.

Assessing the Metabolic Variations of Invasive and Noninvasive Unilateral Retinoblastoma Patients

Retinoblastoma (Rb) is a pediatric eye cancer which if diagnosed at later stages can lead to Rb invasion into the choroid, optic nerve, sclera, or beyond, with the potential of undergoing metastasis. Cancer cells, including Rb cells, reprogram their metabolic circuits for their own survival and progression, which provides a great opportunity to monitor the extent of Rb progression based on metabolic differences. Henceforth, the present study aims to map the metabolic variations in patients with invasive (primarily enucleated eyes with high-risk histopathological features) and noninvasive (eyes salvaged with treatment) unilateral retinoblastoma (Rb) using nuclear magnetic resonance (NMR) based serum metabolomics. Quantification of differential metabolites in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb along with 6 controls (no retinal pathology) was carried out using 1H NMR spectroscopy. A total of 71 metabolites, such as organic acids, amino acids, carbohydrates, and others, were identified in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb. Partial least-squares discriminant analysis (PLS-DA) models depicted distinct grouping of invasive and noninvasive Rb patients and controls. Differential metabolic fingerprints were observed for invasive and noninvasive Rb patients based on their biostatistical analyses with respect to controls. Remarkable perturbation was observed among various metabolites such as 4-aminobutyrate, 2-phosphoglycerate, O-phosphocholine, proline, Sn-glycero-3-phosphocholine (Sn-GPC), and O-phosphoethanolamine in noninvasive and invasive Rb patients with most of the effects being heightened in the latter group. Metabolic changes unique to invasive and noninvasive Rb patients were also observed. Multivariate receiver operating characteristics (ROC) analysis unveiled the highest accuracy and potency of ROC models 2 and 5 to distinguish the noninvasive and invasive Rb from controls, respectively. Metabolites identified in the serum of patients with invasive and noninvasive Rb may aid in advancing our knowledge about Rb tumor biology. Differential aberrant metabolic variations in patients with invasive Rb compared to those with noninvasive Rb may guide the decision of enucleation versus globe salvage.

The potential of aryl hydrocarbon receptor as receptors for metabolic changes in tumors

Cancer cells can alter their metabolism to meet energy and molecular requirements due to unfavorable environments with oxygen and nutritional deficiencies. Therefore, metabolic reprogramming is common in a tumor microenvironment (TME). Aryl hydrocarbon receptor (AhR) is a ligand-activated nuclear transcription factor, which can be activated by many exogenous and endogenous ligands. Multiple AhR ligands can be produced by both TME and tumor cells. By attaching to various ligands, AhR regulates cancer metabolic reprogramming by dysregulating various metabolic pathways, including glycolysis, lipid metabolism, and nucleotide metabolism. These regulated pathways greatly contribute to cancer cell growth, metastasis, and evading cancer therapies; however, the underlying mechanisms remain unclear. Herein, we review the relationship between TME and metabolism and describe the important role of AhR in cancer regulation. We also focus on recent findings to discuss the idea that AhR acts as a receptor for metabolic changes in tumors, which may provide new perspectives on the direction of AhR research in tumor metabolic reprogramming and future therapeutic interventions.

LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer

BACKGROUND

Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach.

METHODS

In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30). The multivariate data analysis, determination of the fold change and the Mann-Whitney U test were used to screen out the differential metabolites. Additionally, machine learning methods including receiver operating curve analysis and logistic regression analysis were conducted to establish diagnostic biomarker panels.

RESULTS

There were 36 metabolites found to be significantly different between BC and non-BC groups, and 12 metabolites discovered to be significantly different between TNBC and non-TNBC patients. Results also showed that four metabolites, including N-acetyl-D-tryptophan, 2-arachidonoylglycerol, pipecolic acid and oxoglutaric acid, were considered as vital biomarkers for the diagnosis of BC and non-BC with an area under the curve (AUC) of 0.995. Another two-metabolite panel of N-acetyl-D-tryptophan and 2-arachidonoylglycerol was discovered to discriminate TNBC from non-TNBC and produced an AUC of 0.965.

CONCLUSION

This study demonstrated that serum metabolomics can be used to identify BC specifically and identified promising serum metabolic markers for TNBC diagnosis.

The extracellular matrix supports breast cancer cell growth under amino acid starvation by promoting tyrosine catabolism

Breast tumours are embedded in a collagen I-rich extracellular matrix (ECM) network, where nutrients are scarce due to limited blood flow and elevated tumour growth. Metabolic adaptation is required for cancer cells to endure these conditions. Here, we demonstrated that the presence of ECM supported the growth of invasive breast cancer cells, but not non-transformed mammary epithelial cells, under amino acid starvation, through a mechanism that required macropinocytosis-dependent ECM uptake. Importantly, we showed that this behaviour was acquired during carcinoma progression. ECM internalisation, followed by lysosomal degradation, contributed to the up-regulation of the intracellular levels of several amino acids, most notably tyrosine and phenylalanine. This resulted in elevated tyrosine catabolism on ECM under starvation, leading to increased fumarate levels, potentially feeding into the tricarboxylic acid (TCA) cycle. Interestingly, this pathway was required for ECM-dependent cell growth and invasive cell migration under amino acid starvation, as the knockdown of p-hydroxyphenylpyruvate hydroxylase-like protein (HPDL), the third enzyme of the pathway, opposed cell growth and motility on ECM in both 2D and 3D systems, without affecting cell proliferation on plastic. Finally, high HPDL expression correlated with poor prognosis in breast cancer patients. Collectively, our results highlight that the ECM in the tumour microenvironment (TME) represents an alternative source of nutrients to support cancer cell growth by regulating phenylalanine and tyrosine metabolism.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

BACKGROUND

Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.

