High-throughput and high-sensitivity quantitative analysis of serum unsaturated fatty acids by chip-based nanoelectrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry: early stage diagnostic biomarkers of pancreatic cancer

Biomarkers for pancreatic cancer based on tissue and serum metabolomics analysis in a multicenter study

BACKGROUND

Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis.

METHODS

We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis.

RESULTS

Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr (odds ratio = 2.17, [95% confidence interval 1.34-3.53]). The three markers showed area under the receiver-operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19-9 (CA19-9) was added to the model.

CONCLUSION

The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19-9.

Novel Stable Isotope-Resolved Metabolomics Method for a Small Number of Cells Using Chip-Based Nanoelectrospray Mass Spectrometry

Stable isotope-resolved metabolomics (SIRM) can provide metabolic conversion information of specific targets; it is a powerful tool for cell metabolism studies. The common analytical platform for SIRM is chromatography-mass spectrometry, which requires a large number of cells and is not suitable for precious rare cell analysis. To study a small number of cells, we established a novel SIRM method using chip-based nanoelectrospray mass spectrometry (MS). ¹³C-glutamine was taken as an example; the unlabeled and ¹³C-labeled cells were cultured and extracted in a 96-well plate and then directly injected into MS and analyzed in full scan mode and parallel reaction monitoring (PRM) mode targeting 44 glutamine-derived metabolites and their isotopologues. To define focused metabolite-related MS2 fragments produced in the PRM, a new strategy was proposed including MS2 exact m/z matching, MS2 false positive filtering, and MS2 fragment grouping to remove the interfering MS2 ions. In total, 292 and 349 pairs of paired MS2 ions were obtained in positive and negative ionization modes, respectively. By searching spectra databases, 31 targeted metabolites with their isotopologues were identified and their characteristic product ions were confirmed for MS2 quantification. The relative quantification was achieved by MS2 quantification, which showed better sensitivity and accuracy than common MS1-based quantification. Finally, this method was applied to isocitrate dehydrogenase I-mutated glioma cells for revealing the effects of triptolide on glioma cell metabolism using U-¹³C-glutamine as a labeling substrate.

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers: A Systematic Review

Introduction

Detecting upper gastrointestinal (GI) cancers in primary care is challenging, as cancer symptoms are common, often non-specific, and most patients presenting with these symptoms will not have cancer. Substantial investment has been made to develop biomarkers for cancer detection, but few have reached routine clinical practice. We aimed to identify novel biomarkers for upper GI cancers which have been sufficiently validated to be ready for evaluation in low-prevalence populations.

Methods

We systematically searched MEDLINE, Embase, Emcare, and Web of Science for studies published in English from January 2000 to October 2019 (PROSPERO registration CRD42020165005). Reference lists of included studies were assessed. Studies had to report on second measures of diagnostic performance (beyond discovery phase) for biomarkers (single or in panels) used to detect pancreatic, oesophageal, gastric, and biliary tract cancers. We included all designs and excluded studies with less than 50 cases/controls. Data were extracted on types of biomarkers, populations and outcomes. Heterogeneity prevented pooling of outcomes.

Results

We identified 149 eligible studies, involving 22,264 cancer cases and 49,474 controls. A total of 431 biomarkers were identified (183 microRNAs and other RNAs, 79 autoantibodies and other immunological markers, 119 other proteins, 36 metabolic markers, 6 circulating tumour DNA and 8 other). Over half (n = 231) were reported in pancreatic cancer studies. Only 35 biomarkers had been investigated in at least two studies, with reported outcomes for that individual marker for the same tumour type. Apolipoproteins (apoAII-AT and apoAII-ATQ), and pepsinogens (PGI and PGII) were the most promising biomarkers for pancreatic and gastric cancer, respectively.

Conclusion

Most novel biomarkers for the early detection of upper GI cancers are still at an early stage of matureness. Further evidence is needed on biomarker performance in low-prevalence populations, in addition to implementation and health economic studies, before extensive adoption into clinical practice can be recommended.

