The significance and robustness of a plasma free amino acid (PFAA) profile-based multiplex function for detecting lung cancer

Metabolomics-based search for lung cancer markers among patients with different smoking status

Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.

Metabolomics and lipidomics in non-small cell lung cancer

Due to its insidious nature, lung cancer remains a leading cause of cancer-related deaths worldwide. Therefore, there is an urgent need to identify sensitive/specific biomarkers for early diagnosis and monitoring. The current study was designed to provide a current metabolic profile of non-small cell lung cancer (NSCLC) by systematically reviewing and summarizing various metabolomic/ lipidomic studies based on NSCLC blood samples, attempting to find biomarkers in human blood that can predict or diagnose NSCLC, and investigating the involvement of key metabolites in the pathogenesis of NSCLC. We searched all articles on lung cancer published in Elsevier, PubMed, Web of Science and the Cochrane Library between January 2012 and December 2022. After critical selection, a total of 31 studies (including 2768 NSCLC patients and 9873 healthy individuals) met the inclusion criteria, and 22 were classified as "high quality". Forty-six metabolites related to NSCLC were repeatedly identified, involving glucose metabolism, amino acid metabolism, lipid metabolism and nucleotide metabolism. Pyruvic acid, carnitine, phenylalanine, isoleucine, kynurenine and 3-hydroxybutyrate showed upward trends in all studies, citric acid, glycine, threonine, cystine, alanine, histidine, inosine, betaine and arachidic acid showed downward trends in all studies. This review summarizes the existing metabolomic/lipidomic studies related to the identification of blood biomarkers in NSCLC, examines the role of key metabolites in the pathogenesis of NSCLC, and provides an important reference for the clinical diagnosis and treatment of NSCLC. Due to the limited size and design heterogeneity of the existing studies, there is an urgent need for standardization of future studies, while validating existing findings with more studies.

[Research Progress on Tumor Metabolic Biomarkers in Liquid Biopsy of Lung Cancer]

Liquid biopsy is gradually being applied in the clinical diagnosis and treatment of lung cancer. At present, with the development of metabolomics, more and more metabolic biomarkers are considered as potential sensitive markers reflecting the occurrence and development of tumors. This article summarizes the changes in the main metabolic pathways of lung cancer, including glucose metabolism, amino acid metabolism, lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and purine metabolism. Meanwhile, this article reviews the role of metabolic biomarkers in the early diagnosis of lung cancer, predicting disease progression, and evaluating the efficacy of chemotherapy and immunotherapy, aiming to provide effective biomarkers for tumor diagnosis and treatment.
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Effects of methionine deficiency on B7H3-DAP12-CAR-T cells in the treatment of lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is a subtype of lung cancer for which precision therapy is lacking. Chimeric antigen receptor T-cells (CAR-T) have the potential to eliminate cancer cells by targeting specific antigens. However, the tumor microenvironment (TME), characterized by abnormal metabolism could inhibit CAR-T function. Therefore, the aim of this study was to improve CAR-T efficacy in solid TME by investigating the effects of amino acid metabolism. We found that B7H3 was highly expressed in LUSC and developed DAP12-CAR-T targeting B7H3 based on our previous findings. When co-cultured with B7H3-overexpressing LUSC cells, B7H3-DAP12-CAR-T showed significant cell killing effects and released cytokines including IFN-γ and IL-2. However, LUSC cells consumed methionine (Met) in a competitive manner to induce a Met deficiency. CAR-T showed suppressed cell killing capacity, reduced cytokine release and less central memory T phenotype in medium with lower Met, while the exhaustion markers were up-regulated. Furthermore, the gene NKG7, responsible for T cell cytotoxicity, was downregulated in CAR-T cells at low Met concentration due to a decrease in m5C modification. NKG7 overexpression could partially restore the cytotoxicity of CAR-T in low Met. In addition, the anti-tumor efficacy of CAR-T was significantly enhanced when co-cultured with SLC7A5 knockdown LUSC cells at low Met concentration. In conclusion, B7H3 is a prospective target for LUSC, and B7H3-DAP12-CAR-T cells are promising for LUSC treatment. Maintaining Met levels in CAR-T may help overcome TME suppression and improve its clinical application potential.