METHODS

A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).

RESULTS

Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.

CONCLUSIONS

The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review

This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.

The diagnostic significance of circulating miRNAs and metabolite profiling in early prediction of breast cancer in Egyptian women

OBJECTIVE

Breast cancer (BC) is one of the most commonly diagnosed solid malignancies in women worldwide.

PURPOSE

Finding new non-invasive circulating diagnostic biomarkers will facilitate the early prediction of BC and provide valuable insight into disease progression and response to therapy using a safe and more accessible approach available every inspection time. Therefore, our present study aimed to investigate expression patterns of potentially circulating biomarkers that can differentiate well between benign, malignant, and healthy subjects.

METHODS

To achieve our target, quantitative analyses were performed for some circulating biomarkers which have a role in the proliferation and tumor growth, as well as, glutamic acid, and human epidermal growth receptor 2 (HER2) in blood samples of BC patients in comparison to healthy controls using qRT-PCR, liquid chromatography/mass spectrometry (LC/MS/MS), and ELISA.

RESULTS

Our findings showed that the two miRNAs (miRNA-145, miRNA-382) were expressed at lower levels in BC sera than healthy control group, while miRNA-21 was expressed at higher levels in BC patients than control subjects. Area under ROC curves of BC samples revealed that AUC of miRNA-145, miRNA-382, miRNA-21, and glutamic acid was evaluated to equal 0.99, 1.00, 1.00 and 1.00, respectively. Besides, there was a significantly positive correlation between miRNA-145 and miRNA-382 (r = 0.737), and a highly significant positive correlation between miRNA-21 and glutamic acid (r = 0.385).

CONCLUSION

Based on our results, we conclude that the detection of serum miRNA-145, -382 and -21 as a panel along with glutamic acid, and circulating HER2 concentrations could be useful as a non-invasive diagnostic profiling for early prediction of breast cancer in Egyptian patients. It can provide an insight into disease progression, discriminate between malignancy and healthy control, and overcome the use limitations (low sensitivity and specificity, repeated risky exposure, and high cost) of other detecting tools, including mammography, magnetic resonance imaging, and ultrasound.

G6PD activation in TNBC cells induces macrophage recruitment and M2 polarization to promote tumor progression

Glucose-6-phosphate dehydrogenase (G6PD) is involved in triple-negative breast cancer (TNBC) progression. Metabolic crosstalk between cancer cells and tumor-associated macrophages mediates tumor progression in TNBC. Molecular biological methods were applied to clarify the mechanism of the crosstalk between TNBC cells and M2 macrophages. In the present study, we verified that G6PD overexpression drives M2 macrophage polarization by directly combining with phospho-STAT1 and upregulating CCL2 and TGF-β1 secretion in TNBC cells. In turn, M2-like TAMs activated TNBC cells through IL-10 secretion, providing feedback to upregulate G6PD and promote TNBC cell migration and proliferation in vitro. Furthermore, we found that 6-AN (a specific inhibitor of G6PD) not only suppressed the cancer-driven polarization of macrophages toward the M2 phenotype but also inhibited the inherent M2 polarization of macrophages. Targeting the G6PD-regulated pentose phosphate pathway restrained TNBC progression and M2-type polarization of macrophages in vitro and in vivo.

Proteomics-Based Identification of Dysregulated Proteins and Biomarker Discovery in Invasive Ductal Carcinoma, the Most Common Breast Cancer Subtype

Invasive ductal carcinoma (IDC) is the most common histological subtype of malignant breast cancer (BC), and accounts for 70-80% of all invasive BCs. IDC demonstrates great heterogeneity in clinical and histopathological characteristics, prognoses, treatment strategies, gene expressions, and proteomic profiles. Significant proteomic determinants of the progression from intraductal pre-invasive malignant lesions of the breast, which characterize a ductal carcinoma in situ (DCIS), to IDC, are still poorly identified, validated, and clinically applied. In the era of "6P" medicine, it remains a great challenge to determine which patients should be over-treated versus which need to be actively monitored without aggressive treatment. The major difficulties for designating DCIS to IDC progression may be solved by understanding the integrated genomic, transcriptomic, and proteomic bases of invasion. In this review, we showed that multiple proteomics-based techniques, such as LC-MS/MS, MALDI-ToF MS, SELDI-ToF-MS, MALDI-ToF/ToF MS, MALDI-MSI or MasSpec Pen, applied to in-tissue, off-tissue, BC cell lines and liquid biopsies, improve the diagnosis of IDC, as well as its prognosis and treatment monitoring. Classic proteomics strategies that allow the identification of dysregulated protein expressions, biological processes, and interrelated pathway analyses based on aberrant protein-protein interaction (PPI) networks have been improved to perform non-invasive/minimally invasive biomarker detection of early-stage IDC. Thus, in modern surgical oncology, highly sensitive, rapid, and accurate MS-based detection has been coupled with "proteome point sampling" methods that allow for proteomic profiling by in vivo "proteome point characterization", or by minimal tissue removal, for ex vivo accurate differentiation and delimitation of IDC. For the detection of low-molecular-weight proteins and protein fragments in bodily fluids, LC-MS/MS and MALDI-MS techniques may be coupled to enrich and capture methods which allow for the identification of early-stage IDC protein biomarkers that were previously invisible for MS-based techniques. Moreover, the detection and characterization of protein isoforms, including posttranslational modifications of proteins (PTMs), is also essential to emphasize specific molecular mechanisms, and to assure the early-stage detection of IDC of the breast.