Electronic Supplementary Material

The online version of this article (10.1007/s12325-020-01571-z) contains supplementary material, which is available to authorized users.

Mass spectrometry-based metabolomics in health and medical science: a systematic review

Metabolomics is the study of the investigation of small molecules derived from cellular and organism metabolism, which reflects the outcomes of the complex network of biochemical reactions in living systems. As the most recent member of the omics family, there has been notable progress in metabolomics in the last decade, mainly driven by the improvement in mass spectrometry (MS). MS-based metabolomic strategies in modern health and medical science studies provide innovative tools for novel diagnostic and prognostic approaches, as well as an augmented role in drug development, nutrition science, toxicology, and forensic science. In the present review, we not only introduce the application of MS-based metabolomics in the above fields, but also discuss the MS analysis technologies commonly used in metabolomics and the application of metabolomics in precision medicine, and further explore the challenges and perspectives of metabolomics in the field of health and medical science, which are expected to make a little contribution to the better development of metabolomics.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

INTRODUCTION

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

OBJECTIVES

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

METHODS

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Advances in mass spectrometry-based metabolomics for investigation of metabolites

Metabolomics is the systematic study of all the metabolites present within a biological system, which consists of a mass of molecules, having a variety of physical and chemical properties and existing over an extensive dynamic range in biological samples. Diverse analytical techniques are needed to achieve higher coverage of metabolites. The application of mass spectrometry (MS) in metabolomics has increased exponentially since the discovery and development of electrospray ionization and matrix-assisted laser desorption ionization techniques. Significant advances have also occurred in separation-based MS techniques (gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, and ion mobility-mass spectrometry), as well as separation-free MS techniques (direct infusion-mass spectrometry, matrix-assisted laser desorption ionization-mass spectrometry, mass spectrometry imaging, and direct analysis in real time mass spectrometry) in the past decades. This review presents a brief overview of the recent advanced MS techniques and their latest applications in metabolomics. The software/websites for MS result analyses are also reviewed.

Metabolomics is the systematic study of all the metabolites present within a biological system, supply functional information and has received extensive attention in the field of life sciences.

Simultaneous Quantification of Serum Multi-Phospholipids as Potential Biomarkers for Differentiating Different Pathophysiological states of lung, stomach, intestine, and pancreas

Background: Aberrant lipid metabolism is closely associated with cancer.

Materials & Methods: Serum levels of sphingomyelins (SM) (34:1), phosphatidylcholine (PC) (34:2), PC(34:1), PC(36:4), PC(36:3), and PC(36:2) in 1449 serum samples (including 599 normal controls, 69 patients with benign lung diseases (BLDs), 61 with benign colorectal diseases, 54 with benign gastric diseases, 67 with benign pancreatic diseases, and 246 with lung cancer (LC), 144 with colorectal cancer, 94 with gastric cancer, 115 with pancreatic cancer) were quantified simultaneously based on their respective calibration equations with correlation coefficient of >0.98.

Results: Receiver operating characteristic (ROC) analysis indicated that 18 panels obtained from these six phospholipids have high diagnostic ability to differentiate between different pathophysiological states. For example, a combination of SM(34:1), PC(34:2), PC(34:1), PC(36:3), and PC(36:2) to differentiating male patients with early stage LC from male normal controls plus male BLDs with a value under ROC curve (AUC) of 0.957, a sensitivity of 88.9%, and a specificity of 90.8%. SM(34:1) and PC(34:1) to differentiating female patients with early stage LC from female normal controls plus female BLDs with an AUC of 0.903, a sensitivity of 90.0%, and a specificity of 77.5%.

Conclusion: Change trends of these six phospholipids were significantly correlated with gender, physiological states, and cancer stages.

