A metabolomic approach to lung cancer

Association of Untargeted Urinary Metabolomics and Lung Cancer Risk Among Never-Smoking Women in China

IMPORTANCE

Chinese women have the highest rate of lung cancer among female never-smokers in the world, and the etiology is poorly understood.

OBJECTIVE

To assess the association between metabolomics and lung cancer risk among never-smoking women.

DESIGN, SETTING, AND PARTICIPANTS

This nested case-control study included 275 never-smoking female patients with lung cancer and 289 never-smoking cancer-free control participants from the prospective Shanghai Women's Health Study recruited from December 28, 1996, to May 23, 2000. Validated food frequency questionnaires were used for the collection of dietary information. Metabolomic analysis was conducted from November 13, 2015, to January 6, 2016. Data analysis was conducted from January 6, 2016, to November 29, 2018.

EXPOSURES

Untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance metabolomic profiles were characterized using prediagnosis urine samples. A total of 39 416 metabolites were measured.

MAIN OUTCOMES AND MEASURES

Incident lung cancer.

RESULTS

Among the 564 women, those who developed lung cancer (275 participants; median [interquartile range] age, 61.0 [52-65] years) and those who did not develop lung cancer (289 participants; median [interquartile range] age, 62.0 [53-66] years) at follow-up (median [interquartile range] follow-up, 10.9 [9.0-11.7] years) were similar in terms of their secondhand smoke exposure, history of respiratory diseases, and body mass index. A peak metabolite, identified as 5-methyl-2-furoic acid, was significantly associated with lower lung cancer risk (odds ratio, 0.57 [95% CI, 0.46-0.72]; P < .001; false discovery rate = 0.039). Furthermore, this peak was weakly correlated with self-reported dietary soy intake (ρ = 0.21; P < .001). Increasing tertiles of this metabolite were associated with lower lung cancer risk (in comparison with first tertile, odds ratio for second tertile, 0.52 [95% CI, 0.34-0.80]; and odds ratio for third tertile, 0.46 [95% CI, 0.30-0.70]), and the association was consistent across different histological subtypes and follow-up times. Additionally, metabolic pathway analysis found several systemic biological alterations that were associated with lung cancer risk, including 1-carbon metabolism, nucleotide metabolism, oxidative stress, and inflammation.

CONCLUSIONS AND RELEVANCE

This prospective study of the untargeted urinary metabolome and lung cancer among never-smoking women in China provides support for the hypothesis that soy-based metabolites are associated with lower lung cancer risk in never-smoking women and suggests that biological processes linked to air pollution may be associated with higher lung cancer risk in this population.

Liquid Biopsy in Lung Cancer Screening: The Contribution of Metabolomics. Results of A Pilot Study

Background: Lung cancer is the most common cause of cancer-related deaths worldwide. Early diagnosis is crucial to increase the curability chance of the patients. Low dose CT screening can reduce lung cancer mortality, but it is associated with several limitations. Metabolomics is a promising technique for cancer diagnosis due to its ability to provide chemical phenotyping data. The intent of our study was to explore metabolomic effects and profiles of lung cancer patients to determine if metabolic perturbations in the SSAT-1/polyamine pathway can distinguish between healthy participants and lung cancer patients as a diagnostic and treatment monitoring tool. Patients and Methods: Plasma samples were collected as part of the SSAT1 Amantadine Cancer Study. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify and quantify metabolite concentrations in lung cancer patient and control samples. Standard statistical analyses were performed to determine whether metabolite concentrations could differentiate between healthy subjects and lung cancer patients, as well as risk prediction modeling applied to determine whether metabolic profiles could provide an indication of cancer progression in later stage patients. Results: A panel consisting of 14 metabolites, which included 6 metabolites in the polyamine pathway, was identified that correctly discriminated lung cancer patients from controls with an area under the curve of 0.97 (95% CI: 0.875-1.0). Conclusion: When used in conjunction with the SSAT-1/polyamine pathway, these metabolites may provide the specificity required for diagnosing lung cancer from other cancer types and could be used as a diagnostic and treatment monitoring tool.

Defining Metabolic Rewiring in Lung Squamous Cell Carcinoma

Metabolomics based on untargeted flow infusion electrospray ionization high-resolution mass spectrometry (FIE-HRMS) can provide a snap-shot of metabolism in living cells. Lung Squamous Cell Carcinoma (SCC) is one of the predominant subtypes of Non-Small Cell Lung Cancers (NSCLCs), which usually shows a poor prognosis. We analysed lung SCC samples and matched histologically normal lung tissues from eight patients. Metabolites were profiled by FIE-HRMS and assessed using t-test and principal component analysis (PCA). Differentially accumulating metabolites were mapped to pathways using the mummichog algorithm in R, and biologically meaningful patterns were indicated by Metabolite Set Enrichment Analysis (MSEA). We identified metabolic rewiring networks, including the suppression of the oxidative pentose pathway and found that the normal tricarboxylic acid (TCA) cycle were decoupled from increases in glycolysis and glutamine reductive carboxylation. Well-established associated effects on nucleotide, amino acid and thiol metabolism were also seen. Novel aspects in SCC tissue were increased in Vitamin B complex cofactors, serotonin and a reduction of γ-aminobutyric acid (GABA). Our results show the value of FIE-HRMS as a high throughput screening method that could be exploited in clinical contexts.

A pilot study of the effect of phospholipid curcumin on serum metabolomic profile in patients with non-alcoholic fatty liver disease: a randomized, double-blind, placebo-controlled trial

BACKGROUND/OBJECTIVES

Curcumin, a natural polyphenol compound in the spice turmeric, has been found to have potent anti-oxidative and anti-inflammatory activity. Curcumin may treat non-alcoholic fatty liver disease (NAFLD) through its beneficial effects on biomarkers of oxidative stress (OS) and inflammation, which are considered as two feature of this disease. However, the effects of curcumin on NAFLD have been remained poorly understood. This investigation evaluated the effects of administrating curcumin on metabolic status in NAFLD patients.

SUBJECTS/METHODS

Fifty-eight NAFLD patients participated in a randomized, double-blind, placebo-controlled parallel design of study. The subjects were allocated randomly into two groups, which either received 250 mg phospholipid curcumin or placebo, one capsule per day for a period of 8 weeks. Fasting blood samples were taken from each subject at the start and end of the study period. Subsequently, metabolomics analysis was performed for serum samples using NMR.