Causal associations of histidine and 12 site-specific cancers: a bidirectional Mendelian randomization study

An increasing number of studies indicate that cancer patients' histidine (HIS) circulating levels have changed. However, the causality between HIS and cancer is still not well established. Thus, to ascertain the causal link between HIS and cancers, we performed a bidirectional Mendelian randomization (MR) analysis. Summary-level data are derived from publicly available genome-wide association studies (GWAS). The causal effects were mainly estimated using the inverse-variance weighted method (IVW). The weighted-median (WM) method and MR-Egger regression were conducted as sensitivity analyses. In the forward-MR, we found malignant neoplasm of respiratory system and intrathoracic organs (OR: 1.020; 95% CI: 1.006-1.035; pIVW = 0.007) genetically associated with circulating HIS. And there was no significant genetic correlation between HIS and another 11 site-specific cancers using IVW method. In the reversed-MR, we did not observe the causal relationship between HIS and 12 site-specific cancers. Our findings help clarify that HIS, as a biomarker for malignant neoplasms of respiratory system and intrathoracic organs, is causal rather than a secondary biomarker of the cancerous progression. The mechanism between histidine and cancer progression deserves further investigation.

Greater plasma essential amino acids and lower 3-methylhistidine with higher protein intake during endurance training: a randomised control trial

Endurance exercise alters amino acid (AA) metabolism that necessitates greater AA intake in the post exercise recovery period to support recovery. Thus, daily AA ingestion during a period of endurance training may affect the metabolically active plasma free AA pool, which is otherwise maintained during periods of inadequate protein intake by the breakdown of skeletal muscle proteins. Nine endurance-trained males completed a 4-day running protocol (20 km, 5 km, 10 km and 20 km on days 1-4, respectively) on three occasions with a controlled diet providing different protein intakes [0.94(LOW), 1.20(MOD) or 1.83gprotein kgbody mass-1 day-1 (HIGH)]. Urine collected over 24 h on day-4 and plasma collected after an overnight fast on day-5 were analyzed for free AA (plasma) and 3-methylhistidine (3MH; plasma and urine), a marker of myofibrillar protein breakdown. There was an effect of protein intake (HIGH > MOD/LOW; P < 0.05) on fasted plasma essential AA, branched chain AA and 3MH but no effect on 24-h urinary 3-MH excretion. Consuming a previously determined optimal daily protein intake of 1.83 g kg-1 day-1 during endurance training maintains fasted plasma free AA and may attenuate myofibrillar protein catabolism, although this latter effect was not detected in 24-h urinary excretion. The maintenance of the metabolically active free plasma AA pool may support greater recovery from exercise and contribute to the previously determined greater whole-body net protein balance in this athletic population. TRN: NCT02801344 (June 15, 2016).

Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer

Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.

Pretransplant systemic metabolic profiles in allogeneic hematopoietic stem cell transplant recipients - identification of patient subsets with increased transplant-related mortality

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is used in the treatment of high-risk acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS); however, the treatment has high risk of severe transplantation-related mortality (TRM). In this study, we examined pretransplantation serum samples derived from 92 consecutive allotransplant recipients with AML or MDS. Using nontargeted metabolomics, we identified 1274 metabolites including 968 of known identity (named biochemicals). We further investigated metabolites that differed significantly when comparing patients with and without early extensive fluid retention, pretransplantation inflammation (both being associated with increased risk of acute graft-versus-host disease [GVHD]/nonrelapse mortality) and development of systemic steroid-requiring acute GVHD (aGVHD). All three factors are associated with TRM and were also associated with significantly altered amino acid metabolism, although there was only a minor overlap between these three factors with regard to significantly altered individual metabolites. Furthermore, steroid-requiring aGVHD was especially associated with altered taurine/hypotaurine, tryptophan, biotin, and phenylacetate metabolism together with altered malate-aspartate shuttle and urea cycle regulation. In contrast, pretransplantation inflammation was associated with a weaker modulation of many different metabolic pathways, whereas extensive fluid retention was associated with a weaker modulation of taurine/hypotaurine metabolism. An unsupervised hierarchical cluster analysis based on the 13 most significantly identified metabolites associated with aGVHD identified a patient subset with high metabolite levels and increased frequencies of MDS/MDS-AML, steroid-requiring aGVHD and early TRM. On the other hand, a clustering analysis based on metabolites that were significantly altered for aGVHD, inflammation, and fluid retention comparison groups identified a patient subset with a highly significant association with TRM. Our study suggests that the systemic pretransplantation metabolic profiles can be used to identify patient subsets with an increased frequency of TRM.