Optimization and Application of Direct Infusion Nanoelectrospray HRMS Method for Large-Scale Urinary Metabolic Phenotyping in Molecular Epidemiology

Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires <10 μL of urine sample and generates high-resolution MS profiles in both positive and negative polarities, enabling further data mining and relative quantification of hundreds of metabolites. Here we present optimization of the DI-nESI-HRMS method in a detailed step-by-step guide and provide a workflow with rigorous quality assessment for large-scale studies. We demonstrate for the first time the application of the method for urinary metabolic profiling in human epidemiological investigations. Implementation of the presented DI-nESI-HRMS method enabled cost-efficient analysis of >10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in <3 weeks with the required sensitivity and accuracy. We illustrate the application of the technique by characterizing the differences in metabolic phenotypes of the USA and Japanese population from the INTERMAP study.

Direct infusion mass spectrometry for metabolomic phenotyping of diseases

Metabolomics based on direct mass spectrometry (MS) analysis, either by direct infusion or flow injection of crude sample extracts, shows a great potential for metabolic fingerprinting because of its high-throughput screening capability, wide metabolite coverage and reduced time of analysis. Considering that numerous metabolic pathways are significantly perturbed during the initiation and progression of diseases, these metabolomic tools can be used to get a deeper understanding about disease pathogenesis and discover potential biomarkers for early diagnosis. In this work, we describe the most common metabolomic platforms used in biomedical research, with special focus on strategies based on direct MS analysis. Then, a comprehensive review on the application of direct MS fingerprinting in clinical issues is provided.

Serum Unsaturated Free Fatty Acids: A Potential Biomarker Panel for Early-Stage Detection of Colorectal Cancer

Background: To screen biomarkers to differentiate early-stage colorectal cancer (CRC) from benign colorectal disease (BCD) and healthy controls.

Materials & Methods: Quantitative and qualitative analysis of C_16:1, C_18:3, C_18:2, C_18:1, C_20:4, and C_22:6 in 185 healthy controls, 55 patients with BCD, and 139 patients with CRC was performed. Comparisons of their levels in between CRC patients, BCD patients, and healthy controls were performed using Mann-Whitney U test.

Results: Serum levels of C_16:1, C_18:3, C_18:2, C_18:1, C_20:4, and C_22:6 in CRC patients were significantly decreased compared with healthy controls and BCD patients. A combination of C_16:1, C_18:2, C_20:4, and C_22:6 has excellent diagnostic performance to differentiate early-stage CRC patients from healthy controls plus BCD patients, with an AUC of 0.926, a sensitivity of 84.6%, and a specificity of 89.8%.

Conclusions: Serum levels of C_16:1, C_18:2, C_20:4, and C_22:6 could be diagnostic indicators of early-stage CRC patients.

Multi-dimensional, comprehensive sample extraction combined with LC-GC/MS analysis for complex biological samples: application in the metabolomics study of acute pancreatitis

Multi-dimensional sample extraction and optimal LC-GC/MS were combined to obtain as much sample information as possible for metabolomics applications.

High-throughput platforms for metabolomics

Mass spectrometry has become a choice method for broad-spectrum metabolite analysis in both fundamental and applied research. This can range from comprehensive analysis achieved through time-consuming chromatography to the rapid analysis of a few target metabolites without chromatography. In this review article, we highlight current high-throughput MS-based platforms and their potential application in metabolomics. Although current MS platforms can reach throughputs up to 0.5 seconds per sample, the metabolite coverage of these platforms are low compared to low-throughput, separation-based MS methods. High-throughput comes at a cost, as it's a trade-off between sample throughput and metabolite coverage. As we will discuss, promising emerging technologies, including microfluidics and miniaturization of separation techniques, have the potential to achieve both rapid and more comprehensive metabolite analysis.

Serum Unsaturated Free Fatty Acids: A Potential Biomarker Panel for Differentiating Benign Thyroid Diseases from Thyroid Cancer

BACKGROUND

Serum free fatty acids (FFAs) are correlated with pathological status, and change in serum FFA levels may be associated with thyroid diseases.

MATERIALS AND METHODS

In this study, 664 serum samples from 322 healthy controls, 129 patients with benign thyroid disease (BTD), and 213 patients with thyroid cancer (TC) were collected. Chip-based direct-infusion nanoelectrospray-mass spectrometry was performed to simultaneously quantify six serum FFAs (i.e., C16:1, C18:1, C18:2, C18:3, C20:4, and C22:6.), with the excellent correlation coefficients of > 0.99 and relative standard deviation of <18% for all analysts. The Mann-Whitney U test was used to compare the differences in serum FFA levels between three above-mentioned groups.