RESULTS

Compared with the placebo, supplementing phospholipid curcumin resulted in significant decreases in serum including 3- methyl-2-oxovaleric acid, 3-hydroxyisobutyrate, kynurenine, succinate, citrate, α-ketoglutarate, methylamine, trimethylamine, hippurate, indoxyl sulfate, chenodeoxycholic acid, taurocholic acid, and lithocholic acid. This profile of metabolic biomarkers could distinguish effectively NAFLD subjects who were treated with curcumin and placebo groups, achieving value of 0.99 for an area under receiver operating characteristic curve (AUC).

CONCLUSIONS

Characterizing the serum metabolic profile of the patients with NAFLD at the end of the intervention using NMR-based metabolomics method indicated that the targets of curcumin treatment included some amino acids, TCA cycle, bile acids, and gut microbiota.

Study of the metabolomic relationship between lung cancer and chronic obstructive pulmonary disease based on direct infusion mass spectrometry

The high prevalence of lung cancer (LC) has triggered the search of biomarkers for early diagnosis of this disease. For this purpose the study of metabolic changes related to the development of lung cancer could provide interesting information about its early diagnosis. In this sense, chronic obstructive pulmonary disease (COPD), a disease associated with tumor development, is a comorbidity that increases the risk of onset and progression of lung neoplasia and has also to be considered in the study of pathology related to lung cancer. This work develop a metabolomic approach based on direct infusion mass spectrometry using a hybrid triple quadrupole-time of flight mass spectrometer (DI-ESI-QqQ-TOF-MS) in order to identify altered metabolites from serum of LC and COPD patients and evaluate its relationship and implication in the progression of LC. This methodology has been applied to 30 serum samples from LC, 30 healthy patients used as controls (HC) and 30 serum samples from COPD to found altered metabolites from both LC and COPD diseases. In addition, some metabolic differences and similarities were found in Pulmonary Emphysema and Chronic Bronchitis patients. On the other hand, altered metabolites were studied in different stages of LC (II, III and IV) to evaluate the perturbation of them throughout the progression of disease. The sample treatment consisted of the extraction of polar and non-polar metabolites from serum that was later infused into the mass spectrometer using an electrospray ionization source in positive and negative mode. Partial least squares discriminant analysis (PLS-DA) allowed a classification between LC, HC and COPD groups in all acquisition modes. A total of 35 altered and common metabolites between LC and COPD, including amino acids, fatty acids, lysophospholipids, phospholipids and triacylglycerides were identified, being alanine, aspartate and glutamate metabolism the most altered. Finally, ROC curves were applied to the dataset and metabolites with AUC value higher than 0.70 were considered as relevant in the progression of LC.

Metabolomics Profiling on Different Stages of Colorectal Cancer: A Systematic Review

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Early diagnosis and accurate staging of the disease is vital to improve the prognosis. Metabolomics has been used to identify changes in metabolite profiles in the different stages of cancer in order to introduce new non-invasive molecular tools for staging. In this systematic review, we aim to identify the common metabolite changes in human biological samples and the dominant metabolic pathways associated with CRC progression. A broad systematic search was carried out from selected databases. Four reviewers screened and reviewed the titles, abstracts, and full-text articles according to the inclusion and exclusion criteria. Quality assessment was conducted on the eight articles which met the criteria. Data showed that the metabolites involved with redox status, energy metabolism and intermediates of amino acids, choline and nucleotides metabolism were the most affected during CRC progression. However, there were differences in the levels of individual metabolites detected between the studies, and this might be due to the study population, sample preparation, analytical platforms used and statistical tools. In conclusion, this systematic review highlights the changes in metabolites from early to late stages of CRC. Moreover, biomarkers for prognosis are important to reduce CRC-related mortality.

Metabolomic profiling of human lung tumor tissues - nucleotide metabolism as a candidate for therapeutic interventions and biomarkers

Although metabolomics has attracted considerable attention in the field of lung cancer (LC) detection and management, only a very limited number of works have applied it to tissues. As such, the aim of this study was the thorough analysis of metabolic profiles of relevant LC tissues, including the most important histological subtypes (adenocarcinoma and squamous cell lung carcinoma). Mass spectrometry‐based metabolomics, along with genetic expression and histological analyses, were performed as part of this study, the widest to date, to identify metabolic alterations in tumors of the most relevant histological subtypes in lung. A total of 136 lung tissue samples were analyzed and 851 metabolites were identified through metabolomic analysis. Our data show the existence of a clear metabolic alteration not only between tumor vs. nonmalignant tissue in each patient, but also inherently intrinsic changes in both AC and SCC. Significant changes were observed in the most relevant biochemical pathways, and nucleotide metabolism showed an important number of metabolites with high predictive capability values. The present study provides a detailed analysis of the metabolomic changes taking place in relevant biochemical pathways of the most important histological subtypes of LC, which can be used as biomarkers and also to identify novel targets.

Metabolic signatures of four major histological types of lung cancer cells

INTRODUCTION

Histologically lung cancer is classified into four major types: adenocarcinoma (Ad), squamous cell carcinoma (SqCC), large cell carcinoma (LCC), and small cell lung cancer (SCLC). Presently, our understanding of cellular metabolism among them is still not clear.

OBJECTIVES

The goal of this study was to assess the cellular metabolic profiles across these four types of lung cancer using an untargeted metabolomics approach.

METHODS

Six lung cancer cell lines, viz., Ad (A549 and HCC827), SqCC (NCl-H226 and NCl-H520), LCC (NCl-H460), and SCLC (NCl-H526), were analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry, with normal human small airway epithelial cells (SAEC) as the control group. The principal component analysis (PCA) was performed to identify the metabolic signatures that had characteristic alterations in each histological type. Further, a metabolite set enrichment analysis was performed for pathway analysis.

RESULTS

Compared to the SAEC, 31, 27, 34, 34, 32, and 39 differential metabolites mainly in relation to nucleotides, amino acid, and fatty acid metabolism were identified in A549, HCC827, NCl-H226, NCl-H520, NCl-H460, and NCl-H526 cells, respectively. The metabolic signatures allowed the six cancerous cell lines to be clearly separated in a PCA score plot.

CONCLUSION

The metabolic signatures are unique to each histological type, and appeared to be related to their cell-of-origin and mutation status. The changes are useful for assessing the metabolic characteristics of lung cancer, and offer potential for the establishment of novel diagnostic tools for different origin and oncogenic mutation of lung cancer.