Systemic Metabolomic Profiles in Adult Patients with Bacterial Sepsis: Characterization of Patient Heterogeneity at the Time of Diagnosis

Sepsis is a dysregulated host response to infection that causes potentially life-threatening organ dysfunction. We investigated the serum metabolomic profile at hospital admission for patients with bacterial sepsis. The study included 60 patients; 35 patients fulfilled the most recent 2016 Sepsis-3 criteria whereas the remaining 25 patients only fulfilled the previous Sepsis-2 criteria and could therefore be classified as having systemic inflammatory response syndrome (SIRS). A total of 1011 identified metabolites were detected in our serum samples. Ninety-seven metabolites differed significantly when comparing Sepsis-3 and Sepsis-2/SIRS patients; 40 of these metabolites constituted a heterogeneous group of amino acid metabolites/peptides. When comparing patients with and without bacteremia, we identified 51 metabolites that differed significantly, including 16 lipid metabolites and 11 amino acid metabolites. Furthermore, 42 metabolites showed a highly significant association with the maximal total Sequential Organ Failure Assessment (SOFA )score during the course of the disease (i.e., Pearson's correlation test, p-value < 0.005, and correlation factor > 0.6); these top-ranked metabolites included 23 amino acid metabolites and a subset of pregnenolone/progestin metabolites. Unsupervised hierarchical clustering analyses based on all 42 top-ranked SOFA correlated metabolites or the subset of 23 top-ranked amino acid metabolites showed that most Sepsis-3 patients differed from Sepsis-2/SIRS patients in their systemic metabolic profile at the time of hospital admission. However, a minority of Sepsis-3 patients showed similarities with the Sepsis-2/SIRS metabolic profile even though several of them showed a high total SOFA score. To conclude, Sepsis-3 patients are heterogeneous with regard to their metabolic profile at the time of hospitalization.

Associations of AminoIndex Cancer Screening (Breast) Grade with Clinical and Laboratory Variables

BACKGROUND

Altered metabolism in the blood of cancer patients is closely related to changes in amino acids. Amino acids play an important physiological role as essential metabolites and regulators of metabolism. AminoIndex Cancer Screening (AICS) uses multivariate analysis of plasma-free amino acid profiles to screen for seven cancer types, including breast cancer.

METHODS

To determine the clinical utility of AICS (breast), we retrospectively analyzed associations of AICS (breast) score with clinical and laboratory variables in 390 patients who underwent AICS (breast) testing. The mean age of participants was 50.7 years (range: 26-87 years) and all were female.

RESULTS

The AICS (breast) grade was A, B, and C for 250 (64.1%), 90 (23.1%), and 50 (12.8%) participants, respectively. AICS (breast) was significantly correlated with AICS (gastric) (r = 0.487, p < 0.0001) and AICS (lung) (r = 0.523, p < 0.0001). Multivariate linear regression analysis showed no significant difference of AICS (breast) grade with age, body mass index, estimated glomerular filtration rate, dyslipidemia, or blood pressure. However, neutrophil-to-lymphocyte ratio significantly differed in relation to AICS (breast) grade (cut-off value, 1.7; p = 0.030), although only data from 72 patients were analyzed.

CONCLUSION

To our knowledge, this is the first study to report associations of AICS (breast) grade with clinical variables.

Changes in serum amino acid levels in non-small cell lung cancer: a case-control study in Chinese population

Background

Previous studies have shown the alteration of amino acid (AA) profile in patients with non-small cell lung cancer (NSCLC). However, there is little data regarding AA profile in NSCLC in Chinese population. The aim of this study was to evaluate AA profile in Chinese NSCLC patients, explore its utility in sample classification and further discuss its related metabolic pathways.

