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

Microbiota and metabolite alterations in pancreatic head and body/tail cancer patients

Pancreatic head cancer (PHC) and pancreatic body/tail cancer (PBTC) have distinct clinical and biological behaviors. The microbial and metabolic differences in PHC and PBTC have not been studied. The pancreatic microbiota and metabolome of 15 PHC and 8 PBTC tissues and their matched nontumor tissues were characterized using 16S rRNA amplicon sequencing and untargeted metabolomics. At the genus level, Bradyrhizobium was increased while Corynebacterium and Ruminococcus were decreased in the PHC tissues (Head T) compared with the matched nontumor tissues (Head N) significantly. Shuttleworthia, Bacillus, and Bifidobacterium were significantly decreased in the PBTC tissues (Body/Tail T) compared with the matched nontumor tissues (Body/Tail N). Significantly, Ileibacterium was increased whereas Pseudoxanthomonas was decreased in Head T and Body/Tail T, and Lactobacillus was increased in Head T but decreased in Body/Tail T. A total of 102 discriminative metabolites were identified between Head T and Head N, which were scattered through linoleic acid metabolism and purine metabolism pathways. However, there were only four discriminative metabolites between Body/Tail T and Body/Tail N, which were related to glycerophospholipid metabolism and autophagy pathways. The differential metabolites in PHC and PBTC were commonly enriched in alpha-linolenic acid metabolism and choline metabolism in cancer pathways. Eubacterium decreased in Head T was positively correlated with decreased linoleic acid while negatively correlated with increased arachidyl carnitine and stearoylcarnitine. Bacillus decreased in Body/Tail T was negatively correlated with increased L-carnitine. These microbiota and metabolites deserve further investigations to reveal their roles in the pathogenesis of PHC and PBTC, providing clues for future treatments.

Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer

Introduction

The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers.

Methods

We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions.

Results

Despite differences between both cohorts of controls, a set of 43 metabolites and lipids uniformly discriminated against breast cancer patients and healthy women. Moreover, smaller groups of female patients with other types of solid cancers (colorectal, head and neck, and lung cancers) were analyzed, which revealed a set of 42 metabolites and lipids that uniformly differentiated all three cancer types from both cohorts of healthy women. A common part of both sets, which could be called a multi-cancer signature, contained 23 compounds, which included reduced levels of a few amino acids (alanine, aspartate, glutamine, histidine, phenylalanine, and leucine/isoleucine), lysophosphatidylcholines (exemplified by LPC(18:0)), and diglycerides. Interestingly, a reduced concentration of the most abundant cholesteryl ester (CE(18:2)) typical for other cancers was the least significant in the serum of breast cancer patients. Components present in a multi-cancer signature enabled the establishment of a well-performing breast cancer classifier, which predicted cancer with a very high precision in independent groups of women (AUC>0.95).

Discussion

In conclusion, metabolites critical for discriminating breast cancer patients from controls included components of hypothetical multi-cancer signature, which indicated wider potential applicability of a general serum metabolome cancer biomarker.

Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning

OBJECTIVE

Rheumatoid arthritis and Ankylosing spondylitis are two common autoimmune inflammatory rheumatic diseases that negatively affect activities of daily living and can lead to structural and functional disability, reduced quality of life. Here, this study utilized Fourier transform infrared (FTIR) spectroscopy on dried serum samples and achieved early diagnosis of rheumatoid arthritis and ankylosing spondylitis based on deep learning models.

METHOD

A total of 243 dried serum samples were collected in this study, including 81 samples each from ankylosing spondylitis, rheumatoid arthritis, and healthy controls. Three multi-scale convolutional modules with different specifications were designed based on the multi-scale convolutional neural network (MSCNN) to effectively fuse the local features to enhance the generalization ability of the model. The FTIR was then combined with the MSCNN model to achieve a non-invasive, fast, and accurate diagnosis of ankylosing spondylitis, rheumatoid arthritis, and healthy controls.

RESULTS

Spectral analysis shows that the curves and waveforms of the three spectral graphs are similar. The main differences are distributed in the spectral regions of 3300-3250 cm-1, 3000-2800 cm-1, 1750-1500 cm-1, and 1500-1300 cm-1, which represent: Amides, fatty acids, cholesterol, proteins with a carboxyl group, amide II, free amino acids, and polysaccharides. Four classification models, namely artificial neural network (ANN), convolutional neural network (CNN), improved AlexNet model, and multi-scale convolutional neural network (MSCNN) were established. Through comparison, it was found that the diagnostic AUC value of the MSCNN model was 0.99, and the accuracy rate was as high as 0.93, which was much higher than the other three models.

CONCLUSION

The study demonstrated the superiority of MSCNN in distinguishing ankylosing spondylitis from rheumatoid arthritis and healthy controls. FTIR may become a rapid, sensitive, and non-invasive means of diagnosing rheumatism.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

Mass Spectrometry Based on Chemical Derivatization Has Brought Novel Discoveries to Lipidomics: A Comprehensive Review

Lipids, as one of the most important organic compounds in organisms, are important components of cells and participate in energy storage and signal transduction of living organisms. As a rapidly rising field, lipidomics research involves the identification and quantification of multiple classes of lipid molecules, as well as the structure, function, dynamics, and interactions of lipids in living organisms. Due to its inherent high selectivity and high sensitivity, mass spectrometry (MS) is the "gold standard" analysis technique for small molecules in biological samples. The combination chemical derivatization with MS detection is a unique strategy that could improve MS ionization efficiency, facilitate structure identification and quantitative analysis. Herein, this review discusses derivatization-based MS strategies for lipidomic analysis over the past decade and focuses on all the reported lipid categories, including fatty acids and modified fatty acids, glycerolipids, glycerophospholipids, sterols and saccharolipids. The functional groups of lipids mainly involved in chemical derivatization include the C=C group, carboxyl group, hydroxyl group, amino group, carbonyl group. Furthermore, representative applications of these derivatization-based lipid profiling methods were summarized. Finally, challenges and countermeasures of lipid derivatization are mentioned and highlighted to guide future studies of derivatization-based MS strategy in lipidomics.