RESULTS

Significant increase in the levels of C16:1, C18:1, C18:2, C18:3, C20:4, and C22:6 in healthy controls relative to TC patients and BTD patients was observed, and the levels of C16:1, C18:2, C20:4, and C22:6 in BTD patients were significantly decreased relative to TC patients. Receiver operating characteristic (ROC) analysis indicated that a combination of C16:1, C18:2, C20:4, and C22:6 has excellent diagnostic performance to differentiate BTD patients from TC patients, with an area under the ROC curve of 0.857, a sensitivity of 76.8%, and a specificity of 83.7%.

CONCLUSIONS

Change in serum levels of FFAs is closely correlated with thyroid diseases, and a biomarker panel (C16:1, C18:2, C20:4, and C22:6) should be of benefit to differentiate BTD patients from TC patients.

Unsaturated free fatty acids: a potential biomarker panel for early detection of gastric cancer

Changes in the levels of free fatty acids (FFAs) are closely associated with physiological status. Serum levels of C16:1, C18:3, C18:2, C18:1, C20:4, and C22:6 in 164 gastric cancer (GC) patients and 111 benign gastric disease (BGD) patients were significantly decreased compared with 252 healthy controls. Receiver operating characteristic analysis showed that the biomarker panel including C16:1, C18:3, C18:2, C20:4, and C22:6 presents a high diagnostic ability to differentiate early-stage GC patients from healthy controls plus BGD patients, with a sensitivity of 80.6% and a specificity of 72.7%.

Serum unsaturated free Fatty acids: potential biomarkers for early detection and disease progression monitoring of non-small cell lung cancer

Background: Lung cancer (LC) is the deadliest cancer, with earlier stage patients having a better opportunity of long-term survival. The goal of this study is to screen less-invasive and efficient biomarkers for early detection of non-small cell LC (NSCLC).

Material and Methods: We performed the simultaneous quantitative detection of six serum unsaturated free fatty acids (FFAs, C_16:1, C_18:3, C_18:2, C_18:1, C_20:4, and C_22:6) from 317 healthy controls, 78 patients with benign lung diseases (BLD), and 202 patients with NSCLC using chip-based direct-infusion nanoelectrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry (CBDInanoESI-FTICR MS) in the negative ion mode. Multiple point internal standard calibration curves between the concentration ratios of individual fatty acids to internal standards (ISs, C_17:1 as IS of C_16:1, C_18:3, C_18:2, and C_18:1 and C_21:0 as IS of C_20:4 and C_22:6) and their corresponding intensity ratios were constructed, with correlation coefficient of > 0.99. Mann-Whitney U test was employed to compare the differences in the levels of the FFAs between the patients and healthy controls.

Results: Significantly decreased levels of the FFAs in NSCLC patients were observed compared with healthy controls and BLD patients. Receiver operating characteristic curve analysis indicated that a combination of C_16:1, C_18:1, C_18:3, C_18:2, C_20:4, and C_22:6 could excellently differentiate patients with early-stage NSCLC from healthy controls plus BLD patients, with an AUC value of 0.933, a sensitivity of 84.2%, and a specificity of 89.1%. In addition, a biomarker panel (C_16:1 and C_18:1) was also confirmed preliminarily to monitor disease progression in NSCLC patients treated with icotinib, with a lead time between 8 and 48 weeks relative to clinical medical imaging.

Conclusion: A combination of C_16:1, C_18:1, C_18:3, C_18:2, C_20:4, and C_22:6 may be a powerful biomarker panel for the early detection of NSCLC and a combination of C_16:1 and C_18:1for disease progression monitoring of NSCLC.