Comparison of phenomics and cfDNA in a large breast screening population: the Breast Screening and Monitoring Study (BSMS)

To assess their roles in breast cancer diagnostics, we aimed to compare plasma cell-free DNA (cfDNA) levels with the circulating metabolome in a large breast screening cohort of women recalled for mammography, including healthy women and women with mammographically detected breast diseases, ductal carcinoma in situ and invasive breast cancer: the Breast Screening and Monitoring Study (BSMS). In 999 women, plasma was analyzed by nuclear magnetic resonance (NMR) and Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) and then processed to isolate and quantify total cfDNA. NMR and UPLC-MS results were compared with data for 186 healthy women derived from the AIRWAVE cohort. Results showed no significant differences between groups for all metabolites, whereas invasive cancers had significantly higher plasma cfDNA levels than all other groups. When stratified the supervised OPLS-DA analysis and total cfDNA concentration showed high discrimination accuracy between invasive cancers and the disease/medication-free subjects. Furthermore, comparison of OPLS-DA data for invasive breast cancers with the AIRWAVE cohort showed similar discrimination between breast cancers and healthy controls. This is the first report of agreement between metabolomics and plasma cfDNA levels for discriminating breast cancer from healthy subjects in a true screening population. It also emphasizes the importance of sample standardization. Follow on studies will involve analysis of candidate features in a larger validation series as well as comparing results with serial plasma samples taken at the next routine screening mammography appointment. The findings here help establish the role of plasma analysis in the diagnosis of breast cancer in a large real-world cohort.

Metabolomic study of serum in patients with invasive ductal breast carcinoma with LC-MS/MS approach

BACKGROUND

Invasive ductal carcinoma (IDC) is the most common type of breast cancer so its early detection can lead to a significant decrease in mortality rate. However, prognostic factors for IDC are not adequate and we need novel markers for the treatment of different individuals. Although positron emission tomography and magnetic resonance imaging techniques are available, they are based on morphological features that do not provide any clue for molecular events accompanying cancer progression. In recent years, "omics" approaches have been extensively developed to propose novel molecular signatures of cancers as putative biomarkers, especially in biofluids. Therefore, a mass spectrometry-based metabolomics investigation was performed to find some putative metabolite markers of IDC and potential metabolites with prognostic value related to the estrogen receptor, progesterone receptor, lymphovascular invasion, and human epidermal growth factor receptor 2.

METHODS

An untargeted metabolomics study of IDC patients was performed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The multivariate principal component analysis by XCMS online built a model that could separate the study groups and define the significantly altered m/z parameters. The most important biological pathways were also identified by pathway enrichment analysis.

RESULTS

The results showed that the significantly altered metabolites in IDC serum samples mostly belonged to amino acids and lipids. The most important involved pathways included arginine and proline metabolism, glycerophospholipid metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis.

CONCLUSIONS

Significantly altered metabolites in IDC serum samples compared to healthy controls could lead to the development of metabolite-based potential biomarkers after confirmation with other methods and in large cohorts.

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

Liquid biopsy markers for early diagnosis of brain metastasis patients with breast cancer by metabolomics

Introduction: Breast cancer is the most common cancer in women and is the second most common cause of cancer related mortality. Metabolomics, the identification of small metabolites, is a technique for determining the amount of these metabolites. Objectives: This study aimed to identify markers for the early diagnosis of brain metastasis by metabolomic methods in breast cancer patients. Methods: A total of 88 breast cancer patients with distant metastases were included in the study. The patients were divided into two groups according to their metastasis status: patients with brain metastases and distant metastases without any brain metastases. Liquid chromatography quadrupole time-of-flight mass spectrometry (LC-qTOF-MS) and gas chromatography-mass spectrometry (GC-MS) analysis methods were used for metabolomic analyses. Results: 33 of them, 88 patients had brain metastasis, and 55 patients had distant metastases without brain metastasis. A total of 72 and 35 metabolites were identified by the GC-MS and LC-qTOF-MS analysis, respectively. 47 of them were found to be significantly different in patients with brain metastasis. The pathway analysis, performed with significantly altered metabolites, showed that aminoacyl tRNA biosynthesis, valine, leucine and isoleucine biosynthesis, alanine, aspartate, and glutamate metabolism, arginine biosynthesis, glycine, serine, and threonine metabolism pathways significantly altered in patients with brain metastasis. Predictive accuracies for have identifying the brain metastasis were performed with receiver operating characteristic (ROC) analysis, and the model with fifteen metabolites has 96.9% accuracy. Conclusions: While these results should be supported by prospective studies, these data are promising for early detection of brain metastasis with markers in liquid biopsy samples.