Tumor Lipid Signatures Are Descriptive of Acquisition of Therapy Resistance in an Endocrine-Related Breast Cancer Mouse Model

The lipid metabolism adaptations of estrogen and progesterone receptor-positive breast cancer tumors from a mouse syngeneic model are investigated in relation to differences across the transition from hormone-dependent (HD) to hormone-independent (HI) tumor growth and the acquisition of endocrine therapy (ET) resistance (HIR tumors). Results are articulated with reported polar metabolome results to complete a metabolic picture of the above transitions and suggest markers of tumor progression and aggressiveness. Untargeted nuclear magnetic resonance metabolomics was used to analyze tumor and mammary tissue lipid extracts. Tumor progression (HD-HI-HIR) was accompanied by increased nonesterified cholesterol forms and phospholipids (phosphatidylcholine, phosphatidylethanolamine, sphingomyelins, and plasmalogens) and decreased relative contents of triglycerides and fatty acids. Predominating fatty acids became shorter and more saturated on average. These results were consistent with gradually more activated cholesterol synthesis, β-oxidation, and phospholipid biosynthesis to sustain tumor growth, as well as an increase in cholesterol (possibly oxysterol) forms. Particular compound levels and ratios were identified as potential endocrine tumor HD-HI-HIR progression markers, supporting new hypotheses to explain acquired ET resistance.

Metabolomics unravels subtype-specific characteristics related to neoadjuvant therapy response in breast cancer patients

INTRODUCTION

Breast cancer is the most diagnosed tumor and the leading cause of cancer death in women worldwide. Metabolomics allows the quantification of the entire set of metabolites in blood samples, making it possible to study differential metabolomics patterns related to neoadjuvant treatment in the breast cancer neoadjuvant setting.

OBJECTIVES

Characterizing metabolic differences in breast cancer blood samples according to their response to neoadjuvant treatment.

METHODS

One hundred and three plasma samples of breast cancer patients, before receiving neoadjuvant treatment, were analyzed through UPLC-MS/MS metabolomics. Then, metabolomics data were analyzed using probabilistic graphical models and biostatistics methods.

RESULTS

Metabolomics data allowed the identification of differences between groups according to response to neoadjuvant treatment. These differences were specific to each breast cancer subtype. Patients with HER2+ tumors showed differences in metabolites related to amino acids and carbohydrates pathways between the two pathological response groups. However, patients with triple-negative tumors showed differences in metabolites related to the long-chain fatty acids pathway. Patients with Luminal B tumors showed differences in metabolites related to acylcarnitine pathways.

CONCLUSIONS

It is possible to identify differential metabolomics patterns between complete and partial responses to neoadjuvant therapy, being this metabolomic profile specific for each breast cancer subtype.

Lipid Biomarkers in Liquid Biopsies: Novel Opportunities for Cancer Diagnosis

Altered cellular metabolism is a well-established hallmark of cancer. Although most studies have focused on the metabolism of glucose and glutamine, the upregulation of lipid metabolism is also frequent in cells undergoing oncogenic transformation. In fact, cancer cells need to meet the enhanced demand of plasma membrane synthesis and energy production to support their proliferation. Moreover, lipids are precursors of signaling molecules, termed lipid mediators, which play a role in shaping the tumor microenvironment. Recent methodological advances in lipid analysis have prompted studies aimed at investigating the whole lipid content of a sample (lipidome) to unravel the complexity of lipid changes in cancer patient biofluids. This review focuses on the application of mass spectrometry-based lipidomics for the discovery of cancer biomarkers. Here, we have summarized the main lipid alteration in cancer patients' biofluids and uncovered their potential use for the early detection of the disease and treatment selection. We also discuss the advantages of using biofluid-derived extracellular vesicles as a platform for lipid biomarker discovery. These vesicles have a molecular signature that is a fingerprint of their originating cells. Hence, the analysis of their molecular cargo has emerged as a promising strategy for the identification of sensitive and specific biomarkers compared to the analysis of the unprocessed biofluid.

Metabolomic profiles in night shift workers: A cross-sectional study on hospital female nurses

Background and aim

Shift work, especially including night shifts, has been found associated with several diseases, including obesity, diabetes, cancers, and cardiovascular, mental, gastrointestinal and sleep disorders. Metabolomics (an omics-based methodology) may shed light on early biological alterations underlying these associations. We thus aimed to evaluate the effect of night shift work (NSW) on serum metabolites in a sample of hospital female nurses.

Methods

We recruited 46 nurses currently working in NSW in Milan (Italy), matched to 51 colleagues not employed in night shifts. Participants filled in a questionnaire on demographics, lifestyle habits, personal and family health history and work, and donated a blood sample. The metabolome was evaluated through a validated targeted approach measuring 188 metabolites. Only metabolites with at least 50% observations above the detection limit were considered, after standardization and log-transformation. Associations between each metabolite and NSW were assessed applying Tobit regression models and Random Forest, a machine-learning algorithm.

Results

When comparing current vs. never night shifters, we observed lower levels of 21 glycerophospholipids and 6 sphingolipids, and higher levels of serotonin (+171.0%, 95%CI: 49.1-392.7), aspartic acid (+155.8%, 95%CI: 40.8-364.7), and taurine (+182.1%, 95%CI: 67.6-374.9). The latter was higher in former vs. never night shifters too (+208.8%, 95%CI: 69.2-463.3). Tobit regression comparing ever (i.e., current + former) and never night shifters returned similar results. Years worked in night shifts did not seem to affect metabolite levels. The Random-Forest algorithm confirmed taurine and aspartic acid among the most important variables in discriminating current vs. never night shifters.

Conclusions

This study, although based on a small sample size, shows altered levels of some metabolites in night shift workers. If confirmed, our results may shed light on early biological alterations that might be related to adverse health effects of NSW.

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

Metabolic differences among newborns born to mothers with a history of leukemia or lymphoma

BACKGROUND

Leukemia and lymphoma are cancers affecting children, adolescents, and young adults and may affect reproductive outcomes and maternal metabolism. We evaluated for metabolic changes in newborns of mothers with a history of these cancers.

METHODS

A cross-sectional study was conducted on California births from 2007 to 2011 with linked maternal hospital discharge records, birth certificate, and newborn screening metabolites. History of leukemia or lymphoma was determined using ICD-9-CM codes from hospital discharge data and newborn metabolite data from the newborn screening program.

RESULTS

A total of 2,068,038 women without cancer history and 906 with history of leukemia or lymphoma were included. After adjusting for differences in maternal age, infant sex, age at metabolite collection, gestational age, and birthweight, among newborns born to women with history of leukemia/lymphoma, several acylcarnitines were significantly (p < .001 - based on Bonferroni correction for multiple testing) higher compared to newborns of mothers without cancer history: C3-DC (mean difference (MD) = 0.006), C5-DC (MD = 0.009), C8:1 (MD = 0.008), C14 (MD = 0.010), and C16:1 (MD = 0.011), whereas citrulline levels were significantly lower (MD = -0.581) among newborns born to mothers with history of leukemia or lymphoma compared to newborns of mothers without a history of cancer.

CONCLUSION

The varied metabolite levels suggest history of leukemia or lymphoma has metabolic impact on newborn offspring, which may have implications for future metabolic consequences such as necrotizing enterocolitis and urea cycle enzyme disorders in children born to mothers with a history of leukemia or lymphoma.

Non-Targeted Serum Lipidomics Analysis and Potential Biomarkers of Laryngeal Cancer Based on UHPLC-QTOF-MS

Laryngeal cancer is a common head and neck malignant cancer type. However, effective biomarkers for diagnosis are lacking and pathogenesis is unclear. Lipidomics is a powerful tool for identifying biomarkers and explaining disease mechanisms. Hence, in this study, non-targeted lipidomics based on ultra-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-QTOF-MS) were applied to screen the differential lipid metabolites in serum and allowed for exploration of the remodeled lipid metabolism of laryngeal cancer, laryngeal benign tumor patients, and healthy crowds. Multivariate analysis and univariate analysis were combined to screen for differential lipid metabolites among the three groups. The results showed that, across a total of 57 lipid metabolic markers that were screened, the regulation of the lipid metabolism network occurred mainly in phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and sphingomyelin (SM) metabolism. Of note, the concentration levels of sphingolipids 42:2 (SM 42:2) and sphingolipids 42:3 (SM 42:3) correlated with laryngeal cancer progression and were both significantly different among the three groups. Both of them could be considered as potential biomarkers for diagnosis and indicators for monitoring the progression of laryngeal cancer. From the perspective of lipidomics, this study not only revealed the regulatory changes in the lipid metabolism network, but also provided a new possibility for screening biomarkers in laryngeal cancer.

In situ Detecting Lipids as Potential Biomarkers for the Diagnosis and Prognosis of Intrahepatic Cholangiocarcinoma

Purpose

To quantitatively analyze lipid molecules in tumors and adjacent tissues of intrahepatic cholangiocarcinoma (ICC), to establish diagnostic model and to examine lipid changes with clinical classification.

Patients and Methods

We measured the quantity of 202 lipid molecules in 100 tumor observation points and 100 adjacent observation points of patients who were diagnosed with ICC. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were handles, along with Student’s t-test to identify specific metabolites. Prediction accuracy was validated in the validation set. Another logistic regression model was also established on the training set and validated on the validation set.

Results

Distinct separation was obtained from PCA and OPLS-DA model. Ten differentiating metabolites were identified using PCA, OPLA-DA and Lasso regression: [m/z 722.5130], [m/z 863.5655], [m/z 436.2834], [m/z 474.2626], [m/z 661.4813], [m/z 750.5443], [m/z 571.2889], [m/z 836.5420], [m/z 772.5862] and [m/z 478.2939]. Using logical regression, a diagnostic equation: y = 3.4*[m/z 436.2834] - 3.773*[m/z 474.2626] + 3.82*[m/z 661.4813] - 4.394*[m/z 863.5655] + 10.165 based on four metabolites successfully differentiated cancerous areas from adjacent normal areas. The AUROC of the model reached 0.993 (95% CI: 0.985–0.999) in the validation group. Compared with the adjacent non-tumor area, three characteristic metabolites FA (22:4), PA (P-18:0/0:0) and Glucosylceramide (d18:1/12:0) showed an increasing trend from stage I to stage II, while seven other metabolites LPA(16:0), PE(34:2), PE(36:4), PE(38:3), PE(40:6), PE(40:5) and LPE(16:0) showed a decreasing trend from stage I to stage II.

Conclusion

We successfully identified lipid molecules in differentiating tumor tissue and adjacent tissue of ICC, established a discrimination logistic model which could be used as a classifier to classify tumor and non-tumor regions based on analysis in tumor margins and provided information for biomarker changes in ICC, and proposed to related lipid changes with clinical classification.

Melatonin Regulates the Daily Levels of Plasma Amino Acids, Acylcarnitines, Biogenic Amines, Sphingomyelins, and Hexoses in a Xenograft Model of Triple Negative Breast Cancer

Metabolic dysregulation as a reflection of specific metabolite production and its utilization is a common feature of many human neoplasms. Melatonin, an indoleamine that is highly available during darkness, has a variety of metabolic functions in solid tumors. Because plasma metabolites undergo circadian changes, we investigated the role of melatonin on the profile of amino acids (AAs), biogenic amines, carnitines, sphingolipids, and hexoses present in the plasma of mice bearing xenograft triple negative breast cancer (MDA-MB-231 cells) over 24 h. Plasma concentrations of nine AAs were reduced by melatonin, especially during the light phase, with a profile closer to that of non-breast cancer (BC) animals. With respect to acylcarnitine levels, melatonin reduced 12 out of 24 molecules in BC-bearing animals compared to their controls, especially at 06:00 h and 15:00 h. Importantly, melatonin reduced the concentrations of asymmetric dimethylarginine, carnosine, histamine, kynurenine, methionine sulfoxide, putrescine, spermidine, spermine, and symmetric dimethylarginine, which are associated with the BC metabolite sets. Melatonin also led to reduced levels of sphingomyelins and hexoses, which showed distinct daily variations over 24 h. These results highlight the role of melatonin in controlling the levels of plasma metabolites in human BC xenografts, which may impact cancer bioenergetics, in addition to emphasizing the need for a more accurate examination of its metabolomic changes at different time points.

Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning

Background

Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs.

Methods

Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers.

Findings

A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866.

Interpretation

The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput.

Funding

A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients

Background

The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non‐small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read‐out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning.

Methods

Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high‐resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy‐naïve patients with NSCLC (n = 85).

Results

The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716–8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004–4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445–1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977–1.550) were not independent prognostic factors. Using Kaplan‐Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non‐responders (P < 0.001).

Conclusions

Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology‐based assessment of tumor response.

Endocrine Therapy-Resistant Breast Cancer Cells Are More Sensitive to Ceramide Kinase Inhibition and Elevated Ceramide Levels Than Therapy-Sensitive Breast Cancer Cells

Simple Summary

Endocrine therapy (ET) resistance is a major problem in estrogen receptor-positive breast cancer patients. Since there have been few lipidomic studies in ET resistance and sphingolipids are heavily implicated in multidrug-resistant and chemotherapy-resistant cancers, we aimed to investigate the sphingolipidome of tamoxifen-resistant breast cancer cells in search of a unique sphingolipid profile that can potentially be exploited therapeutically. We found that ET-resistant breast cancer cells maintain a lower level of ceramides for their survival. In order to achieve this, they are dependent on ceramide kinase (CERK), the activity of which helps maintain low endogenous ceramide levels, therefore promoting tamoxifen-resistant cell survival. Targeting CERK can therefore represent an opportunity to target therapy-resistant breast tumors and improve the patient outcome for women with ET-resistant disease.

Abstract

ET resistance is a critical problem for estrogen receptor-positive (ER+) breast cancer. In this study, we have investigated how alterations in sphingolipids promote cell survival in ET-resistant breast cancer. We have performed LC-MS-based targeted sphingolipidomics of tamoxifen-sensitive and -resistant MCF-7 breast cancer cell lines. Follow-up studies included treatments of cell lines and patient-derived xenograft organoids (PDxO) with small molecule inhibitors; cytometric analyses to measure cell death, proliferation, and apoptosis; siRNA-mediated knockdown; RT-qPCR and Western blot for gene and protein expression; targeted lipid analysis; and lipid addback experiments. We found that tamoxifen-resistant cells have lower levels of ceramides and hexosylceramides compared to their tamoxifen-sensitive counterpart. Upon perturbing the sphingolipid pathway with small molecule inhibitors of key enzymes, we identified that CERK is essential for tamoxifen-resistant breast cancer cell survival, as well as a fulvestrant-resistant PDxO. CERK inhibition induces ceramide-mediated cell death in tamoxifen-resistant cells. Ceramide-1-phosphate (C1P) partially reverses CERK inhibition-induced cell death in tamoxifen-resistant cells, likely through lowering endogenous ceramide levels. Our findings suggest that ET-resistant breast cancer cells maintain lower ceramide levels as an essential pro-survival mechanism. Consequently, ET-resistant breast cancer models have a unique dependence on CERK as its activity can inhibit de novo ceramide production.

Multi-omics reveals host metabolism associated with the gut microbiota composition in mice with dietary ε-polylysine

This study aimed to assess the influence of dietary supplementation of ε-polylysine on the gut microbiota and host nutrient metabolism, which is not systematically discussed by multi-omics analysis. A total of 40 mice were randomly divided into two groups exposed to either a basal diet (AIN-76A) or a basal diet with 150 ppm ε-polylysine. Fecal samples were collected for gut bacteria identification. Liver and plasma samples were collected for metabolomic and proteomic analyses. The results showed that ε-polylysine decreased the body weight of mice and affected the presence of certain types of intestinal microorganisms. The richness of the microbiota and number of phyla increased with age. ε-Polylysine affected the presence of genera and species, and either regulated or took part in the metabolism of energy, nitrogen, amino acids, lipids, carbohydrates, glycans, cofactors, and vitamins. The metabolite profiling showed that lipid and lipid-like molecules metabolites occupied the majority percent of plasma and liver metabolites. Additionally, ε-polylysine regulated the key role of metabolites and related metabolic enzymes in the metabolic pathways, especially phospholipid metabolism. In conclusion, dietary ε-polylysine improved the immunity of growing mice, and had a greater effect on the anabolism of nutrients in adult mice.

Metabolomic Profiling of Blood-Derived Microvesicles in Breast Cancer Patients

Malignant cells differ from benign ones in their metabolome and it is largely unknown whether this difference is reflected in the metabolic profile of their microvesicles (MV), which are secreted into the blood of cancer patients. Here, they are present together with MV from the various blood and endothelial cells. Harvesting MV from 78 breast cancer patients (BC) and 30 controls, we characterized the whole blood MV metabolome using targeted and untargeted mass spectrometry. Especially (lyso)-phosphatidylcholines and sphingomyelins were detected in a relevant abundance. Eight metabolites showed a significant discriminatory power between BC and controls. High concentrations of lysoPCaC26:0 and PCaaC38:5 were associated with shorter overall survival. Comparing BC subtype-specific metabolome profiles, 24 metabolites were differentially expressed between luminal A and luminal B. Pathway analysis revealed alterations in the glycerophospholipid metabolism for the whole cancer cohort and in the ether lipid metabolism for the molecular subtype luminal B. Although this mixture of blood-derived MV contains only a minor number of tumor MV, a combination of metabolites was identified that distinguished between BC and controls as well as between molecular subtypes, and was predictive for overall survival. This suggests that these metabolites represent promising biomarkers and, moreover, that they may be functionally relevant for tumor progression.

MCF-7 Drug Resistant Cell Lines Switch Their Lipid Metabolism to Triple Negative Breast Cancer Signature

Obesity and adipose tissue have been closely related to a poor cancer prognosis, especially in prostate and breast cancer patients. The ability of transferring lipids from the adipose tissue to the tumor cells is actively linked to tumor progression. However, different types of breast tumor seem to use these lipids in different ways and metabolize them in different pathways. In this study we have tracked by mass spectrometry how palmitic acid from the adipocytes is released to media being later incorporated in different breast cancer cell lines (MDA-MB-231, SKBR3, BT474, MCF-7 and its resistant MCF-7 EPI^R and MCF-7 TAX^R). We have observed that different lines metabolize the palmitic acid in a different way and use their carbons in the synthesis of different new lipid families. Furthermore, we have observed that the lipid synthesis pattern varied according to the cell line. Surprisingly, the metabolic pattern of the resistant cells was more related to the TNBC cell line compared to their sensitive cell line MCF-7. These results allow us to determine a specific lipid pattern in different cell lines that later might be used in breast cancer diagnosis and to find a better treatment according to the cancer molecular type.