Changes in Metabolites Present in Lung-Lining Fluid Following Exposure of Humans to Ozone

Controlled human exposure to the oxidant air pollutant ozone causes decrements in lung function and increased inflammation as evidenced by neutrophil influx into the lung and increased levels of proinflammatory cytokines in the airways. Here we describe a targeted metabolomics evaluation of human bronchoalveolar lavage fluid (BALF) following controlled in vivo exposure to ozone to gain greater insight into its pulmonary effects. In a 2-arm cross-over study, each healthy adult human volunteer was randomly exposed to filtered air (FA) and to 0.3 ppm ozone for 2 h while undergoing intermittent exercise with a minimum of 4 weeks between exposures. Bronchoscopy was performed and BALF obtained at 1 (n = 9) or 24 (n = 23) h postexposure. Metabolites were detected using ultrahigh performance liquid chromatography-tandem mass spectroscopy. At 1-h postexposure, a total of 28 metabolites were differentially expressed (DE) (p < .05) following ozone exposure compared with FA-exposure. These changes were associated with increased glycolysis and antioxidant responses, suggesting rapid increased energy utilization as part of the cellular response to oxidative stress. At 24-h postexposure, 41 metabolites were DE. Many of the changes were in amino acids and linked with enhanced proteolysis. Changes associated with increased lipid membrane turnover were also observed. These later-stage changes were consistent with ongoing repair of airway tissues. There were 1.37 times as many metabolites were differentially expressed at 24 h compared with 1-h postexposure. The changes at 1 h reflect responses to oxidative stress while the changes at 24 h indicate a broader set of responses consistent with tissue repair. These results illustrate the ability of metabolomic analysis to identify mechanistic features of ozone toxicity and aspects of the subsequent tissue response.

A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment

INTRODUCTION

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.

OBJECTIVES

This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.

METHOD

We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.

RESULT

Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis.

CONCLUSION

We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.

Next-Generation Novel Noninvasive Cancer Molecular Diagnostics Platforms Beyond Tissues

In recent years, there has been a revolutionary expansion in technologic advances and therapeutic innovations in cancer medicine. Cancer diagnostics has begun to move away from a sole dependence on direct tumor tissue biopsy for cancer detection, diagnosis, and treatment monitoring. The need for improvement in molecular cancer diagnostics has never been more important, with not only the advent of cancer genomics and genomics-guided precision medicine but also the recent arrival of cancer immunotherapies. Owing to the practical limitations and risks associated with tissue-based biopsy diagnostics, novel noninvasive cancer diagnostics platforms have continued to evolve and expand in recent years. Examples of these platforms include the liquid biopsy, which is used to interrogate ctDNA or circulating tumor cells, proteomics, metabolomics, and exosomes; the urine biopsy, which is used to assay ctDNAs; saliva and stool biopsies, which are used for molecular genomics assays; and the breath biopsy, which measures volatile organic compounds. These next-generation noninvasive molecular diagnostics assays beyond tissues fundamentally transform the potential utilities of cancer diagnostics to enable repeat, prospective, and serial longitudinal "biopsies" to monitor disease response resistance and progression on therapies. Moreover, they allow continual interrogation and molecular in-depth analysis of the evolving tumor's pan-canceromics under therapeutic stress. These technological and diagnostic advances have already brought about paradigm-changing next-generation cancer therapeutic strategies to enhance overall treatment efficacies. This article reviews the key noninvasive next-generation molecular diagnostics platforms beyond tissues, with emphasis on clinical utilities and applications.

Integrated semi-targeted metabolomics analysis reveals distinct metabolic dysregulation in pleural effusion caused by tuberculosis and malignancy

BACKGROUND

Tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) are the 2 most frequent causes of exudative pleural effusions (PEs). However, the clinical differentiation is challenging.

METHODS

Metabolic signatures in pleural effusion from 156 patients were profiled. An integrated semi-targeted metabolomics platform was incorporated for high throughput metabolite identification and quantitation. In this platform, orbitrap based mass spectrometry with data dependent MS/MS acquisition was applied in the analysis. In-house database containing ~1000MS/MS spectra were established and "MetaInt" was developed for metabolite alignment.

RESULTS

Using this strategy, lower levels of amino acids, citric acid cycle intermediates and free fatty acids accompanied with elevated acyl-carnitines and bile acids were observed, demonstrating increased energy expenditure caused by TPE. Kynurenine pathway from tryptophan was significantly enhanced in TPE. The ratio of tryptophan/kynurenine exhibited decent performance in differentiating TPE from MPE with sensitivity of 92.7% and specificity of 86.1%. After two further independent validations, it turns out that the ratio of tryptophan/kynurenine can be applied confidently as a potential biomarker together with adenosine deaminase (ADA) for clinical diagnosis of TPE.

CONCLUSIONS

Conclusively, the integrated in-house platform for high throughput semi-targeted metabolomics analysis reliably identified great potential of tryptophan/kynurenine ratio as a novel diagnostic biomarker to distinguish pleural effusion caused by tuberculosis and malignancy.

Critical review of reporting of the data analysis step in metabolomics

INTRODUCTION

We present the first study to critically appraise the quality of reporting of the data analysis step in metabolomics studies since the publication of minimum reporting guidelines in 2007.

OBJECTIVES

The aim of this study was to assess the standard of reporting of the data analysis step in metabolomics biomarker discovery studies and to investigate whether the level of detail supplied allows basic understanding of the steps employed and/or reuse of the protocol. For the purposes of this review we define the data analysis step to include the data pretreatment step and the actual data analysis step, which covers algorithm selection, univariate analysis and multivariate analysis.

METHOD

We reviewed the literature to identify metabolomic studies of biomarker discovery that were published between January 2008 and December 2014. Studies were examined for completeness in reporting the various steps of the data pretreatment phase and data analysis phase and also for clarity of the workflow of these sections.

RESULTS

We analysed 27 papers, published anytime in 2008 until the end of 2014 in the area or biomarker discovery in serum metabolomics. The results of this review showed that the data analysis step in metabolomics biomarker discovery studies is plagued by unclear and incomplete reporting. Major omissions and lack of logical flow render the data analysis' workflows in these studies impossible to follow and therefore replicate or even imitate.

CONCLUSIONS

While we await the holy grail of computational reproducibility in data analysis to become standard, we propose that, at a minimum, the data analysis section of metabolomics studies should be readable and interpretable without omissions such that a data analysis workflow diagram could be extrapolated from the study and therefore the data analysis protocol could be reused by the reader. That inconsistent and patchy reporting obfuscates reproducibility is a given. However even basic understanding and reuses of protocols are hampered by the low level of detail supplied in the data analysis sections of the studies that we reviewed.

Circulating metabolite profiles to predict overall survival in advanced non-small cell lung cancer patients receiving first-line chemotherapy

OBJECTIVES

The prognosis for advanced-stage non-small cell lung cancer (NSCLC) is usually poor. However, survival may be variable and difficult to predict. In the current study, we aimed to identify circulating metabolites as potential predictive biomarkers for overall survival of advanced-stage (III/IV) NSCLC patients treated with first-line platinum-based chemotherapy.

MATERIALS AND METHODS

Using two-stage study design, we performed global metabolomic profiling in blood of 220 advanced-stage NSCLC patients, including 110 with poor survival and 110 with good survival. Metabolomic profiling was conducted using Metabolon platform. The association of each metabolite with survival was assessed by Cox proportional hazard regression model with adjustment for covariates.

RESULTS AND CONCLUSION

We found levels of 4 metabolites, caffeine, paraxanthine, stachydrine, and methyl glucopyranoside (alpha+beta), differed significantly between NSCLC patients with poor and good survival in both discovery and validation phases (P<0.05). Interestingly, majority of the identified metabolites are involved in caffeine metabolism, and 2 metabolites are related to coffee intake. In fact, caffeine metabolism pathway was the only significant pathway identified which significantly differed between NSCLC patients with poor and good survival (P=1.48E-07) in the pathway analysis. We also found 4 metabolites whose levels were significantly associated with good survival in both discovery and validation phases. Strong cumulative effects on overall survival were observed for these 4 metabolites. In conclusion, we identified a panel of metabolites including metabolites in caffeine metabolism pathway that may predict survival outcome in advanced-stage NSCLC patients. The identified small metabolites may be useful biomarker candidates to help identify patients who may benefit from platinum-based chemotherapy.

Discovery of potential biomarkers in human melanoma cells with different metastatic potential by metabolic and lipidomic profiling

Malignant melanoma, characterized by its ability to metastasize to other organs, is responsible for 90% of skin cancer mortality. To investigate alterations in the cellular metabolome and lipidome related to melanoma metastasis, gas chromatography-mass spectrometry (GC-MS) and direct infusion-mass spectrometry (DI-MS)-based metabolic and lipidomic profiling were performed on extracts of normal human melanocyte (HEMn-LP), low metastatic melanoma (A375, G361), and highly metastatic melanoma (A2058, SK-MEL-28) cell lines. In this study, metabolomic analysis identified aminomalonic acid as a novel potential biomarker to discriminate between different stages of melanoma metastasis. Uptake and release of major metabolites as hallmarks of cancer were also measured between high and low metastatic melanoma cells. Lipid analysis showed a progressive increase in phosphatidylinositol (PI) species with saturated and monounsaturated fatty acyl chains, including 16:0/18:0, 16:0/18:1, 18:0/18:0, and 18:0/18:1, with increasing metastatic potential of melanoma cells, defining these lipids as possible biomarkers. In addition, a partial-least-squares projection to latent structure regression (PLSR) model for the prediction of metastatic properties of melanoma was established, and central metabolic and lipidomic pathways involved in the increased motility and metastatic potential of melanoma cells were identified as therapeutic targets. These results could be used to diagnose and control of melanoma metastasis.

Differential expression of novel metabolic and immunological biomarkers in oysters challenged with a virulent strain of OsHV-1

Early lifestages of the Pacific oyster (Crassostrea gigas) are highly susceptible to infection by OsHV-1 μVar, but little information exists regarding metabolic or pathophysiological responses of larval hosts. Using a metabolomics approach, we identified a range of metabolic and immunological responses in oyster larvae exposed to OsHV-1 μVar; some of which have not previously been reported in molluscs. Multivariate analyses of entire metabolite profiles were able to separate infected from non-infected larvae. Correlation analysis revealed the presence of major perturbations in the underlying biochemical networks and secondary pathway analysis of functionally-related metabolites identified a number of prospective pathways differentially regulated in virus-exposed larvae. These results provide new insights into the pathogenic mechanisms of OsHV-1 infection in oyster larvae, which may be applied to develop disease mitigation strategies and/or as new phenotypic information for selective breeding programmes aiming to enhance viral resistance.

Novel serum metabolomics-based approach by gas chromatography/triple quadrupole mass spectrometry for detection of human skin cancers: Candidate biomarkers

Skin cancer incidence rates are continuing to rise; however, if detected at an early stage, they can be cured with minimally invasive treatment. Therefore, the identification of novel and robust biomarkers for the early detection of skin cancer is required to improve the quality of life of the patient after treatment. In the present study, we aimed to identify novel biomarkers of skin cancers. We carried out serum metabolomics using gas chromatography/triple quadrupole mass spectrometry for two types of skin cancer: squamous cell carcinoma and melanoma. The changes in the expression of metabolites compared with healthy volunteers were analyzed by principal component analysis. Among all 118 metabolites, 27 in patients with squamous cell carcinoma and 33 in patients with melanoma showed significant changes in comparison with healthy volunteers. Principal component analysis showed that both skin cancer groups could be distinguished from the healthy volunteers group. We further investigated the specific metabolites most useful for these distinctions. In the squamous cell carcinoma group, these metabolites were glycerol, 4-hydroxybenzoic acid, sebacic acid, fucose and suberic acid. In the melanoma group, these metabolites were glutamic acid, sebacic acid, suberic acid, 4-hydroxybenzoic acid and phenylalanine. The present study identified several metabolites that were distinct for certain skin cancer types, which could potentially be used as diagnostic biomarkers leading to novel clinical management strategies.

Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry

OBJECTIVES

Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform.

METHODS

Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses.

RESULTS

A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R²X of 0.246, R²Y of 0.913 and Q² of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100%, specificity 97.1%, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set.

CONCLUSIONS

This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS.

Development of LC-QTOF-MS method for human lung tissue fingerprinting. A preliminary application to nonsmall cell lung cancer

The major histologic subtypes of non-small cell lung cancer (NSCLC) include adenocarcinoma (ADC), squamous cell lung carcinoma (SCC), and large-cell carcinoma (LCC). Clinical trials of targeted agents and newer chemotherapy agents yielded differences in outcomes according to histologic subgroups providing a rationale for histology-based treatment in NSCLC. Currently, NSCLC subtyping is performed based on histopathological examinations and immunohistochemistry. However available methods leave about 10% of NSCLC cases as not otherwise specified. The purpose of this study was development of an LC-QTOF-MS method for human lung tissue metabolic fingerprinting that could discriminate NSCLC histological subtypes and propose biomarkers candidates that could support proper NSCLC diagnosis. Metabolites were extracted with acetonitrile or methanol/ethanol and different chromatographic conditions were tested. In the final method 10 mg of lung tissue was homogenized with 50% methanol and metabolites were extracted with acetonitrile. Metabolites were separated on C8-RP and HILIC columns. About 3500 and 2000 of metabolic features (in both ion modes) were detected with good repeatability (CV < 20%) by RP and HILIC methods, respectively. Lung tumor and control tissue samples obtained from NSCLC patients were analyzed with developed methodology. Acylcarnitines, fatty acids, phospholipids, and amino acids were found more abundant in tumor as compared to control tissue. Acylcarnitines, lysophospholipids, creatinine, creatine, and alanine were identified as potential targets enabling classification of NSCLC subtypes.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

Integrated Metabolomics and Proteomics Highlight Altered Nicotinamide- and Polyamine Pathways in Lung Adenocarcinoma

Lung cancer is the leading cause of cancer mortality in the United States with non-small cell lung cancer (NSCLC) adenocarcinoma being the most common histological type. Early perturbations in cellular metabolism are a hallmark of cancer, but the extent of these changes in early stage lung adenocarcinoma remains largely unknown. In the current study, an integrated metabolomics and proteomics approach was utilized to characterize the biochemical and molecular alterations between malignant and matched control tissue from 27 subjects diagnosed with early stage lung adenocarcinoma. Differential analysis identified 71 metabolites and 1102 proteins that delineated tumor from control tissue. Integrated results indicated four major metabolic changes in early stage adenocarcinoma: (1) increased glycosylation and glutaminolysis; (2) elevated Nrf2 activation; (3) increase in nicotinic and nicotinamide salvaging pathways; and (4) elevated polyamine biosynthesis linked to differential regulation of the SAM/nicotinamide methyl-donor pathway. Genomic data from publicly available databases were included to strengthen proteomic findings. Our findings provide insight into the biochemical and molecular biological reprogramming that may accompanies early stage lung tumorigenesis and highlight potential therapeutic targets.

Study of early stage non-small-cell lung cancer using Orbitrap-based global serum metabolomics

Purpose

The aim of the project was to apply ultra-high-performance liquid chromatography–quadrupole-Orbitrap-high-resolution mass spectrometry for serum metabolite profiling of non-small-cell lung cancer (NSCLC). This Orbitrap-based methodology has been applied for a study of NSCLC potential markers for the first time.

Methods

After extraction using protein precipitation, sera were separated on the ACE Excel 2 C18-PFP (100 × 2.1 mm, 2.0 µm) column using gradient elution and analyzed within the range of 70–1000 m/z. Only patients with early stage disease (stages IA–IIB) were included in the study, providing opportunity to find biomarkers for early lung cancer detection. The resulting metabolite profiles were subjected to univariate and multivariate statistical tests.

Results

36 features were found significantly changed between NSCLC group and controls after FDR adjustment and 19 were identified using various metabolite databases (in-house library, HMDB, mzCloud). The study revealed a number of NSCLC biomarker candidates which belong to such compound classes as acylcarnitines, organic acids, and amino acids. Multivariate ROC curve built using 12 identified metabolites was characterized by AUC = 0.836 (0.722–0.946). There were no significant differences in the serum metabolite profiles between two most common histological types of lung cancer—adenocarcinoma and squamous cell carcinoma.

Conclusions

Through identification of novel potential tumor markers, Orbitrap-based global metabolic profiling is a useful strategy in cancer research. Our study can accelerate development of new diagnostic and therapeutic strategies in NSCLC. The metabolites involved in discrimination between NSCLC patients and the control subjects should be further explored using a targeted approach.

Electronic supplementary material

The online version of this article (doi:10.1007/s00432-017-2347-0) contains supplementary material, which is available to authorized users.

Tumor Delineation and Quantitative Assessment of Glucose Metabolic Rate within Histologic Subtypes of Non-Small Cell Lung Cancer by Using Dynamic 18F Fluorodeoxyglucose PET

Purpose To assess whether dynamic fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) positron emission tomography (PET) has added value over static ¹⁸F-FDG PET for tumor delineation in non-small cell lung cancer (NSCLC) radiation therapy planning by using pathology volumes as the reference standard and to compare pharmacokinetic rate constants of ¹⁸F-FDG metabolism, including regional variation, between NSCLC histologic subtypes. Materials and Methods The study was approved by the institutional review board. Patients gave written informed consent. In this prospective observational study, 1-hour dynamic ¹⁸F-FDG PET/computed tomographic examinations were performed in 35 patients (36 resectable NSCLCs) between 2009 and 2014. Static and parametric images of glucose metabolic rate were obtained to determine lesion volumes by using three delineation strategies. Pathology volume was calculated from three orthogonal dimensions (n = 32). Whole tumor and regional rate constants and blood volume fraction (V_B) were computed by using compartment modeling. Results Pathology volumes were larger than PET volumes (median difference, 8.7-25.2 cm³; Wilcoxon signed rank test, P < .001). Static fuzzy locally adaptive Bayesian (FLAB) volumes corresponded best with pathology volumes (intraclass correlation coefficient, 0.72; P < .001). Bland-Altman analyses showed the highest precision and accuracy for static FLAB volumes. Glucose metabolic rate and ¹⁸F-FDG phosphorylation rate were higher in squamous cell carcinoma (SCC) than in adenocarcinoma (AC), whereas V_B was lower (Mann-Whitney U test or t test, P = .003, P = .036, and P = .019, respectively). Glucose metabolic rate, ¹⁸F-FDG phosphorylation rate, and V_B were less heterogeneous in AC than in SCC (Friedman analysis of variance). Conclusion Parametric images are not superior to static images for NSCLC delineation. FLAB-based segmentation on static ¹⁸F-FDG PET images is in best agreement with pathology volume and could be useful for NSCLC autocontouring. Differences in glycolytic rate and V_B between SCC and AC are relevant for research in targeting agents and radiation therapy dose escalation. ^© RSNA, 2016 Online supplemental material is available for this article.

Metabolite Profiles of the Serum of Patients with Non-Small Cell Carcinoma

INTRODUCTION

Alterations of serum metabolites may allow us to identify individuals with lung cancer and advance our understanding of the nature and treatment of their cancer. We aimed to identify serum metabolites that differentiate patients with lung cancer from at-risk controls.

METHODS

Serum samples from patients with biopsy-confirmed untreated stage I through stage III non-small cell lung cancer and at-risk controls were divided into fractions for analysis by ultrahigh-performance liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Compounds were identified by comparison with library entries of purified standards. Differences in concentrations of single metabolites and metabolite ratios were identified. Prediction models were developed.

RESULTS

Serum samples from 284 subjects was analyzed. The subjects' mean age was 67 and 48% were female. Ninety-four patients had lung cancer (50 had adenocarcinoma and 44 had squamous cell carcinoma), 44% had stage I disease, 17% had stage II disease, and 39% had stage III disease. The patients with cancer were slightly older than the controls (68.7 versus 66.2 years, p = 0.013). A total of 534 metabolites were identified in eight metabolite superpathways and 73 subpathways. The concentrations of 149 metabolites differed significantly (q values <0.05) between the cancer and control groups (70 were lower in the cancer group and 79 were higher), and 9723 metabolite ratios differed significantly (q values <0.001) between the cancer and control groups. The accuracies of the models (cancer and cancer subtypes versus control) trained on 70% of the subjects and tested on 30% (expressed as C-statistics) ranged from 0.748 to 0.858.

CONCLUSIONS

Differences in the serum metabolite profile exist between patients with stage I through stage III non-small cell lung cancer and matched controls.

NMR based serum metabolomics reveals a distinctive signature in patients with Lupus Nephritis

Management of patient with Lupus Nephritis (LN) continues to remain a challenge for the treating physicians because of considerable morbidity and even mortality. The search of biomarkers in serum and urine is a focus of researchers to unravel new targets for therapy. In the present study, the utility of NMR-based serum metabolomics has been evaluated for the first time in discriminating LN patients from non-nephritis lupus patients (SLE) and further to get new insights into the underlying disease processes for better clinical management. Metabolic profiling of sera obtained from 22 SLE patients, 40 LN patients and 30 healthy controls (HC) were performed using high resolution 1D ¹H-CPMG and diffusion edited NMR spectra to identify the potential molecular biomarkers. Using multivariate analysis, we could distinguish SLE and LN patients from HC and LN from SLE patients. Compared to SLE patients, the LN patients had increased serum levels of lipid metabolites (including LDL/VLDL lipoproteins), creatinine and decreased levels of acetate. Our results revealed that metabolic markers especially lipids and acetate derived from NMR spectroscopy has high sensitivity and specificity to distinguish LN among SLE patients and has the potential to be a useful adjunctive tool in diagnosis and clinical management of LN.

Serum metabonomics of acute leukemia using nuclear magnetic resonance spectroscopy

Acute leukemia is a critical neoplasm of white blood cells. In order to differentiate between the metabolic alterations associated with two subtypes of acute leukemia, acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), we investigated the serum of ALL and AML patients and compared with two controls (healthy and aplastic anemia) using (1)H NMR (nuclear magnetic resonance) spectroscopy. Thirty-seven putative metabolites were identified using Carr-Purcell-Meiboom-Gill (CPMG) sequence. The use of PLS-DA and OPLS-DA models gave results with 84.38% and 90.63% classification rate, respectively. The metabolites responsible for classification are mainly lipids, lactate and glucose. Compared with controls, ALL and AML patients showed serum metabonomic differences involving aberrant metabolism pathways including glycolysis, TCA cycle, lipoprotein changes, choline and fatty acid metabolisms.

Biomarker development in the precision medicine era: lung cancer as a case study

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

Changes of serum amino acid profiles by an epidermal growth factor receptor mutation and benzo[a]pyrene in mouse lung tumorigenesis

Studies suggest that gene mutation and carcinogen exposure contribute to lung tumorigenesis including a mutation of epidermal growth factor receptor (EGFR) and exposure to benzo[a]pyrene (BaP). However, the interaction between EGFR mutation and BaP exposure during lung tumorigenesis is unclear. Metabolomics has become an important tool in clinical research and has been utilized to help our understanding of mechanisms and to identify indicators of cancers. This study's aim was to identify the changes in metabolite profiles in mice associated with an EGFR exon 21 deletion and/or BaP treatment-induced lung tumorigenesis. While the EGFR mutation increased the incidence of lung adenoma in transgenic mice (EGFR mutant mice) at 32 weeks of age, exposure to BaP caused the onset of lung tumorigenesis in these mice as early as 16 weeks after exposure. Using a metabolomics strategy involving liquid chromatography-mass spectrometry in conjunction with principal component analysis and confirmation by liquid chromatography triple quadrupole tandem mass spectrometry, we demonstrated that the serum amino acid profiles of these mice were changed. A total of eight amino acid concentrations were lower in EGFR mutant mice than in wild-type mice at 32 weeks of age. Five amino acids were lower in tumor-bearing mice than in non-tumor-bearing EGFR mutant mice at 10th week post-treatment of BaP, namely phenylalanine, tyrosine, alanine, proline, and threonine. Our results suggest that gene mutation and carcinogen exposure-induced lung adenomas share some common mechanisms. Changes in serum amino acid profiles may be early indicators of lung tumorigenesis.

SERUM metabolomics of acute lymphoblastic leukaemia and acute myeloid leukaemia for probing biomarker molecules

Acute leukaemia (AL) is a critical neoplasm of white blood cells. Diagnosing AL requires bone marrow puncture procedure, which many patients do not consent to for it is invasive. Hence sensitive and specific early diagnostic biomarkers are essential for non-invasive diagnosis, new therapeutics and improving the disease prognosis. To differentiate the metabolic alterations associated with acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML), we investigated serum of ALL and AML patients in comparison with two controls using gas chromatography coupled with triple quadrupole tandem mass spectrometry and multivariate statistical analysis. Twenty seven out of 1425 metabolites were found differentiative among ALL, AML, aplastic anaemia (APA) patients and healthy control using p-value ≤ 0.001. ALL is the most dissimilar group from other three groups as in hierarchical clustering showed 72.1% dissimilarity. Model generation using PLSDA gave an overall accuracy of 91.9%. This study helps in metabolic fingerprinting of control and disease serum at high significance levels and could be used for early diagnosing of AL. Based on pathways analysis, fatty acid metabolism is deregulated in patients with AL and may represent an underlying metabolic pathway associated with disease progression. Copyright © 2016 John Wiley & Sons, Ltd.