Development and in silico evaluation of large-scale metabolite identification methods using functional group detection for metabolomics

Large-scale identification of metabolites is key to elucidating and modeling metabolism at the systems level. Advances in metabolomics technologies, particularly ultra-high resolution mass spectrometry (MS) enable comprehensive and rapid analysis of metabolites. However, a significant barrier to meaningful data interpretation is the identification of a wide range of metabolites including unknowns and the determination of their role(s) in various metabolic networks. Chemoselective (CS) probes to tag metabolite functional groups combined with high mass accuracy provide additional structural constraints for metabolite identification and quantification. We have developed a novel algorithm, Chemically Aware Substructure Search (CASS) that efficiently detects functional groups within existing metabolite databases, allowing for combined molecular formula and functional group (from CS tagging) queries to aid in metabolite identification without a priori knowledge. Analysis of the isomeric compounds in both Human Metabolome Database (HMDB) and KEGG Ligand demonstrated a high percentage of isomeric molecular formulae (43 and 28%, respectively), indicating the necessity for techniques such as CS-tagging. Furthermore, these two databases have only moderate overlap in molecular formulae. Thus, it is prudent to use multiple databases in metabolite assignment, since each major metabolite database represents different portions of metabolism within the biosphere. In silico analysis of various CS-tagging strategies under different conditions for adduct formation demonstrate that combined FT-MS derived molecular formulae and CS-tagging can uniquely identify up to 71% of KEGG and 37% of the combined KEGG/HMDB database vs. 41 and 17%, respectively without adduct formation. This difference between database isomer disambiguation highlights the strength of CS-tagging for non-lipid metabolite identification. However, unique identification of complex lipids still needs additional information.

Distinguishing between the metabolome and xenobiotic exposome in environmental field samples analysed by direct-infusion mass spectrometry based metabolomics and lipidomics

Environmental metabolomics is increasingly used to investigate organismal responses to complex chemical mixtures, including waste water effluent (WWE). In parallel, increasingly sensitive analytical methods are being used in metabolomics studies, particularly mass spectrometry. This introduces a considerable, yet overlooked, challenge that high analytical sensitivity will not only improve the detection of endogenous metabolites in biological specimens but also exogenous chemicals. If these often unknown xenobiotic features are not removed from the "biological" dataset, they will bias the interpretation and could lead to incorrect conclusions about the biotic response. Here we illustrate and validate a novel workflow classifying the origin of peaks detected in biological samples as: endogenous, xenobiotics, or metabolised xenobiotics. The workflow is demonstrated using direct infusion mass spectrometry-based metabolomic analysis of testes from roach exposed to different concentrations of a complex WWE. We show that xenobiotics and their metabolic products can be detected in roach testes (including triclosan, chloroxylenol and chlorophene), and that these compounds have a disproportionately high level of statistical significance within the total (bio)chemical changes induced by the WWE. Overall we have demonstrated that this workflow extracts more information from an environmental metabolomics study of complex mixture exposures than was possible previously.

Decreased serum levels of free fatty acids are associated with breast cancer

BACKGROUND

Changes in the levels of lipids are associated with breast cancer (BC).

METHODS

Disease-specific serum free fatty acids (FFAs) were quantified using chip-based direct-infusion nanoelectrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry (CBDInanoESI-FTICR MS) in the negative ion mode. Multiple point internal standard calibration curves between the concentration ratios of fatty acids (i.e., C16:1, C18:3, C18:2, C18:1, C20:4, and C22:6) to internal standards (C17:1 for C16:1, C18:3, C18:2, and C18:1, C21:0 for C20:4 and C22:6) and their corresponding intensity ratios were established with a correlation coefficient of greater than 0.986.

RESULTS

Data from 342 serum samples including 202 healthy controls and 140 BC patients indicate that serum concentrations of FFAs in patients with BC were significantly decreased compared with those in healthy controls. A panel of C16:1, C18:3, C18:2, C20:4, and C22:6 showed an excellent diagnostic ability to differentiate the patients with early stage BC from healthy controls, with the area under the receiver operating characteristics (ROC) curve of 0.953, a sensitivity of 83.3%, and a specificity of 87.1%.

CONCLUSION

Our findings suggest that these FFAs may be a valuable biomarker panel for the early-stage detection of BC.

Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment.