NMR metabolomics of MTLn3E breast cancer cells identifies a role for CXCR4 in lipid and choline regulation

Faecal and urine metabolites, but not gut microbiota, may predict response to low FODMAP diet in irritable bowel syndrome

BACKGROUND

The low FODMAP diet (LFD) leads to clinical response in 50%-80% of patients with irritable bowel syndrome (IBS). It is unclear why only some patients respond.

AIMS

To determine if differences in baseline faecal microbiota or faecal and urine metabolite profiles may separate clinical responders to the diet from non-responders allowing predictive algorithms to be proposed.

METHODS

We recruited adults fulfilling Rome III criteria for IBS to a blinded randomised controlled trial. Patients were randomised to sham diet with a placebo supplement (control) or LFD supplemented with either placebo (LFD) or 1.8 g/d B-galactooligosaccharide (LFD/B-GOS), for 4 weeks. Clinical response was defined as adequate symptom relief at 4 weeks after the intervention (global symptom question). Differences between responders and non-responders in faecal microbiota (FISH, 16S rRNA sequencing) and faecal (gas-liquid chromatography, gas-chromatography mass-spectrometry) and urine (1 H NMR) metabolites were analysed.

RESULTS

At 4 weeks, clinical response differed across the 3groups with adequate symptom relief of 30% (7/23) in controls, 50% (11/22) in the LFD group and 67% (16/24) in the LFD/B-GOS group (p = 0.048). In the control and the LFD/B-GOS groups, microbiota and metabolites did not separate responders from non-responders. In the LFD group, higher baseline faecal propionate (sensitivity 91%, specificity 89%) and cyclohexanecarboxylic acid esters (sensitivity 80%, specificity 78%), and urine metabolite profile (Q2 0.296 vs. randomised -0.175) predicted clinical response.

CONCLUSIONS

Baseline faecal and urine metabolites may predict response to the LFD.

Rifampin- or Capreomycin-Induced Remodeling of the Mycobacterium smegmatis Mycolic Acid Layer Is Mitigated in Synergistic Combinations with Cationic Antimicrobial Peptides

The mycobacterial cell wall affords natural resistance to antibiotics. Antimicrobial peptides (AMPs) modify the surface properties of mycobacteria and can act synergistically with antibiotics from differing classes. Here, we investigate the response of Mycobacterium smegmatis to the presence of rifampin or capreomycin, either alone or in combination with two synthetic, cationic, α-helical AMPs that are distinguished by the presence (D-LAK120-HP13) or absence (D-LAK120-A) of a kink-inducing proline. Using a combination of high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) metabolomics, diphenylhexatriene (DPH) fluorescence anisotropy measurements, and laurdan emission spectroscopy, we show that M. smegmatis responds to challenge with rifampin or capreomycin by substantially altering its metabolism and, in particular, by remodeling the cell envelope. Overall, the changes are consistent with a reduction of trehalose dimycolate and an increase of trehalose monomycolate and are associated with increased rigidity of the mycolic acid layer observed following challenge by capreomycin but not rifampin. Challenge with D-LAK120-A or D-LAK120-HP13 induced no or modest changes, respectively, in mycomembrane metabolites and did not induce a significant increase in the rigidity of the mycolic acid layer. Furthermore, the response to rifampin or capreomycin was significantly reduced when these were combined with D-LAK120-HP13 and D-LAK120-A, respectively, suggesting a possible mechanism for the synergy of these combinations. The remodeling of the mycomembrane in M. smegmatis is therefore identified as an important countermeasure deployed against rifampin or capreomycin, but this can be mitigated and the efficacy of rifampin or capreomycin potentiated by combining the drug with AMPs.IMPORTANCE We have used a combined NMR metabolomics/biophysical approach to better understand differences in the mechanisms of two closely related antimicrobial peptides, as well as the response of the model organism Mycobacterium smegmatis to challenge with first- or second-line antibiotics used against mycobacterial pathogens. We show that, in addition to membrane damage, the triggering of oxidative stress may be an important part of the mechanism of action of one AMP. The metabolic shift that accompanied rifampin and, particularly, capreomycin challenge was associated with modest and more dramatic changes, respectively, in the mycomembrane, providing a rationale for how the response to one antibiotic may affect bacterial penetration and, hence, the action of another. This study presents the first insights into how antimicrobial peptides may operate synergistically with existing antibiotics whose efficacy is waning or sensitize MDR mycobacteria and/or latent mycobacterial infections to them, prolonging the useful life of these drugs.

Applications of high-resolution magic angle spinning MRS in biomedical studies I-cell line and animal models

High-resolution magic angle spinning (HRMAS) MRS allows for direct measurements of non-liquid tissue and cell specimens to present valuable insights into the cellular metabolisms of physiological and pathological processes. HRMAS produces high-resolution spectra comparable to those obtained from solutions of specimen extracts but without complex metabolite extraction processes, and preserves the tissue cellular structure in a form suitable for pathological examinations following spectroscopic analysis. The technique has been applied in a wide variety of biomedical and biochemical studies and become one of the major platforms of metabolomic studies. By quantifying single metabolites, metabolite ratios, or metabolic profiles in their entirety, HRMAS presents promising possibilities for diagnosis and prediction of clinical outcomes for various diseases, as well as deciphering of metabolic changes resulting from drug therapies or xenobiotic interactions. In this review, we evaluate HRMAS MRS results on animal models and cell lines reported in the literature, and present the diverse applications of the method for the understanding of pathological processes and the effectiveness of therapies, development of disease animal models, and new progress in HRMAS methodology.

[18F]tetrafluoroborate-PET/CT enables sensitive tumor and metastasis in vivo imaging in a sodium iodide symporter-expressing tumor model

Cancer cell metastasis is responsible for most cancer deaths. Non-invasive in vivo cancer cell tracking in spontaneously metastasizing tumor models still poses a challenge requiring highest sensitivity and excellent contrast. The goal of this study was to evaluate if the recently introduced PET radiotracer [18F]tetrafluoroborate ([18F]BF4-) is useful for sensitive and specific metastasis detection in an orthotopic xenograft breast cancer model expressing the human sodium iodide symporter (NIS) as a reporter. In vivo imaging was complemented by ex vivo fluorescence microscopy and γ-counting of harvested tissues. Radionuclide imaging with [18F]BF4- (PET/CT) was compared to the conventional tracer [123I]iodide (sequential SPECT/CT). We found that [18F]BF4- was superior due to better pharmacokinetics, i.e. faster tumor uptake and faster and more complete clearance from circulation. [18F]BF4--PET was also highly specific as in all detected tissues cancer cell presence was confirmed microscopically. Undetected comparable tissues were similarly found to be free of metastasis. Metastasis detection by routine metabolic imaging with [18F]FDG-PET failed due to low standard uptake values and low contrast caused by adjacent metabolically active organs in this model. [18F]BF4--PET combined with NIS expressing disease models is particularly useful whenever preclinical in vivo cell tracking is of interest.

Divergent Relationships between Fecal Microbiota and Metabolome following Distinct Antibiotic-Induced Disruptions

Despite the fundamental importance of antibiotic therapies to human health, their functional impact on the intestinal microbiome and its subsequent ability to recover are poorly understood. Much research in this area has focused on changes in microbiota composition, despite the interdependency and overlapping functions of many members of the microbial community. These relationships make prediction of the functional impact of microbiota-level changes difficult, while analyses based on the metabolome alone provide relatively little insight into the taxon-level changes that underpin changes in metabolite levels. Here, we used combined microbiota and metabolome profiling to characterize changes associated with clinically important antibiotic combinations with distinct effects on the gut. Correlation analysis of changes in the metabolome and microbiota indicate that a combined approach will be essential for a mechanistic understanding of the functional impact of distinct antibiotic classes.

The intestinal microbiome plays an essential role in regulating many aspects of host physiology, and its disruption through antibiotic exposure has been implicated in the development of a range of serious pathologies. The complex metabolic relationships that exist between members of the intestinal microbiota and the potential redundancy in functional pathways mean that an integrative analysis of changes in both structure and function are needed to understand the impact of antibiotic exposure. We used a combination of next-generation sequencing and nuclear magnetic resonance (NMR) metabolomics to characterize the effects of two clinically important antibiotic treatments, ciprofloxacin and vancomycin-imipenem, on the intestinal microbiomes of female C57BL/6 mice. This assessment was performed longitudinally and encompassed both antibiotic challenge and subsequent microbiome reestablishment. Both antibiotic treatments significantly altered the microbiota and metabolite compositions of fecal pellets during challenge and recovery. Spearman’s correlation analysis of microbiota and NMR data revealed that, while some metabolites could be correlated with individual operational taxonomic units (OTUs), frequently multiple OTUs were associated with a significant change in a given metabolite. Furthermore, one metabolite, arginine, can be associated with increases/decreases in different sets of OTUs under differing conditions. Taken together, these findings indicate that reliance on shifts in one data set alone will generate an incomplete picture of the functional effect of antibiotic intervention. A full mechanistic understanding will require knowledge of the baseline microbiota composition, combined with both a comparison and an integration of microbiota, metabolomics, and phenotypic data.

IMPORTANCE Despite the fundamental importance of antibiotic therapies to human health, their functional impact on the intestinal microbiome and its subsequent ability to recover are poorly understood. Much research in this area has focused on changes in microbiota composition, despite the interdependency and overlapping functions of many members of the microbial community. These relationships make prediction of the functional impact of microbiota-level changes difficult, while analyses based on the metabolome alone provide relatively little insight into the taxon-level changes that underpin changes in metabolite levels. Here, we used combined microbiota and metabolome profiling to characterize changes associated with clinically important antibiotic combinations with distinct effects on the gut. Correlation analysis of changes in the metabolome and microbiota indicate that a combined approach will be essential for a mechanistic understanding of the functional impact of distinct antibiotic classes.

M. de Rosales R. Exploiting the Metal-Chelating Properties of the Drug Cargo for In Vivo Positron Emission Tomography Imaging of Liposomal Nanomedicines

The clinical value of current and future nanomedicines can be improved by introducing patient selection strategies based on noninvasive sensitive whole-body imaging techniques such as positron emission tomography (PET). Thus, a broad method to radiolabel and track preformed nanomedicines such as liposomal drugs with PET radionuclides will have a wide impact in nanomedicine. Here, we introduce a simple and efficient PET radiolabeling method that exploits the metal-chelating properties of certain drugs (e.g., bisphosphonates such as alendronate and anthracyclines such as doxorubicin) and widely used ionophores to achieve excellent radiolabeling yields, purities, and stabilities with 89Zr, 52Mn, and 64Cu, and without the requirement of modification of the nanomedicine components. In a model of metastatic breast cancer, we demonstrate that this technique allows quantification of the biodistribution of a radiolabeled stealth liposomal nanomedicine containing alendronate that shows high uptake in primary tumors and metastatic organs. The versatility, efficiency, simplicity, and GMP compatibility of this method may enable submicrodosing imaging studies of liposomal nanomedicines containing chelating drugs in humans and may have clinical impact by facilitating the introduction of image-guided therapeutic strategies in current and future nanomedicine clinical studies.

Metabolomics Analysis of Hormone-Responsive and Triple-Negative Breast Cancer Cell Responses to Paclitaxel Identify Key Metabolic Differences

To date, no targeted therapies are available to treat triple negative breast cancer (TNBC), while other breast cancer subtypes are responsive to current therapeutic treatment. Metabolomics was conducted to reveal differences in two hormone receptor-negative TNBC cell lines and two hormone receptor-positive Luminal A cell lines. Studies were conducted in the presence and absence of paclitaxel (Taxol). TNBC cell lines had higher levels of amino acids, branched-chain amino acids, nucleotides, and nucleotide sugars and lower levels of proliferation-related metabolites like choline compared with Luminal A cell lines. In the presence of paclitaxel, each cell line showed unique metabolic responses, with some similarities by type. For example, in the Luminal A cell lines, levels of lactate and creatine decreased while certain choline metabolites and myo-inositol increased with paclitaxel. In the TNBC cell lines levels of glutamine, glutamate, and glutathione increased, whereas lysine, proline, and valine decreased in the presence of drug. Profiling secreted inflammatory cytokines in the conditioned media demonstrated a greater response to paclitaxel in the hormone-positive Luminal cells compared with a secretion profile that suggested greater drug resistance in the TNBC cells. The most significant differences distinguishing the cell types based on pathway enrichment analyses were related to amino acid, lipid and carbohydrate metabolism pathways, whereas several biological pathways were differentiated between the cell lines following treatment.

Metabolomic Approaches in Cancer Epidemiology

Metabolomics is the study of low molecular weight molecules or metabolites produced within cells and biological systems. It involves technologies such as mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) that can measure hundreds of thousands of unique chemical entities (UCEs). The metabolome provides one of the most accurate reflections of cellular activity at the functional level and can be leveraged to discern mechanistic information during normal and disease states. The advantages of metabolomics over other “omics” include its high sensitivity and ability to enable the analysis of relatively few metabolites compared with the number of genes and messenger RNAs (mRNAs). In clinical samples, metabolites are more stable than proteins or RNA. In fact, metabolomic profiling in basic, epidemiologic, clinical, and translational studies has revealed potential new biomarkers of disease and therapeutic outcome and has led to a novel mechanistic understanding of pathogenesis. These potential biomarkers include novel metabolites associated with cancer initiation, regression, and recurrence. Unlike genomics or even proteomics, however, the degree of metabolite complexity and heterogeneity within biological systems presents unique challenges that require specialized skills and resources to overcome. This article discusses epidemiologic studies of altered metabolite profiles in several cancers as well as challenges in the field and potential approaches to overcoming them.

The roles of IP3 receptor in energy metabolic pathways and reactive oxygen species homeostasis revealed by metabolomic and biochemical studies

Inositol 1,4,5-trisphosphate receptors (IP(3)Rs) are calcium channels modulating important calcium-mediated processes. Recent studies implicate IP(3)R in cell metabolism, but specific evidence is missing regarding IP(3)R's effects on actual metabolic pathways and key energy metabolites. Here, we applied metabolomics and molecular biology to compare DT40 cell lines devoid of IP(3)R (KO) and its wild-type (WT) counterpart. NMR and LC-MS metabolomic data showed that the KO cell line has a very different basic energy metabolism from the WT cell line, showing enhanced Warburg effect. In particular, the KO cells exhibited significant perturbation in energy charge, reduced glutathione and NADPH ratios with slower cellular growth rate. Subsequent flow cytometry results showed that the KO cell line has a higher level of general reactive oxygen species (ROS) but has a lower level of peroxynitrites. This ROS disturbance could be explained by observing lower expression of superoxide dismutase 2 (SOD2) and unchanged expression of catalase. The higher ROS seems to be involved in the slower growth rate of the KO cells, with an ROS scavenger increasing their growth rate. However, the KO and WT cell lines did not show any noticeable differences in AMPK and phosphorylated AMPK levels, suggesting possible saturation of AMPK-mediated metabolic regulatory circuit in both cells. Overall, our study reveals IP3R's roles in ROS homeostasis and metabolic pathways as well as the effects of its KO on cellular phenotypes.

In situ Fourier transform infrared analysis of live cells' response to doxorubicin

The study of the response of cancer cells to chemotherapy drugs is of high importance due to the specificity of some drugs to certain types of cancer and the resistance of some specific cancer types to chemotherapy drugs. Our aim was to develop and apply the label-free and non-destructive Fourier transform infrared (FTIR) method to determine the sensitivity of three different cancer cell-lines to a common anti-cancer drug doxorubicin at different concentrations and to demonstrate that information about the mechanism of resistance to the chemotherapy drug can be extracted from spectral data. HeLa, PC3, and Caco-2 cells were seeded and grown on an attenuated total reflection (ATR) crystal, doxorubicin was applied at the clinically significant concentration of 0.1-20 μM, and spectra of the cells were collected hourly over 20 h. Analysis of the amide bands was correlated with cell viability, which had been cross validated with MTT assays, allowing to determine that the three cell lines had significantly different resistance to doxorubicin. The difference spectra and principal component analysis (PCA) highlighted the subtle chemical changes in the living cells under treatment. Spectral regions assigned to nucleic acids (mainly 1085 cm(-1)) and carbohydrates (mainly 1024 cm(-1)) showed changes that could be related to the mode of action of the drug and the mechanism of resistance of the cell lines to doxorubicin. This is a cost-effective method that does not require bioassay reagents but allows label-free, non-destructive and in situ analysis of chemical changes in live cells, using standard FTIR equipment adapted to ATR measurements.

Fluorescence axial nanotomography with plasmonics

We present a novel imaging technique with super-resolution axial sensitivity, exploiting the changes in fluorescence lifetime above a plasmonic substrate. Using conventional confocal fluorescence lifetime imaging, we show that it is possible to deliver down to 6 nm axial position sensitivity of fluorophores in whole biological cell imaging. We employ this technique to map the topography of the cellular membrane, and demonstrate its application in an investigation of receptor-mediated endocytosis in carcinoma cells.

Metabolomics in cell culture--a strategy to study crucial metabolic pathways in cancer development and the response to treatment

Metabolomics is a comprehensive tool for monitoring processes within biological systems. Thus, metabolomics may be widely applied to the determination of diagnostic biomarkers for certain diseases or treatment outcomes. There is significant potential for metabolomics to be implemented in cancer research because cancer may modify metabolic pathways in the whole organism. However, not all biological questions can be answered solely by the examination of small molecule composition in biofluids; in particular, the study of cellular processes or preclinical drug testing requires ex vivo models. The major objective of this review was to summarise the current achievement in the field of metabolomics in cancer cell culture-focusing on the metabolic pathways regulated in different cancer cell lines-and progress that has been made in the area of drug screening and development by the implementation of metabolomics in cell lines.

Functional divergence in gastrointestinal microbiota in physically-separated genetically identical mice

Despite the fundamental contribution of the gut microbiota to host physiology, the extent of its variation in genetically-identical animals used in research is not known. We report significant divergence in both the composition and metabolism of gut microbiota in genetically-identical adult C57BL/6 mice housed in separate controlled units within a single commercial production facility. The reported divergence in gut microbiota has the potential to confound experimental studies using mammalian models.

Combined systems approaches reveal highly plastic responses to antimicrobial peptide challenge in Escherichia coli

Obtaining an in-depth understanding of the arms races between peptides comprising the innate immune response and bacterial pathogens is of fundamental interest and will inform the development of new antibacterial therapeutics. We investigated whether a whole organism view of antimicrobial peptide (AMP) challenge on Escherichia coli would provide a suitably sophisticated bacterial perspective on AMP mechanism of action. Selecting structurally and physically related AMPs but with expected differences in bactericidal strategy, we monitored changes in bacterial metabolomes, morphological features and gene expression following AMP challenge at sub-lethal concentrations. For each technique, the vast majority of changes were specific to each AMP, with such a plastic response indicating E. coli is highly capable of discriminating between specific antibiotic challenges. Analysis of the ontological profiles generated from the transcriptomic analyses suggests this approach can accurately predict the antibacterial mode of action, providing a fresh, novel perspective for previous functional and biophysical studies.

Antimicrobial peptides (AMP) are small proteins with often potent antibacterial activity found in a variety of organisms, including humans. Understanding how these antibiotics operate is challenging and often controversial since many studies have necessarily focussed on identifying a single major cause of bacterial cell death while, increasingly, others have cautioned that AMPs are likely to have access to multiple bactericidal features. Systems biology is an emerging field that comprises a series of techniques capable of giving a global view of how bacteria respond to external stimuli. Here we have monitored changes in gene expression and metabolism in bacteria that have been challenged with sub-lethal concentrations of four different AMPs. By understanding how bacteria respond to a threat we can reveal how the bacteria perceive the AMP to be operating. Our approach provides a sophisticated bacterial perspective of the mode of action of each AMP and reveals that the bacteria have a vast array of weapons that can be marshalled to deal with distinct AMP threats. Indeed, around a third (or even more) of the bacterial machinery might be useful in dealing with antibiotic challenges, highlighting why antibiotic resistance is such a persistent problem.

A whole-body dual-modality radionuclide optical strategy for preclinical imaging of metastasis and heterogeneous treatment response in different microenvironments

Imaging spontaneous cancer cell metastasis or heterogeneous tumor responses to drug treatment in vivo is difficult to achieve. The goal was to develop a new highly sensitive and reliable preclinical longitudinal in vivo imaging model for this purpose, thereby facilitating discovery and validation of anticancer therapies or molecular imaging agents.

METHODS

The strategy is based on breast cancer cells stably expressing the human sodium iodide symporter (NIS) fused to a red fluorescent protein, thereby permitting radionuclide and fluorescence imaging. Using whole-body nano-SPECT/CT with (99m)TcO4(-), we followed primary tumor growth and spontaneous metastasis in the presence or absence of etoposide treatment. NIS imaging was used to classify organs as small as individual lymph nodes (LNs) to be positive or negative for metastasis, and results were confirmed by confocal fluorescence microscopy. Etoposide treatment efficacy was proven by ex vivo anticaspase 3 staining and fluorescence microscopy.

RESULTS

In this preclinical model, we found that the NIS imaging strategy outperformed state-of-the-art (18)F-FDG imaging in its ability to detect small tumors (18.5-fold-better tumor-to-blood ratio) and metastases (LN, 3.6-fold) because of improved contrast in organs close to metastatic sites (12- and 8.5-fold-lower standardized uptake value in the heart and kidney, respectively). We applied the model to assess the treatment response to the neoadjuvant etoposide and found a consistent and reliable improvement in spontaneous metastasis detection. Importantly, we also found that tumor cells in different microenvironments responded in a heterogeneous manner to etoposide treatment, which could be determined only by the NIS-based strategy and not by (18)F-FDG imaging.

CONCLUSION

We developed a new strategy for preclinical longitudinal in vivo cancer cell tracking with greater sensitivity and reliability than (18)F-FDG PET and applied it to track spontaneous and distant metastasis in the presence or absence of genotoxic stress therapy. Importantly, the model provides sufficient sensitivity and dynamic range to permit the reliable assessment of heterogeneous treatment responses in various microenvironments.

A relationship between Pseudomonal growth behaviour and cystic fibrosis patient lung function identified in a metabolomic investigation

Chronic polymicrobial lung infections in adult cystic fibrosis patients are typically dominated by high levels of Pseudomonas aeruginosa. Determining the impact of P. aeruginosa growth on airway secretion composition is fundamental to understanding both the behaviour of this pathogen in vivo, and its relationship with other potential colonising species. We hypothesised that the marked differences in the phenotypes of clinical isolates would be reflected in the metabolite composition of spent culture media. ¹H NMR spectroscopy was used to characterise the impact of P. aeruginosa growth on a synthetic medium as part of an in vitro CF lower airways model system. Comparisons of 15 CF clinical isolates were made and four distinct metabolomic clusters identified. Highly significant relationships between P. aeruginosa isolate cluster membership and both patient lung function (FEV₁) and spent culture pH were identified. This link between clinical isolate growth behaviour and FEV₁ indicates characterisation of P. aeruginosa growth may find application in predicting patient lung function while the significant divergence in metabolite production and consumption observed between CF clinical isolates suggests dominant isolate characteristics have the potential to play both a selective role in microbiota composition and influence pseudomonal behaviour in vivo.

Omic-profiling in breast cancer metastasis to bone: implications for mechanisms, biomarkers and treatment

Despite well-recognised advances in breast cancer treatment, there remain substantial numbers of patients who develop metastatic disease, of which up to 70% involves spread to bone, resulting in skeletal complications which have a major negative impact on mortality and quality of life. Bisphosphonates and newer bone-targeted agents have reduced the prevalence of skeletal complications, yet there remains significant unmet clinical need, particularly for the development of more specific therapies for the prevention and treatment of metastatic bone disease, for the prediction of risk of its development in individual patients and for the prediction of response to treatments. Modern 'omic' strategies can potentially make a major contribution to meeting this need. Technological advances in the field of nucleic acid sequencing, mass spectrometry and metabolic profiling have driven progress in genomics, transcriptomics (functional genomics), proteomics and metabolomics. This review appraises the recent application of these approaches to studies of breast cancer metastasis (particularly to bone), with a focus on understanding how omic approaches may lead to new therapeutic options and to novel biomarker molecules or molecular signatures with potential value in clinical practise. The increasingly recognised need for rigorous sample quality control and both pre-clinical and clinical validation to meet the ultimate goals of clinical utility and patient benefit is discussed. Future directions of omic driven research in breast cancer metastasis are considered, in particular micro-RNAs and their role in the post-transcriptional regulation of gene function and the possible role of cancer-stem cells and epigenetic modifications in the development of distant metastases.

Metabolomics in noninvasive breast cancer

Breast cancer remains the most leading cause of death among women worldwide. Common methods for diagnosis and surveillance include mammography, histopathology and blood tests. The major drawback of mammography is the high rate of false reports, aside from the risk from repeated exposure to harmful ionizing radiations; histopathology is time consuming and often prone to subjective interpretations; blood-based tests are attractive, but lack the sensitivity and specificity. Obviously, more sensitive biomarkers for early detection and molecular targets for better treating breast cancer are urgently needed. Fortunately, molecular level 'omics' diagnosis is becoming increasingly popular; metabolomics, diagnosis based on 'metabolic fingerprinting' may provide clinically useful biomarkers applied toward identifying metabolic alterations and has introduced new insights into the pathology of breast cancer. By applying advanced analytical and statistical tools, metabolomics involves the comprehensive profiling of the full complement of low molecular weight compounds in a biological system and could classify the basis of tumor biology of breast cancer, to identify new prognostic and predictive markers and discover new targets for future therapeutic interventions. This advanced bioanalytic methods may now open new avenues for diagnostics in cancer via discovery of biomarkers. In this review we take a closer look at the metabolomics used within the field of breast cancer diagnosis. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples.

Power of metabolomics in diagnosis and biomarker discovery of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the commonest primary hepatic malignancy and the third most common cause of cancer-related death worldwide. Incidence remains highest in the developing world and is steadily increasing across the developed world. Current diagnostic modalities, of ultrasound and α-fetoprotein, are expensive and lack sensitivity in tumor detection. Because of its asymptomatic nature, HCC is usually diagnosed at late and advanced stages, for which there are no effective therapies. Thus, biomarkers for early detection and molecular targets for treating HCC are urgently needed. Emerging high-throughput metabolomics technologies have been widely applied, aiming at the discovery of candidate biomarkers for cancer staging, prediction of recurrence and prognosis, and treatment selection. Metabolic profiles, which are affected by many physiological and pathological processes, may provide further insight into the metabolic consequences of this severe liver disease. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand HCC phenotypes. The power of metabolomics allows an unparalleled opportunity to query the molecular mechanisms of HCC. This technique-driven review aims to demystify the metabolomics pathway, while also illustrating the potential of this technique, with recent examples of its application in HCC.

Mass spectrometry-based quantitative metabolomics revealed a distinct lipid profile in breast cancer patients

Breast cancer accounts for the largest number of newly diagnosed cases in female cancer patients. Although mammography is a powerful screening tool, about 20% of breast cancer cases cannot be detected by this method. New diagnostic biomarkers for breast cancer are necessary. Here, we used a mass spectrometry-based quantitative metabolomics method to analyze plasma samples from 55 breast cancer patients and 25 healthy controls. A number of 30 patients and 20 age-matched healthy controls were used as a training dataset to establish a diagnostic model and to identify potential biomarkers. The remaining samples were used as a validation dataset to evaluate the predictive accuracy for the established model. Distinct separation was obtained from an orthogonal partial least squares-discriminant analysis (OPLS-DA) model with good prediction accuracy. Based on this analysis, 39 differentiating metabolites were identified, including significantly lower levels of lysophosphatidylcholines and higher levels of sphingomyelins in the plasma samples obtained from breast cancer patients compared with healthy controls. Using logical regression, a diagnostic equation based on three metabolites (lysoPC a C16:0, PC ae C42:5 and PC aa C34:2) successfully differentiated breast cancer patients from healthy controls, with a sensitivity of 98.1% and a specificity of 96.0%.

The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans

The metabolic investigation of the human population is becoming increasingly important in the study of health and disease. The phenotypic variation can be investigated through the application of metabolomics; to provide a statistically robust investigation, the study of hundreds to thousands of individuals is required. In untargeted and MS-focused metabolomic studies this once provided significant hurdles. However, recent innovations have enabled the application of MS platforms in large-scale, untargeted studies of humans. Herein we describe the importance of experimental design, the separation of the biological study into multiple analytical experiments and the incorporation of QC samples to provide the ability to perform signal correction in order to reduce analytical variation and to quantitatively determine analytical precision. In addition, we describe how to apply this in quality assurance processes. These innovations have opened up the capabilities to perform routine, large-scale, untargeted, MS-focused studies.