Evaluation of serum amino acid profiles' utility in non-small cell lung cancer detection in Polish population

OBJECTIVES

Data from studies performed in Japanese and Korean populations suggest that free amino acid profiles have the potential to aid in non-small cell lung cancer (NSCLC) detection. However, there is still no data regarding abnormalities of free amino acids and their usefulness in NSCLC detection in European populations. The aim of the study was an evaluation of utility of amino acid profiles in NSCLC detection in Polish patients.

MATERIALS AND METHODS

Levels of 31 free amino acids were determined in 153 serum samples applying a liquid chromatography-tandem mass spectrometry-based methodology. Patients with I stage lung cancer represented a significant part of the studied group (46.7%). The obtained metabolite profiles along with clinical data were subjected to multivariate statistical tests.

RESULTS

The presented study indicated that the increased serum level of phenylalanine and decreased level of citrulline are among the most robust cancer signatures in blood of NSCLC group. In addition, increased levels of aspartic acid and β-alanine were also recognized as important features of NSCLC. Amino acid selected based on studies of Asian patients were found to have insufficient specificity in NSCLC detection in the studied population. Therefore, we proposed a new set of 6 amino acids (aspartic acid, β-alanine, histidine, asparagine, phenylalanine and serine), which ensured higher accuracy in sample classification (from 90.3% to 77.1% depending of histological type).

CONCLUSION

We indicated that some of the free amino acid alterations occur in serum of NSCLC patients in early stage of disease and thus they can be valuable components of a blood multi-marker panel for NSCLC detection.

Identification of Serum Peptidome Signatures of Non-Small Cell Lung Cancer

Due to high mortality rates of lung cancer, there is a need for identification of new, clinically useful markers, which improve detection of this tumor in early stage of disease. In the current study, serum peptide profiling was evaluated as a diagnostic tool for non-small cell lung cancer patients. The combination of the ZipTip technology with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the analysis of peptide pattern of cancer patients (n = 153) and control subjects (n = 63) was presented for the first time. Based on the observed significant differences between cancer patients and control subjects, the classification model was created, which allowed for accurate group discrimination. The model turned out to be robust enough to discriminate a new validation set of samples with satisfactory sensitivity and specificity. Two peptides from the diagnostic pattern for non-small cell lung cancer (NSCLC) were identified as fragments of C3 and fibrinogen α chain. Since ELISA test did not confirm significant differences in the expression of complement component C3, further study will involve a quantitative approach to prove clinical utility of the other proteins from the proposed multi-peptide cancer signature.

Glucose metabolism in gastric cancer: The cutting-edge

Glucose metabolism in gastric cancer cells differs from that of normal epithelial cells. Upregulated aerobic glycolysis (Warburg effect) in gastric cancer meeting the demands of cell proliferation is associated with genetic mutations, epigenetic modification and proteomic alteration. Understanding the mechanisms of aerobic glycolysis may contribute to our knowledge of gastric carcinogenesis. Metabolomic studies offer novel, convenient and practical tools in the search for new biomarkers for early detection, diagnosis, prognosis, and chemosensitivity prediction of gastric cancer. Interfering with the process of glycolysis in cancer cells may provide a new and promising therapeutic strategy for gastric cancer. In this article, we present a brief review of recent studies of glucose metabolism in gastric cancer, with primary focus on the clinical applications of new biomarkers and their potential therapeutic role in gastric cancer.

The metabolomic detection of lung cancer biomarkers in sputum

OBJECTIVES

Developing screening and diagnosis methodologies based on novel biomarkers should allow for the detection of the lung cancer (LC) and possibly at an earlier stage and thereby increase the effectiveness of clinical interventions. Here, our primary objective was to evaluate the potential of spontaneous sputum as a source of non-invasive metabolomic biomarkers for LC status.

MATERIALS AND METHODS

Spontaneous sputum was collected and processed from 34 patients with suspected LC, alongside 33 healthy controls. Of the 34 patients, 23 were subsequently diagnosed with LC (LC(+), 16 NSCLC, six SCLC, and one radiological diagnosis), at various stages of disease progression. The 67 samples were analysed using flow infusion electrospray ion mass spectrometry (FIE-MS) and gas-chromatography mass spectrometry (GC-MS).

RESULTS

Principal component analysis identified negative mode FIE-MS as having the main separating power between samples from healthy and LC. Discriminatory metabolites were identified using ANOVA and Random Forest. Indications of potential diagnostic accuracy involved the use of receiver operating characteristic/area under the curve (ROC/AUC) analyses. This approach identified metabolites changes that were only observed with LC. Metabolites with AUC values of greater than 0.8 which distinguished between LC(+)/LC(-) binary classifications where identified and included Ganglioside GM1 which has previously been linked to LC.

CONCLUSION

This study indicates that metabolomics based on sputum can yield metabolites that can be used as a diagnostic and/or discriminator tool. These could aid clinical intervention and targeted diagnosis of LC within an 'at risk' LC(-) population group. The use of sputum as a non-invasive source of metabolite biomarkers may aid in the development of an at-risk population screening programme for lung cancer or enhanced clinical diagnostic pathways.

Cancer-Specific Production of N-Acetylaspartate via NAT8L Overexpression in Non-Small Cell Lung Cancer and Its Potential as a Circulating Biomarker

In order to identify new cancer-associated metabolites that may be useful for early detection of lung cancer, we performed a global metabolite profiling of a non-small cell lung cancer (NSCLC) line and immortalized normal lung epithelial cells from the same patient. Among several metabolites with significant cancer/normal differences, we identified a unique metabolic compound, N-acetylaspartate (NAA), in cancer cells-undetectable in normal lung epithelium. NAA's cancer-specific detection was validated in additional cancer and control lung cells as well as selected NSCLC patient tumors and control tissues. NAA's cancer specificity was further supported in our analysis of NAA synthetase (gene symbol: NAT8L) gene expression levels in The Cancer Genome Atlas: elevated NAT8L expression in approximately 40% of adenocarcinoma and squamous cell carcinoma cases (N = 577), with minimal expression in all nonmalignant lung tissues (N = 74). We then showed that NAT8L is functionally involved in NAA production of NSCLC cells through siRNA-mediated suppression of NAT8L, which caused selective reduction of intracellular and secreted NAA. Our cell culture experiments also indicated that NAA biosynthesis in NSCLC cells depends on glutamine availability. For preliminary evaluation of NAA's clinical potential as a circulating biomarker, we developed a sensitive NAA blood assay and found that NAA blood levels were elevated in 46% of NSCLC patients (N = 13) in comparison with age-matched healthy controls (N = 21) among individuals aged 55 years or younger. Taken together, these results indicate that NAA is produced specifically in NSCLC tumors through NAT8L overexpression, and its extracellular secretion can be detected in blood. Cancer Prev Res; 9(1); 43-52. ©2015 AACR.

Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment

Lung cancer causes more deaths in men and women than any other cancer related disease. Currently, few effective strategies exist to predict how patients will respond to treatment. We evaluated the serum metabolomic profiles of 25 lung cancer patients undergoing chemotherapy ± radiation to evaluate the feasibility of metabolites as temporal biomarkers of clinical outcomes. Serial serum specimens collected prospectively from lung cancer patients were analyzed using both nuclear magnetic resonance (¹H-NMR) spectroscopy and gas chromatography mass spectrometry (GC-MS). Multivariate statistical analysis consisted of unsupervised principal component analysis or orthogonal partial least squares discriminant analysis with significance assessed using a cross-validated ANOVA. The metabolite profiles were reflective of the temporal distinction between patient samples before during and after receiving therapy (¹H-NMR, p < 0.001: and GC-MS p < 0.01). Disease progression and survival were strongly correlative with the GC-MS metabolite data whereas stage and cancer type were associated with ¹H-NMR data. Metabolites such as hydroxylamine, tridecan-1-ol, octadecan-1-ol, were indicative of survival (GC-MS p < 0.05) and metabolites such as tagatose, hydroxylamine, glucopyranose, and threonine that were reflective of progression (GC-MS p < 0.05). Metabolite profiles have the potential to act as prognostic markers of clinical outcomes for lung cancer patients. Serial ¹H-NMR measurements appear to detect metabolites diagnostic of tumor pathology, while GC-MS provided data better related to prognostic clinical outcomes, possibility due to physiochemical bias related to specific biochemical pathways. These results warrant further study in a larger cohort and with various treatment options.

Personalized Medicine in Respiratory Disease: Role of Proteomics

Respiratory diseases affect humanity globally, with chronic lung diseases (e.g., asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, among others) and lung cancer causing extensive morbidity and mortality. These conditions are highly heterogeneous and require an early diagnosis. However, initial symptoms are nonspecific, and the clinical diagnosis is made late frequently. Over the last few years, personalized medicine has emerged as a medical care approach that uses novel technology aiming to personalize treatments according to the particular patient's medical needs. This review highlights the contributions of proteomics toward the understanding of personalized medicine in respiratory disease and its potential applications in the clinic.

In situ biomarker discovery and label-free molecular histopathological diagnosis of lung cancer by ambient mass spectrometry imaging

Sensitive and spatial exploration of the metabolism of tumors at the metabolome level is highly challenging. In this study, we developed an in situ metabolomics method based on ambient mass spectrometry imaging using air flow-assisted desorption electrospray ionization (AFADESI), which can spatially explore the alteration of global metabolites in tissues with high sensitivity. Using this method, we discovered potential histopathological diagnosis biomarkers (including lipids, amino acids, choline, peptides, and carnitine) from 52 postoperative lung cancer tissue samples and then subsequently used these biomarkers to generate images for rapid and label-free histopathological diagnosis. These biomarkers were validated with a sensitivity and a specificity of 93.5% and 100%, respectively. Moreover, a single imaging analysis of a cryosection that visualized all these biomarkers, taking tens of minutes, revealed the type and subtype of the cancer. This method could potentially be used as a molecular pathological tool for rapid clinical lung cancer diagnosis and immediate image-guided surgery.

A recording chamber for small volume slice electrophysiology

Electrophysiological recordings from brain slices are typically performed in small recording chambers that allow for the superfusion of the tissue with artificial extracellular solution (ECS), while the chamber holding the tissue is mounted in the optical path of a microscope to image neurons in the tissue. ECS itself is inexpensive, and thus superfusion rates and volumes of ECS consumed during an experiment using standard ECS are not critical. However, some experiments require the addition of expensive pharmacological agents or other chemical compounds to the ECS, creating a need to build superfusion systems that operate on small volumes while still delivering appropriate amounts of oxygen and other nutrients to the tissue. We developed a closed circulation tissue chamber for slice recordings that operates with small volumes of bath solution in the range of 1.0 to 2.6 ml and a constant oxygen/carbon dioxide delivery to the solution in the bath. In our chamber, the ECS is oxygenated and recirculated directly in the recording chamber, eliminating the need for tubes and external bottles/containers to recirculate and bubble ECS and greatly reducing the total ECS volume required for superfusion. At the same time, the efficiency of tissue oxygenation and health of the section are comparable to standard superfusion methods. We also determined that the small volume of ECS contains a sufficient amount of nutrients to support the health of a standard brain slice for several hours without concern for either depletion of nutrients or accumulation of waste products.

Assessment of the efficacy of anticancer drugs by amino acid metabolomics using fluorescence derivatization-HPLC

Metabolomic studies conducted for evaluating cancer pathogenesis and progression by monitoring the amino acids metabolic balance hold great promise for assessing current and future anticancer treatments. We performed a comprehensive quantification of 21 amino acids concentrations in cultured human colorectal adenocarcinoma cells treated with the anticancer drugs 5-fluorouracil, irinotecan, and cisplatin. A precolumn fluorescence derivatization-HPLC method involving 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate was used. Amino acid concentration data were analyzed by principal-component analysis and partial least-squares multivariate statistical methods to represent samples on two-dimensional graphs. The hierarchical cluster analysis and linear discriminant analysis were used to classify the samples on the score plots. Unlike the cluster analysis approach, the linear discrimination analysis classification successfully distinguished anticancer drug-treated samples from the untreated controls. Moreover, three candidate amino acids (serine, aspartic acid, and methionine) were identified from the loading plots as potential biomarkers. Our proposed method might be able to evaluate the effectiveness of anticancer therapy even in small laboratories or medical institutions.