Metabolic Reprogramming and Cell Adhesion in Acute Leukemia Adaptation to the CNS Niche

Involvement of the Central Nervous System (CNS) in acute leukemia confers poor prognosis and lower overall survival. Existing CNS-directed therapies are associated with a significant risk of short- or long-term toxicities. Leukemic cells can metabolically adapt and survive in the microenvironment of the CNS. The supporting role of the CNS microenvironment in leukemia progression and dissemination has not received sufficient attention. Understanding the mechanism by which leukemic cells survive in the nutrient-poor and oxygen-deprived CNS microenvironment will lead to the development of more specific and less toxic therapies. Here, we review the current literature regarding the roles of metabolic reprogramming in leukemic cell adhesion and survival in the CNS.

MR spectroscopy in breast cancer metabolomics

Breast cancer poses a significant health care challenge worldwide requiring early detection and effective treatment strategies for better patient outcome. A deeper understanding of the breast cancer biology and metabolism may help developing better diagnostic and therapeutic approaches. Metabolomic studies give a comprehensive analysis of small molecule metabolites present in human tissues in vivo. The changes in the level of these metabolites provide information on the complex mechanism of the development of the disease and its progression. Metabolomic approach using analytical techniques such as magnetic resonance spectroscopy (MRS) has evolved as an important tool for identifying clinically relevant metabolic biomarkers. The metabolic characterization of breast lesions using in-vivo MRS has shown that malignant breast tissues contain elevated levels of choline containing compounds (tCho), suggesting rapid proliferation of cancer cells and alterations in membrane metabolism. Also, tCho has been identified as one of the important biomarkers that help to enhance the diagnostic accuracy of dynamic contrast enhanced magnetic resonance imaging and also for monitoring treatment response. Further, metabolome of malignant tissues can be studied using ex vivo and in vitro MRS at high magnetic fields. This provided the advantage of detection of a large number of compounds that facilitated more comprehensive insight into the altered metabolic pathways associated with the cancer development and progression and also in identification of several metabolites as potential biomarkers. This article briefly reviews the role of MRS based metabolic profiling in the discovery of biomarkers and understanding of the altered metabolism in breast cancer.

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.

Expression of Autophagy and Mitophagy Markers in Breast Cancer Tissues

Mitochondria play important roles in regulating cell bioenergetics status and reactive oxygen species (ROS) generation. ROS-induced mitochondrial damage is among the main intracellular signal inducers of autophagy. Autophagy is a cellular catabolic process that regulates protein and organelle turnover, while a selective form of autophagy, mitophagy, specifically targets dysfunctional mitochondrial degradation. This study aims to measure the levels of autophagy, mitophagy, oxidative stress, and apoptosis in invasive breast carcinoma tissues using immunohistochemistry (IHC). Tissue microarrays of 76 patients with breast cancer were stained with six IHC markers (MnSOD, Beclin-1, LC3, BNIP3, Parkin, and cleaved caspase 3). The expression intensity was determined for each tumor tissue and the adjacent tumor-matched control tissues. Intermediate and strong staining scores of MnSOD, Beclin-1, LC-3, BNIP-3, and Parkin were significantly higher in tumor tissues compared to the adjacent matched control. The scoring intensity was further classified into tissues with negative staining and positive staining, which showed that positive scores of Beclin-1 and Parkin were significantly high in tumor tissues compared to other markers. Positive association was also noted between BNIP-3 and Beclin-1 as well as LC-3 and cleaved caspase-3 immunostaining. To our knowledge, this is one of the first studies that measure both mitophagy and autophagy in the same breast cancer tissues and the adjacent matched control. The findings from this study will be of great potential in identifying new cancer biomarkers and inspire significant interest in applying anti-autophagy therapies as a possible treatment for breast cancer.

Successive High-Resolution (H2O)n-GCIB and C60-SIMS Imaging Integrates Multi-Omics in Different Cell Types in Breast Cancer Tissue

The temporo-spatial organization of different cells in the tumor microenvironment (TME) is the key to understanding their complex communication networks and the immune landscape that exists within compromised tissues. Multi-omics profiling of single-interacting cells in the native TME is critical for providing further information regarding the reprograming mechanisms leading to immunosuppression and tumor progression. This requires new technologies for biomolecular profiling of phenotypically heterogeneous cells on the same tissue sample. Here, we developed a new methodology for comprehensive lipidomic and metabolomic profiling of individual cells on frozen-hydrated tissue sections using water gas cluster ion beam secondary ion mass spectrometry ((H2O)n-GCIB-SIMS) (at 1.6 μm beam spot size), followed by profiling cell-type specific lanthanide antibodies on the same tissue section using C60-SIMS (at 1.1 μm beam spot size). We revealed distinct variations of distribution and intensities of >150 key ions (e.g., lipids and important metabolites) in different types of the TME individual cells, such as actively proliferating tumor cells as well as infiltrating immune cells. The demonstrated feasibility of SIMS imaging to integrate the multi-omics profiling in the same tissue section at the single-cell level will lead to new insights into the role of lipid reprogramming and metabolic response in normal regulation or pathogenic discoordination of cell-cell interactions in a variety of tissue microenvironments.