Lipidomic Analysis of Archival Pathology Specimens Identifies Altered Lipid Signatures in Ovarian Clear Cell Carcinoma

Cancer metabolism is associated with the enhanced lipogenesis required for rapid growth and proliferation. However, the magnitude of dysregulation of diverse lipid species still requires significant characterization, particularly in ovarian clear cell carcinoma (OCCC). Here, we have implemented a robust sample preparation workflow together with targeted LC-MS/MS to identify the lipidomic changes in formalin-fixed paraffin-embedded specimens from OCCC compared to tumor-free ovarian tissue. We quantitated 340 lipid species, representing 28 lipid classes. We observed differential regulation of diverse lipid species belonging to several glycerophospholipid classes and trihexosylceramide. A number of unsaturated lipid species were increased in OCCC, whereas saturated lipid species showed a decrease in OCCC compared to the controls. We also carried out total fatty acid analysis and observed an increase in the levels of several unsaturated fatty acids with a concomitant increase in the index of stearoyl-CoA desaturase (SCD) in OCCC. We confirmed the upregulation of SCD (the rate-limiting enzyme for the synthesis of monounsaturated fatty acids) by immunohistochemistry (IHC) assays. Hence, by carrying out a mass spectrometry analysis of archival tissue samples, we were able to provide insights into lipidomic alterations in OCCC.

Untargeted metabolomics of newborn dried blood spots reveals sex-specific associations with pediatric acute myeloid leukemia

The etiology of pediatric acute myeloid leukemia (AML) is largely unknown, but evidence for mutations in utero and long latency periods suggests that environmental factors play a role. Therefore, we used untargeted metabolomics of archived newborn dried blood spots (DBS) to investigate neonatal exposures as potential causal risk factors for AML. Untargeted metabolomics profiling was performed on DBS punches from 48 pediatric patients with AML and 46 healthy controls as part of the California Childhood Leukemia Study (CCLS). Because sex disparities are suggested by differences in AML incidence rates, metabolite features associated with AML were identified in analyses stratified by sex. There was no overlap between the 16 predictors of AML in females and 15 predictors in males, suggesting that neonatal metabolomic profiles of pediatric AML risk are sex-specific. In females, four predictors of AML were putatively annotated as ceramides, a class of metabolites that has been linked with cancer cell proliferation. In females, two metabolite predictors of AML were strongly correlated with breastfeeding duration, indicating a possible biological link between this putative protective risk factor and childhood leukemia. In males, a heterogeneous metabolite profile of AML predictors was observed. Replication with larger participant numbers is required to validate findings.

Multi-Omics Analysis of Brain Metastasis Outcomes Following Craniotomy

Background

The incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death.

Methods

We executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization.

Results

We discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference.

Conclusion

A multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.

Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring.

Rhodiola crenulata extract decreases fatty acid oxidation and autophagy to ameliorate pulmonary arterial hypertension by targeting inhibiton of acylcarnitine in rats

Pulmonary arterial hypertension (PAH) is a devastating pulmonary circulation disease lacking high-efficiency therapeutics. The present study aims to decipher the therapeutic mechanism of Rhodiola crenulata, a well-known traditional chinese medicine with cardiopulmonary protection capacity, on PAH by exploiting functional lipidomics. The rat model with PAH was successfully established for first, following Rhodiola crenulata water extract (RCE) treatment, then analysis of chemical constituents of RCE was performed, additional morphologic, hemodynamic, echocardiographic measurements were examined, further targeted lipidomics assay was performed to identify differential lipidomes, at last accordingly mechanism assay was done by combining qRT-PCR, Western blot and ELISA. Differential lipidomes were identified and characterized to differentiate the rats with PAH from healthy controls, mostly assigned to acylcarnitines, phosphatidylcholines, sphingomyelin associated with the PAH development. Excitingly, RCE administration reversed high level of decadienyl-L-carnitine by the modulation of metabolic enzyme CPT1A in mRNA and protein level in serum and lung in the rats with PAH. Furthermore, RCE was observed to reduce autophagy, confirmed by significantly inhibited PPARγ, LC3B, ATG7 and upregulated p62, and inactivated LKB1-AMPK signal pathway. Notably, we accurately identified the constituents in RCE, and delineated the therapeutic mechansim that RCE ameliorated PAH through inhibition of fatty acid oxidation and autophagy. Altogether, RCE might be a potential therapeutic medicine with multi-targets characteristics to prevent the progression of PAH. This novel findings pave a critical foundation for the use of RCE in the treatment of PAH.

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

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

Enhancing detection and characterization of lipids using charge manipulation in electrospray ionization-tandem mass spectrometry

Heightened awareness regarding the implication of disturbances in lipid metabolism with respect to prevalent human-related pathologies demands analytical techniques that provide unambiguous structural characterization and accurate quantitation of lipids in complex biological samples. The diversity in molecular structures of lipids along with their wide range of concentrations in biological matrices present formidable analytical challenges. Modern mass spectrometry (MS) offers an unprecedented level of analytical power in lipid analysis, as many advancements in the field of lipidomics have been facilitated through novel applications of and developments in electrospray ionization tandem mass spectrometry (ESI-MS/MS). ESI allows for the formation of intact lipid ions with little to no fragmentation and has become widely used in contemporary lipidomics experiments due to its sensitivity, reproducibility, and compatibility with condensed-phase modes of separation, such as liquid chromatography (LC). Owing to variations in lipid functional groups, ESI enables partial chemical separation of the lipidome, yet the preferred ion-type is not always formed, impacting lipid detection, characterization, and quantitation. Moreover, conventional ESI-MS/MS approaches often fail to expose diverse subtle structural features like the sites of unsaturation in fatty acyl constituents or acyl chain regiochemistry along the glycerol backbone, representing a significant challenge for ESI-MS/MS. To overcome these shortcomings, various charge manipulation strategies, including charge-switching, have been developed to transform ion-type and charge state, with aims of increasing sensitivity and selectivity of ESI-MS/MS approaches. Importantly, charge manipulation approaches afford enhanced ionization efficiency, improved mixture analysis performance, and access to informative fragmentation channels. Herein, we present a critical review of the current suite of solution-based and gas-phase strategies for the manipulation of lipid ion charge and type relevant to ESI-MS/MS.

Lipid and protein tumor markers for head and neck squamous cell carcinoma identified by imaging mass spectrometry

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. To improve pre- and post-operative diagnosis and prognosis novel molecular markers are desirable. Here we used MALDI imaging mass spectrometry (IMS) and immunohistochemistry (IHC) to seek tumor specific expression of proteins and lipids in HNSCC samples. Among low molecular weight proteins visualized, S100A8 and S100A9 were found to be expressed in the regions of tumor tissue but not in the surrounding healthy stroma of a post-operative microdissected tissue. Marker potential of S100A8 and S100A9 was confirmed by immunohistochemistry of paraffin-embedded pathological samples. Imaging lipids showed a remarkable depletion of lysophosphatidylcholine species LPC[16:0], LPC[18:2] and, in parallel, accumulation of major glycerophospholipid species PE-P[36:4], PC[32:1], PC[34:1] in neoplastic areas. This was confirmed by shotgun lipidomics of dissected healthy and tumor tissue sections. A combination of the negative (LPC[16:0]) and positive (PC[32:1], PC[34:1]) markers was also applicable to uncover tumorous character of a pre-operative biopsy. Furthermore, marker potential of lysophospholipids was supported by elevated expression levels of the lysophospholipid degrading enzyme lysophospholipase A1 (LYPLA1) in the tumor regions of paraffin-embedded HNSCC samples. Finally, experimental evidence of 3D cell spheroid tests showed that LPC[16:0] facilitates HNSCC invasion, implying that HNSCC progression in vivo may be dependent on lysophospholipid supply. Altogether, a series of novel proteins and lipid species were identified by IMS and IHC screening, which may serve as potential molecular markers for tumor diagnosis, prognosis, and may pave the way to better understand HNSCC pathophyisiology.

Estimating the magnitude of clinical benefit from (neo)adjuvant chemotherapy in patients with ER-positive/HER2-negative breast cancer

Gene-expression assays were originally validated retrospectively as tools of prognostication, with evidence emerging from more recent prospectively-conducted studies such as MINDACT and TAILORx supporting their clinical validity and utility as biomarkers in identifying patients with luminal breast cancer who might be spared chemotherapy. However, these assays still do not have the ability to identify all patients who may safely avoid chemotherapy, and may over-estimate the risk of relapse in some cases. Future studies should aim to prospectively integrate contemporary approaches that assume a theoretical risk of relapse (based on pathological and/or genomic evaluation of the primary tumour), with new tools that can detect signals of active micro-metastatic disease. Until current methods of estimating prognosis and predicting benefit from adjuvant chemotherapy are significantly refined, estimating and improving the true magnitude of benefit derived from chemotherapy remains a challenge for clinicians.

Multiplatform Investigation of Plasma and Tissue Lipid Signatures of Breast Cancer Using Mass Spectrometry Tools

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

Structural Elucidation of Ether Glycerophospholipids Using Gas-Phase Ion/Ion Charge Inversion Chemistry

Ether lipids represent a unique subclass of glycerophospholipid (GPL) that possesses a 1-O-alkyl (i.e., plasmanyl subclass) or a 1-O-alk-1'-enyl (i.e., plasmenyl subclass) group linked at the sn-1 position of the glycerol backbone. As changes in ether GPL composition and abundance are associated with numerous human pathologies, analytical strategies capable of providing high-level structural detail are desirable. While mass spectrometry (MS) has emerged as a prominent tool for lipid structural elucidation in biological extracts, distinctions between the various isomeric forms of ether-linked GPLs have remained a significant challenge for tandem MS, principally due to similarities in the conventional tandem mass spectra obtained from the two ether-linked subclasses. To distinguish plasmanyl and plasmenyl GPLs, a multistage (i.e., MSn where n = 3 or 4) mass spectrometric approach reliant on low-energy collision-induced dissociation (CID) is required. While this method facilitates assignment of the sn-1 bond type (i.e., 1-O-alkyl versus 1-O-alk-1'-enyl), a composite distribution of isomers is left unresolved, as carbon-carbon double-bond (C=C) positions cannot be localized in the sn-2 fatty acyl substituent. In this study, we combine a systematic MSn approach with two unique gas-phase charge inversion ion/ion chemistries to elucidate ether GPL structures with high-level detail. Ultimately, we assign both the sn-1 bond type and sites of unsaturation in the sn-2 fatty acyl substituent using an entirely gas-phase MS-based workflow. Application of this workflow to human blood plasma extract permitted isomeric resolution and in-depth structural identification of major and, in some cases, minor isomeric contributors to ether GPLs that have been previously unresolved when examined via conventional methods.

Lipidomic study of cell lines reveals differences between breast cancer subtypes

Breast cancer (BC) is the most prevalent type of cancer in women in western countries. BC mortality has not declined despite early detection by screening, indicating the need for better informed treatment decisions. Therefore, a novel noninvasive diagnostic tool for BC would give the opportunity of subtype-specific treatment and improved prospects for the patients. Heterogeneity of BC tumor subtypes is reflected in the expression levels of enzymes in lipid metabolism. The aim of the study was to investigate whether the subtype defined by the transcriptome is reflected in the lipidome of BC cell lines. A liquid chromatography mass spectrometry (LC-MS) platform was applied to analyze the lipidome of six cell lines derived from human BC cell lines representing different BC subtypes. We identified an increased abundance of triacylglycerols (TG) ≥ C-48 with moderate or multiple unsaturation in fatty acyl chains and down-regulated ether-phosphatidylethanolamines (PE) (C-34 to C-38) in cell lines representing estrogen receptor and progesterone receptor positive tumor subtypes. In a cell line representing HER2-overexpressing tumor subtype an elevated expression of TG (≤ C-46), phosphatidylcholines (PC) and PE containing short-chained (≤ C-16) saturated or monounsaturated fatty acids were observed. Increased abundance of PC ≥ C-40 was found in cell lines of triple negative BC subtype. In addition, differences were detected in lipidomes within these previously defined subtypes. We conclude that subtypes defined by the transcriptome are indeed reflected in differences in the lipidome and, furthermore, potentially biologically relevant differences may exist within these defined subtypes.

Prepregnant Obesity of Mothers in a Multiethnic Cohort Is Associated with Cord Blood Metabolomic Changes in Offspring

Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal prepregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using a coupled untargeted and targeted metabolomic approach from the newborn cord blood metabolomes associated with a matched maternal prepregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multiethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using an elastic net regularization-based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age, ethnicity, parity, and gravidity). We validated the model results in a subsequent set of samples (N = 30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese and normal-weight groups. Interestingly, hydroxy-3-methylbutyric acid showed significantly higher levels in cord blood from the NHPI group compared to that from Asian and Caucasian groups. In summary, significant associations were observed between maternal prepregnant obesity and offspring metabolomic alternation at birth, revealing the intergenerational impact of maternal obesity.

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.

Metabolomics-Based Discovery of Molecular Signatures for Triple Negative Breast Cancer in Asian Female Population

Triple negative breast cancer (TNBC) is a devastating cancer disease characterized by its poor prognosis, distinct metastatic patterns, and aggressive biological behavior. Research indicates that the prevalence and presentation of TNBC varies among races, with Asian TNBC patients more commonly presenting with large invasive tumors, high node positivity, and high histologic grade. In this work, we applied ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomics to discover metabolic signatures in Asian female TNBC patients. Serum samples from 31 TNBC patients and 31 healthy controls (CN) were involved in this study. A total of 2860 metabolic features were detected in the serum samples. Among them, 77 metabolites, whose levels were significantly different between TNBC with CN, were confirmed. Using multivariate statistical analysis, literature mining, metabolic network and pathway analysis, we performed an in-depth study of the metabolic alterations in the Asian TNBC population. In addition, we discovered a panel of metabolic signatures that are highly correlated with the 5-year survival rate of the TNBC patients. This metabolomic study provides a better understanding of the metabolic details of TNBC in the Asian population.

Toward Complete Structure Elucidation of Glycerophospholipids in the Gas Phase through Charge Inversion Ion/Ion Chemistry

Shotgun lipidomics has recently gained popularity for lipid analysis. Conventionally, shotgun analysis of glycerophospholipids via direct electrospray ionization tandem mass spectrometry (ESI-MS/MS) provides glycerophospholipid (GPL) class (i.e., headgroup composition) and fatty acyl composition. Reliant on low-energy collision-induced dissociation (CID), traditional ESI-MS/MS fails to define fatty acyl regiochemistry along the glycerol backbone or carbon-carbon double bond position(s) in unsaturated fatty acyl substituents. Therefore, isomeric GPLs are often unresolved, representing a significant challenge for shotgun-MS approaches. We developed a top-down shotgun-MS method utilizing gas-phase ion/ion charge inversion chemistry that provides near-complete GPL structural identification. First, in negative ion mode, CID of mass-selected GPL anions generates fatty acyl carboxylate anions via fragmentation of ester bonds linking the fatty acyl substituents at the sn-1 and sn-2 positions of the glycerol backbone. Product anions, including fatty acyl carboxylate ions, were then derivatized in the mass spectrometer via an ion/ion charge inversion reaction with tris-phenanthroline magnesium dications. Subsequent CID of charge-inverted fatty acyl complex cations yielded isomer-specific product ion spectra that permit (i) unambiguous assignment of carbon-carbon double bond position(s) and (ii) relative quantitation of isomeric fatty acyl substituents. The outlined strategy was applied to the analysis of targeted GPLs extracted from human plasma, including several proposed plasma biomarkers. A single experiment thus facilitates assignment of the GPL headgroup, fatty acyl composition, carbon-carbon double bond position(s) in unsaturated fatty acyl chains, and, in some cases, fatty acyl sn-position and relative abundances for isomeric fatty acyl substituents. Ultimately, this MSn platform paired with ion/ion chemistry permitted identification of major, and some minor, isomeric contributors that are unresolved using conventional ESI-MS/MS.

Serum Lipidomic Profiling in Breast Cancer to Identify Screening, Diagnostic, and Prognostic Biomarkers

Breast cancer is the major mortality cause of women worldwide. In the course of management of breast cancer, the identification of a biomarker is important in enhancing our knowledge on cancer pathology, predicting the response to treatment, and selecting the patients who are more favorable to receive certain treatments. These biomarkers have a prognostic value. In addition to traditional breast cancer prognosis factors such as the tumor size and grade, the axillary lymph node micrometastasis, and biomarkers such as HER2/neu, newly discovered biomarkers have been discovered. Some of these factors are genetic signature in tissue or in peripheral blood. Lipid profil, a simple and accessible biological examination, has been a novel path on the prediction of breast cancer risk of occurrence and recurrence in many studies. The main goal of our review is to evaluate lipid profile and breast cancer risk with an emphasis on the prognosis value of lipid profiles in breast cancer patient management.

Plasma metabolites as possible biomarkers for diagnosis of breast cancer

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

Phospholipids are A Potentially Important Source of Tissue Biomarkers for Hepatocellular Carcinoma: Results of a Pilot Study Involving Targeted Metabolomics

Background: Hepatocellular carcinoma (HCC) pathogenesis involves the alteration of multiple liver-specific metabolic pathways. We systematically profiled cancer- and liver-related classes of metabolites in HCC and adjacent liver tissues and applied supervised machine learning to compare their potential yield for HCC biomarkers. Methods: Tumor and corresponding liver tissue samples were profiled as follows: Bile acids by ultra-performance liquid chromatography (LC) coupled to tandem mass spectrometry (MS), phospholipids by LC-MS/MS, and other small molecules including free fatty acids by gas chromatography—time of flight MS. The overall classification performance of metabolomic signatures derived by support vector machine (SVM) and random forests machine learning algorithms was then compared across classes of metabolite. Results: For each metabolite class, there was a plateau in classification performance with signatures of 10 metabolites. Phospholipid signatures consistently showed the highest discrimination for HCC followed by signatures derived from small molecules, free fatty acids, and bile acids with area under the receiver operating characteristic curve (AUC) values of 0.963, 0.934, 0.895, 0.695, respectively, for SVM-generated signatures comprised of 10 metabolites. Similar classification performance patterns were observed with signatures derived by random forests. Conclusion: Membrane phospholipids are a promising source of tissue biomarkers for discriminating between HCC tumor and liver tissue.

Metabolic profiling of serum and tissue from the rotator interval and anterior capsule in shoulder stiffness: a preliminary study

Background

Using mass spectrometry, we evaluated the metabolic profiles of patients who had rotator cuff tears with shoulder stiffness, or shoulder stiffness only, and compared these with samples from a control group.

Methods

This study enrolled 28 patients, including 10 patients with shoulder stiffness only (group I), nine patients with rotator cuff tear and stiffness (group II), and nine controls selected from patients diagnosed with impingement syndrome or long head of the biceps lesions without evident limitation of joint motion or rotator cuff tears. Serum and tissue from the rotator interval and anterior capsule were collected. In all, 82 samples were analyzed for metabolite profiling using the AbsoluteIDQ™p180 Kit.

Results

Comparison of 186 metabolites revealed that groups I and II had significantly higher concentrations of sphingolipids in serum (SM C24:1; group I = 65.16 μm, group II = 68.07 μm) than controls (55.37 μm, p = 0.005 & 0.015, respectively). Higher concentrations of sphingolipids were also present in the rotator interval tissue (SM C22:3) of groups 1 (0.0197 μm) and 2 (0.0144 μm) than controls (0.0081 μm, p = 0.012 & 0.014, respectively). The concentration of glycerophospholipid (PC aa C30:0) was higher in the anterior capsule tissue of groups I (0.850 μm) and II (1.164 μm) than controls (0.572 μm; p = 0.007) Total cholesterol was positively correlated with sphingolipid concentration in serum (SM C24:1, rho = 0.782, p = 0.008) and rotator interval tissue (SM C22:3, rho = 0.750, p = 0.017). There was no significant difference in the metabolites evaluated in groups I and II.

Conclusion

Metabolic profiling showed that levels of lipid-related metabolites were increased in the anterior capsule tissue and rotator interval tissue of patients with shoulder stiffness. Sphingomyelin (SM C22:3) in the tissue of the rotator interval was positively correlated with the serum level of total cholesterol in patients with shoulder stiffness only. The level of glycerophospholipid (PC30:0) in the anterior capsule was positively correlated with the serum level of total cholesterol in patients who had rotator cuff tear with shoulder stiffness. The results indicate that serum total cholesterol may be related to shoulder stiffness. Future studies are needed to evaluate the role of serum cholesterol in the pathogenesis of shoulder stiffness.

Trial registration

KC12OISI0532. Registered Nov 15, 2012. approval by the Institutional Review Board of Seoul St. Mary’s Hospital, the Catholic University of Korea.

Deep Metabolomics of a High-Grade Serous Ovarian Cancer Triple-Knockout Mouse Model

High-grade serous carcinoma (HGSC) is the most common and deadliest ovarian cancer (OC) type, accounting for 70-80% of OC deaths. This high mortality is largely due to late diagnosis. Early detection is thus crucial to reduce mortality, yet the tumor pathogenesis of HGSC remains poorly understood, making early detection exceedingly difficult. Faithfully and reliably representing the clinical nature of human HGSC, a recently developed triple-knockout (TKO) mouse model offers a unique opportunity to examine the entire disease spectrum of HGSC. Metabolic alterations were investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to serum samples collected from these mice at premalignant, early, and advanced stages of HGSC. This comprehensive analysis revealed a panel of 29 serum metabolites that distinguished mice with HGSC from controls and mice with uterine tumors with over 95% accuracy. Meanwhile, our panel could further distinguish early-stage HGSC from controls with 100% accuracy and from advanced-stage HGSC with over 90% accuracy. Important identified metabolites included phospholipids, sphingomyelins, sterols, N-acyltaurine, oligopeptides, bilirubin, 2(3)-hydroxysebacic acids, uridine, N-acetylneuraminic acid, and pyrazine derivatives. Overall, our study provides insights into dysregulated metabolism associated with HGSC development and progression, and serves as a useful guide toward early detection.

Serum lipidome screening in patients with stage I non-small cell lung cancer

The ability of early lung cancer diagnosis is an unmet need in clinical practice. Lung cancer metabolomic analyses conducted so far have demonstrated several abnormalities in cancer lipid profile providing a rationale for further study of blood lipidome of the patients. In the present research, we performed a targeted lipidome screening to select molecules that show promise for early lung cancer detection. The study was conducted on serum samples collected from newly diagnosed, stage I non-small cell lung cancer (NSCLC) patients and non-cancer controls. A high-throughput mass spectrometry-based platform with confirmed interlaboratory reproducibility was used. The analyzed profile consisted of acylcarnitines, sphingomyelins, phosphatidylcholines and lysophosphatidylcholines. Among the assayed lipid species, the significant differences between NSCLC and non-cancer subjects were observed in the group of phosphatidylcholines (PC) and lysophosphatidylcholines (lysoPC), especially in the levels of lysoPC a C26:0; lysoPC a C26:1; PC aa C42:4; and PC aa C34:4. The metabolites mentioned above were used to create a multivariate classification model, which reliability was proved by permutation tests as well as external validation. Our study indicated choline-containing phospholipids as potential lung cancer markers. Further investigations of phospholipidome are crucial to better describe the shifts in metabolite composition occurring in lung cancer patients.

Current Status and Future Prospects of Clinically Exploiting Cancer-specific Metabolism-Why Is Tumor Metabolism Not More Extensively Translated into Clinical Targets and Biomarkers?

Tumor cells exhibit a specialized metabolism supporting their superior ability for rapid proliferation, migration, and apoptotic evasion. It is reasonable to assume that the specific metabolic needs of the tumor cells can offer an array of therapeutic windows as pharmacological disturbance may derail the biochemical mechanisms necessary for maintaining the tumor characteristics, while being less important for normally proliferating cells. In addition, the specialized metabolism may leave a unique metabolic signature which could be used clinically for diagnostic or prognostic purposes. Quantitative global metabolic profiling (metabolomics) has evolved over the last two decades. However, despite the technology’s present ability to measure 1000s of endogenous metabolites in various clinical or biological specimens, there are essentially no examples of metabolomics investigations being translated into actual utility in the cancer clinic. This review investigates the current efforts of using metabolomics as a tool for translation of tumor metabolism into the clinic and further seeks to outline paths for increasing the momentum of using tumor metabolism as a biomarker and drug target opportunity.