Methods

The concentrations of 22 AAs in serum samples from 200 patients with NSCLC and 202 healthy controls were determined by liquid chromatography-tandem mass spectrometer (LC-MS/MS). AA levels in different tumor stages and histological types were also discussed. The performance of AA panel in classifying the cases and controls was evaluated in the training data set and validation data set based on the receiver operating characteristic (ROC) curve, and the important metabolic pathways were identified.

Results

The concentrations of tryptophan (Trp), phenylalanine (Phe), isoleucine (Ile), glycine (Gly), serine (Ser), aspartic acid (Asp), asparagine (Asn), cystein (Cys), glutamic acid (Glu), ornithine (Orn) and citrulline (Cit) were significantly altered in NSCLC patients compared with controls (all P-FDR < 0.05). Among these, four AAs including Asp, Cys, Glu and Orn were substantially up-regulated in NSCLC patients (FC ≥ 1.2). AA levels were significantly altered in patients with late-stage NSCLC, but not in those with early-stage when comparing with healthy controls. In terms of histological type, these AAs were altered in both adenocarcinoma and squamous cell carcinoma. For discrimination of NSCLC from controls, the area under the ROC curve (AUC) was 0.80 (95% CI [0.74-0.85]) in the training data set and 0.79 (95%CI [0.71-0.87]) in the validation data set. The AUCs for early-stage and late-stage NSCLC were 0.75 (95% CI [0.68-0.81]) and 0.86 (95% CI [0.82-0.91]), respectively. Moreover, the model showed a better performance in the classification of squamous cell carcinoma (AUC = 0.90, 95% CI [0.85-0.95]) than adenocarcinoma (AUC = 0.77, 95% CI [0.71-0.82]) from controls. Three important metabolic pathways were involved in the alteration of AA profile, including Gly, Ser and Thr metabolism; Ala, Asp and Glu metabolism; and Arg biosynthesis.

Conclusions

The levels of several AAs in serum were altered in Chinese NSCLC patients. These altered AAs may be utilized to classify the cases from the controls. Gly, Ser and Thr metabolism; Ala, Asp and Glu metabolism and Arg biosynthesis pathways may play roles in metabolism of the NSCLC patient.

Genetically Predicted Circulating Concentrations of Alanine and Alanine Aminotransferase Were Associated with Prostate Cancer Risk

Object

Prostate cancer is one of the leading malignancies in men worldwide. Previous observational studies have linked amino acids and transaminase with altered risk of prostate cancer. However, whether these associations were causal remained unclear. Therefore, we conducted a Mendelian randomization (MR) to assess their potential causal associations.

Methods

Summary-level data for prostate cancer were obtained from a meta-analysis of genome-wide association studies (GWAS) including 79,148 prostate cancer cases and 61,106 controls of European descent. Instrumental variables (IVs) of amino acids and alanine aminotransferase (ALT) were obtained from a GWAS of 86,507 European individuals and a GWAS of 312,572 participants from the UK Biobank, respectively. MR analyses were performed using inverse-variance-weighted (IVW), likelihood-based, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test and MR-Egger regression.

Results

Genetically predicted circulating concentrations of alanine were associated with an increased risk of prostate cancer (odds ratio (OR): 1.16, 95% confidence interval (CI): 1.01-1.33, P=0.037 by IVW). Consistently, genetically predicted ALT was inversely associated with the risk of prostate cancer (OR: 0.43, 95% CI: 0.27-0.68, P=3.28×10^-4 by IVW). MR-Egger regression did not indicate evidence of directional pleiotropy and sensitivity analyses yielded consistent associations.

Conclusion

Our study revealed that genetically predicted circulating alanine and ALT levels were associated with an altered risk of prostate cancer, suggesting their potential roles in the development of prostate cancer. Whether targeting alanine, ALT or its downstream effectors are helpful in reducing prostate cancer incidence warrants further investigation.