Cancer metabolomics: A tool of clinical utility for early diagnosis of gynaecological cancers

Gynaecological cancers are the major cause of cancer-related deaths in Indian women. The poor prognosis and lack of symptoms in the early stages make early cancer diagnosis difficult. The absence of mandatory screening programmes and the lack of awareness pose to be a real challenge in a developing economy as India. Prompt intervention is required to enhance cancer patient survival statistics and to lessen the social and financial burden. Conventional screening and cytological techniques employed currently have helped to reduce the incidence of cancers considerably. However, these tests offer low sensitivity and specificity and are not widely used for risk assessment, leading to inadequate early-stage cancer diagnosis. The accomplishment of Human Genome Project (HGP) has opened doors to exciting 'omics' platforms. Promising research in genomics and proteomics has revolutionized cancer detection and screening methodologies by providing more insights in the gene expression, protein function and how specific mutation in specific genes corresponds to a particular phenotype. However, these are incompetent to translate the information into clinical applicability. Various factors such as low sensitivity, diurnal variation in protein, poor reproducibility and analytical variables are prime hurdles. Thus the focus has been shifted to metabolomics, which is a much younger platform compared to genomics and proteomics. Metabolomics focuses on endpoint metabolites, which are final products sustained in the response to genetic or environmental changes by a living system. As a result, the metabolome indicates the cell's functional condition, which is directly linked to its phenotype. Metabolic profiling aims to study the changes occurred in metabolic pathways. This metabolite profile is capable of differentiating the healthy individuals from those having cancer. The pathways that a cell takes in turning malignant are exceedingly different, owing to the fact that transformation of healthy cells to abnormal cells is linked with significant metabolic abnormalities. This review is aimed to discuss metabolomics and its potential role in early diagnosis of gynaecological cancers, viz. breast, ovarian and cervical 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).

Personalized Metabolic Analysis of Diseases

The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm called Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for a detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with a dynamically built objective function based on biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds supervised classification models to discriminate between patients and healthy subjects based on the computed metabolic network changes. The use of Metabolitics is demonstrated for three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully differentiate patients from healthy individuals by an average f1-score of 88 percent. Besides, in addition to the confirmation of previously reported breast cancer-associated pathways, we discovered that Biotin Metabolism along with Arginine and Proline Metabolism is subject to a significant increase in flux capacity, which have not been reported before.

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

PURPOSE

The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure.

METHODS

Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.

RESULTS

We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85.

CONCLUSION

Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

Lipidomics investigations into the tissue phospholipidomic landscape of invasive ductal carcinoma of the breast

The need of identifying alternative therapeutic targets for invasive ductal carcinoma (IDC) of the breast with high specificity and sensitivity for effective therapeutic intervention is crucial for lowering the risk of fatality. Lipidomics has emerged as a key area for the discovery of potential candidates owing to its several shared pathways between cancer cell proliferation and survival. In the current study, we performed comparative phospholipidomic analysis of IDC, benign and control tissue samples of the breast to identify the significant lipid alterations associated with malignant transformation. A total of 33 each age-matched tissue samples from malignant, benign and control were analyzed to identify the altered phospholipids by using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM/MS). A combination of univariate and multivariate statistical approaches was used to select the phospholipid species with the highest contribution in group segregation. Furthermore, these altered phospholipids were structurally confirmed by tandem mass spectrometry. A total of 244 phospholipids were detected consistently at quantifiable levels, out of which 32 were significantly altered in IDC of the breast. Moreover, in pairwise comparison of IDC against benign and control samples, 11 phospholipids were found to be significantly differentially expressed. Particularly, LPI 20:3, PE (22:1/22:2), LPE 20:0 and PC (20:4/22:4) were observed to be most significantly associated with IDC tissue samples. Apart from that, we also identified that long-chain unsaturated fatty acids were enriched in the IDC tissue samples as compared to benign and control samples, indicating its possible association with the invasive phenotype.

Differential epigenetic and transcriptional profile in MCF-7 breast cancer cells exposed to cadmium

Cadmium (Cd) has been confirmed to be associated with breast carcinogenesis, but the mechanism was not clarified yet. Given that epigenetic modification was speculated as underlying mechanism, we examined the differential epigenome caused by Cd in breast cancer cells. Profiles of DNA methylation, microRNA (miRNA), long non-coding RNA (lncRNA), and message RNA (mRNA) were derived from Cd-treated and untreated MCF-7 breast cancer cells by microarray. We identified 997 target genes epigenetically regulated by Cd through cross-verification with the differential epigenome and transcriptome, and 400 of them were further validated in a breast cancer cohort. Biological function analyses suggested that several pathways were involved in Cd-induced breast carcinogenesis, such as Wnt signaling, metabolism, and human papilloma virus (HPV) infection. TXNRD1 and CCT3 were further identified as the critical genes based on the degree of expression change, hazard ratio difference, and connectivity. The present study revealed that Cd epigenetically regulated several pathways involving in breast carcinogenesis, particularly the Wnt signaling and metabolic pathways, among which TXNRD1 and CCT3 might play critical roles. It was also suggested that Cd and HPV infection might jointly participate in breast tumorigenesis.

Targeted Metabolomics Analyses Reveal Specific Metabolic Alterations in High-Grade Prostate Cancer Patients

Prostate cancer (PCa) is a hormone-dependent tumor characterized by an extremely heterogeneous prognosis. Despite recent advances in partially uncovering some of the biological processes involved in its progression, there is still an urgent need for identifying more accurate and specific prognostic procedures to differentiate between disease stages. In this context, targeted approaches, focused on mapping dysregulated metabolic pathways, could play a critical role in identifying the mechanisms driving tumorigenesis and metastasis. In this study, a targeted analysis of the nuclear magnetic resonance-based metabolomic profile of PCa patients with different tumor grades, guided by transcriptomics profiles associated with their stages, was performed. Serum and urine samples were collected from 73 PCa patients. Samples were classified according to their Gleason score (GS) into low-GS (GS < 7) and high-GS PCa (GS ≥ 7) groups. A total of 36 metabolic pathways were found to be dysregulated in the comparison between different PCa grades. Particularly, the levels of glucose, glycine and 1-methlynicotinamide, metabolites involved in energy metabolism and nucleotide synthesis were significantly altered between both groups of patients. These results underscore the potential of targeted metabolomic profiling to characterize relevant metabolic changes involved in the progression of this neoplastic process.

Distinct pattern of one-carbon metabolism, a nutrient-sensitive pathway, in invasive breast cancer: A metabolomic study

Altered cell metabolism is a hallmark of cancer and critical for its development. Particularly, activation of one-carbon metabolism in tumor cells can sustain oncogenesis while contributing to epigenetic changes and metabolic adaptation during tumor progression. We assessed whether increased one-carbon metabolism activity is a metabolic feature of invasive ductal carcinoma (IDC). Differences in the metabolic profile between biopsies from IDC (n = 47) and its adjacent tissue (n = 43) and between biopsies from different breast cancer subtypes were assessed by gas spectrometry in targeted (Biocrates Life Science^®) and untargeted approaches, respectively. The metabolomics data were statistically treated using MetaboAnalyst 4.0, SIMCA P+ (version 12.01), Statistica 10 software and t test with p < 0.05. The Cancer Genome Atlas breast cancer dataset was also assessed to validate the metabolomic profile of IDC. Our targeted metabolomics analysis showed distinct metabolomics profiles between IDC and adjacent tissue, where IDC displayed a comparative enrichment of metabolites involved in one-carbon metabolism (serine, glycine, threonine, and methionine) and a predicted increase in the activity of pathways that receive and donate carbon units (i.e., folate, methionine, and homocysteine). In addition, the targeted and untargeted metabolomics analyses showed similar metabolomics profiles between breast cancer subtypes. The gene set enrichment analysis identified different transcription-related functions between IDC and non-tumor tissues that involved one-carbon metabolism. Our data suggest that one-carbon metabolism may be a central pathway in IDC and even in general breast tumors, representing a potential target for its treatment and prevention.

Application of metabolomics in the diagnosis of breast cancer: a systematic review

Breast cancer (BC) remains the most frequent type of cancer in females worldwide. However, the pathogenesis of BC is still under the cloud, along with the huge challenge of early diagnosis, which is widely acknowledged as the key to a successful therapy. Metabolomics, a newborn innovative technique in recent years, has demonstrated great potential in cancer-related researches. The aim of this review is to look back on clinical and cellular metabolomic studies in the diagnosis of BC over the past decade, and provide a systematic summary of metabolic biomarkers and pathways related to BC diagnosis.

Lyn regulates creatine uptake in an imatinib-resistant CML cell line

BACKGROUND

Imatinib mesylate (imatinib) is the first-line treatment for newly diagnosed chronic myeloid leukemia (CML) due to its remarkable hematologic and cytogenetic responses. We previously demonstrated that the imatinib-resistant CML cells (Myl-R) contained elevated Lyn activity and intracellular creatine pools compared to imatinib-sensitive Myl cells.

METHODS

Stable isotope metabolic labeling, media creatine depletion, and Na⁺/K⁺-ATPase inhibitor experiments were performed to investigate the origin of creatine pools in Myl-R cells. Inhibition and shRNA knockdown were performed to investigate the specific role of Lyn in regulating the Na⁺/K⁺-ATPase and creatine uptake.

RESULTS

Inhibition of the Na⁺/K⁺-ATPase pump (ouabain, digitoxin), depletion of extracellular creatine or inhibition of Lyn kinase (ponatinib, dasatinib), demonstrated that enhanced creatine accumulation in Myl-R cells was dependent on uptake from the growth media. Creatine uptake was independent of the Na⁺/creatine symporter (SLC6A8) expression or de novo synthesis. Western blot analyses showed that phosphorylation of the Na⁺/K⁺-ATPase on Tyr 10 (Y10), a known regulatory phosphorylation site, correlated with Lyn activity. Overexpression of Lyn in HEK293 cells increased Y10 phosphorylation (pY10) of the Na⁺/K⁺-ATPase, whereas Lyn inhibition or shRNA knockdown reduced Na⁺/K⁺-ATPase pY10 and decreased creatine accumulation in Myl-R cells. Consistent with enhanced uptake in Myl-R cells, cyclocreatine (Ccr), a cytotoxic creatine analog, caused significant loss of viability in Myl-R compared to Myl cells.

CONCLUSIONS

These data suggest that Lyn can affect creatine uptake through Lyn-dependent phosphorylation and regulation of the Na⁺/K⁺-ATPase pump activity.

GENERAL SIGNIFICANCE

These studies identify kinase regulation of the Na⁺/K⁺-ATPase as pivotal in regulating creatine uptake and energy metabolism in cells.

Rewiring of Metabolic Network in Mycobacterium tuberculosis During Adaptation to Different Stresses

Metabolic adaptation of Mycobacterium tuberculosis (M. tuberculosis) to microbicidal intracellular environment of host macrophages is fundamental to its pathogenicity. However, an in-depth understanding of metabolic adjustments through key reaction pathways and networks is limited. To understand how such changes occur, we measured the cellular metabolome of M. tuberculosis subjected to four microbicidal stresses using liquid chromatography-mass spectrometric multiple reactions monitoring (LC-MRM/MS). Overall, 87 metabolites were identified. The metabolites best describing the separation between stresses were identified through multivariate analysis. The coupling of the metabolite measurements with existing genome-scale metabolic model, and using constraint-based simulation led to several new concepts and unreported observations in M. tuberculosis; such as (i) the high levels of released ammonia as an adaptive response to acidic stress was due to increased flux through L-asparaginase rather than urease activity; (ii) nutrient starvation-induced anaplerotic pathway for generation of TCA intermediates from phosphoenolpyruvate using phosphoenolpyruvate kinase; (iii) quenching of protons through GABA shunt pathway or sugar alcohols as possible mechanisms of early adaptation to acidic and oxidative stresses; and (iv) usage of alternate cofactors by the same enzyme as a possible mechanism of rewiring metabolic pathways to overcome stresses. Besides providing new leads and important nodes that can be used for designing intervention strategies, the study advocates the strength of applying flux balance analyses coupled with metabolomics to get a global picture of complex metabolic adjustments.

Metabolic crosstalk in the breast cancer microenvironment

During tumorigenesis, breast tumour cells undergo metabolic reprogramming, which generally includes enhanced glycolysis, tricarboxylic acid cycle activity, glutaminolysis and fatty acid biosynthesis. However, the extension and functional importance of these metabolic alterations may diverge not only according to breast cancer subtypes, but also depending on the interaction of cancer cells with the complex surrounding microenvironment. This microenvironment comprises a variety of non-cancerous cells, such as immune cells (e.g. macrophages, lymphocytes, natural killer cells), fibroblasts, adipocytes and endothelial cells, together with extracellular matrix components and soluble factors, which influence cancer progression and are predictive of clinical outcome. The continuous interaction between cancer and stromal cells results in metabolic competition and symbiosis, with oncogenic-driven metabolic reprogramming of cancer cells shaping the metabolism of neighbouring cells and vice versa. This review addresses current knowledge on this metabolic crosstalk within the breast tumour microenvironment (TME). Improved understanding of how metabolism in the TME modulates cancer development and evasion of tumour-suppressive mechanisms may provide clues for novel anticancer therapeutics directed to metabolic targets.

Metabolic adaptation to feed restriction on the green sturgeon (Acipenser medirostris) fingerlings

Food restriction may cause severe biological effects on wildlife and lead to population decline and extinction. The objective of the current study was to examine the metabolic effects on green sturgeon in response to feed restriction. Green sturgeon fingerlings were fed for two weeks at 12.5, 25, 50 and 100% of the optimum feeding rate (OFR), which corresponded to 0.25, 0.50, 1.00, and 2.00% body weight per day. We characterized the changes in hydrophilic and hydrophobic metabolites from extracts of muscle, liver, and kidney using nuclear magnetic resonance spectroscopy followed by multivariate statistical analysis. The results of principal component analysis (PCA) score plots from the analyses of hydrophilic metabolites showed that they exhibited a greater response to feed restriction than hydrophobic metabolites. In general, the hydrophilic metabolites in tissues from fish fed ≦25% of the OFR were separated from those fed 100% of the OFR in the PCA score plots. Among the three types of tissues examined, the overall metabolite changes showed a greater response to feed restriction in kidney tissue than in liver or muscle tissues. Numerous glucogenic amino acids in muscle and most amino acids in the kidney were decreased under feed restriction conditions. A significant decrease in ketone bodies (3-hydroxyisobutyrate) was observed in the muscle. Most fatty acids except for glycerol, phospholipid and cholesterol in the liver and kidney tissues were decreased under feed restriction conditions. Creatine phosphate, taurine and glycine were also significantly increased in tissues under feed restriction conditions. In conclusion, this study suggests that the manipulation of feed restriction under the current conditions perturbed metabolites related to energy metabolism, osmolality regulation, and antioxidation capacity in the sturgeon.

Metabolomics: a promising diagnostic and therapeutic implement for breast cancer

Breast cancer (BC) is the most commonly diagnosed cancer among women and the leading cause of cancer death. Despite the advent of numerous diagnosis and treatment methods in recent years, this heterogeneous disease still presents great challenges in early diagnosis, curative treatments and prognosis monitoring. Thus, finding promising early diagnostic biomarkers and therapeutic targets and approaches is meaningful. Metabolomics, which focuses on the analysis of metabolites that change during metabolism, can reveal even a subtle abnormal change in an individual. In recent decades, the exploration of cancer-related metabolomics has increased. Metabolites can be promising biomarkers for the screening, response evaluation and prognosis of BC. In this review, we summarized the workflow of metabolomics, described metabolite signatures based on molecular subtype as well as reclassification and then discussed the application of metabolomics in the early diagnosis, monitoring and prognosis of BC to offer new insights for clinicians in breast cancer diagnosis and treatment.

Impact of post-surgical freezing delay on brain tumor metabolomics

INTRODUCTION

Translational cancer research has seen an increasing interest in metabolomic profiling to decipher tumor phenotypes. However, the impact of post-surgical freezing delays on mass spectrometric metabolomic measurements of the cancer tissue remains elusive.

OBJECTIVES

To evaluate the impact of post-surgical freezing delays on cancer tissue metabolomics and to investigate changes per metabolite and per metabolic pathway.

METHODS

We performed untargeted metabolomics on three cortically located and bulk-resected glioblastoma tissues that were sequentially frozen as duplicates at up to six different time delays (0-180 min, 34 samples).

RESULTS

Statistical modelling revealed that 10% of the metabolome (59 of 597 metabolites) changed significantly after a 3 h delay. While carbohydrates and energy metabolites decreased, peptides and lipids increased. After a 2 h delay, these metabolites had changed by as much as 50-100%. We present the first list of metabolites in glioblastoma tissues that are sensitive to post-surgical freezing delays and offer the opportunity to define individualized fold change thresholds for future comparative metabolomic studies.

CONCLUSION

More researchers should take these pre-analytical factors into consideration when analyzing metabolomic data. We present a strategy for how to work with metabolites that are sensitive to freezing delays.

Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study

Multiple myeloma (MM) is the second most prevalent hematological malignancy characterized by rapid proliferation of plasma cells, which leads to overproduction of antibodies. MM affects around 15% of all hemato-oncology cases across the world. The present study involves identification of metabolomic alterations in the serum of an MM cohort compared to healthy controls using both LC-MRM/MS based targeted and GC-MS based untargeted approaches. Several MM specific serum metabolomic signatures were observed in this study. A total of 54 metabolites were identified as being significantly altered in MM cohort, out of which, 26 metabolites were identified from LC-MRM/MS based targeted analysis, whereas 28 metabolites were identified from the GC-MS based untargeted analysis. Receiver operating characteristic (ROC) curve analysis demonstrated that six metabolites each from both the datasets can be projected as marker metabolites to discriminate MM subjects with higher specificity and sensitivity. Moreover, pathway analysis deciphered that several metabolic pathways were altered in MM including pyrimidine metabolism, purine metabolism, amino acid metabolism, nitrogen metabolism, sulfur metabolism, and the citrate cycle. Comprehensively, this study contributes valuable information regarding MM induced serum metabolite alterations and their pathways, which could offer further insights into this cancer.

This study presents the potential of serum metabolomics approach towards the segregation of multiple myeloma cohort from healthy controls.

Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

INTRODUCTION

Invasive ductal carcinoma (IDC) is a type of breast cancer, usually detected in advanced stages due to its asymptomatic nature which ultimately leads to low survival rate. Identification of urinary metabolic adaptations induced by IDC to understand the disease pathophysiology and monitor therapy response would be a helpful approach in clinical settings. Moreover, its non-invasive and cost effective strategy better suited to minimize apprehension among high risk population.

OBJECTIVE

This study aims toward investigating the urinary metabolic alterations of IDC by targeted (LC-MRM/MS) and untargeted (GC-MS) approaches for the better understanding of the disease pathophysiology and monitoring therapy response.

METHODS

Urinary metabolic alterations of IDC subjects (63) and control subjects (63) were explored by targeted (LC-MRM/MS) and untargeted (GC-MS) approaches. IDC specific urinary metabolomics signature was extracted by applying both univariate and multivariate statistical tools.

RESULTS

Statistical analysis identified 39 urinary metabolites with the highest contribution to metabolomic alterations specific to IDC. Out of which, 19 metabolites were identified from targeted LC-MRM/MS analysis, while 20 were identified from the untargeted GC-MS analysis. Receiver operator characteristic (ROC) curve analysis evidenced 6 most discriminatory metabolites from each type of approach that could differentiate between IDC subjects and controls with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in IDC including sugar, amino acid, nucleotide metabolism, TCA cycle etc. CONCLUSIONS: Overall, this study provides valuable inputs regarding altered urinary metabolites which improved our knowledge on urinary metabolomic alterations induced by IDC. Moreover, this study identified several dysregulated metabolic pathways which offer further insight into the disease pathophysiology.