Metabolomic Profiling in Lung Cancer: A Systematic Review

Lung cancer continues to be a significant burden worldwide and remains the leading cause of cancer-associated mortality. Two considerable challenges posed by this disease are the diagnosis of 61% of patients in advanced stages and the reduced five-year survival rate of around 4%. Noninvasively collected samples are gaining significant interest as new areas of knowledge are being sought and opened up. Metabolomics is one of these growing areas. In recent years, the use of metabolomics as a resource for the study of lung cancer has been growing. We conducted a systematic review of the literature from the past 10 years in order to identify some metabolites associated with lung cancer. More than 150 metabolites have been associated with lung cancer-altered metabolism. These were detected in different biological samples by different metabolomic analytical platforms. Some of the published results have been consistent, showing the presence/alteration of specific metabolites. However, there is a clear variability due to lack of a full clinical characterization of patients or standardized patients selection. In addition, few published studies have focused on the added value of the metabolomic profile as a means of predicting treatment response for lung cancer. This review reinforces the need for consistent and systematized studies, which will help make it possible to identify metabolic biomarkers and metabolic pathways responsible for the mechanisms that promote tumor progression, relapse and eventually resistance to therapy.

Tryptophan and Its Metabolites in Lung Cancer: Basic Functions and Clinical Significance

Lung cancer is the most lethal malignancy worldwide. Recently, it has been recognized that metabolic reprogramming is a complex and multifaceted factor, contributing to the process of lung cancer. Tryptophan (Try) is an essential amino acid, and Try and its metabolites can regulate the progression of lung cancer. Here, we review the pleiotropic functions of the Try metabolic pathway, its metabolites, and key enzymes in the pathogenic process of lung cancer, including modulating the tumor environment, promoting immune suppression, and drug resistance. We summarize the recent advance in therapeutic drugs targeting the Try metabolism and kynurenine pathway and their clinical trials.

Preoperative AminoIndex Cancer Screening (AICS) abnormalities predict postoperative recurrence in patients undergoing curative resection for non-small cell lung cancer

BACKGROUND

AminoIndex™ Cancer Screening (AICS (lung)) was developed as a screening test for lung cancer using a multivariate analysis of plasma-free amino acid (PFAA) profiles. According to the developed index composed of PFAA, the probability of lung cancer was categorized into AICS (lung) ranks A, B, and C in order of increasing risk. The aim of the present study was to investigate the relationship between the preoperative AICS (lung) rank and surgical outcomes in patients who underwent curative resection for non-small cell lung cancer (NSCLC).

METHODS

Preoperative blood samples were collected from 297 patients who underwent curative resection for NSCLC between 2006 and 2015. PFAA concentrations were measured. The relationship between the preoperative AICS (lung) rank and clinicopathological factors was examined. The effects of the preoperative AICS (lung) rank on postoperative outcomes were also analyzed.

RESULTS

The AICS (lung) rank was A in 93 patients (31.3%), B in 82 (27.6%), and C in 122 (41.1%). The AICS (lung) rank did not correlate with any clinicopathological factors, except for age. Based on follow-up data (median follow-up period of 6 years), postoperative recurrence was observed in 22 rank A patients (23.7%), 15 rank B (18.3%) and 49 rank C (40.2%). In the univariate analysis, preoperative AICS (lung) rank C was a worse factor of recurrence-free survival (p = 0.0002). The multivariate analysis identified preoperative AICS (lung) rank C (HR: 2.17, p = 0.0005) as a significant predictor of postoperative recurrence, particularly in patients with early-stage disease or adenocarcinoma.

CONCLUSION

Preoperative AICS (lung) rank C is a high-risk predictor of postoperative recurrence in patients undergoing curative resection for NSCLC.

Prognostic Role of Serum Amino Acids in Head and Neck Cancer

Introduction

Serum amino acid (AA) profiles represent a valuable tool in the metabolic assessment of cancer patients; still, information on the AA pattern in head and neck cancer (HNC) patients is insufficient. The aim of the study was to assess whether serum AA levels were associated with the stage of neoplastic disease and prognosis in primary HNC patients.

Methods

Two hundred and two primary HNC patients were included in the study. Thirty-one AAs and derivatives were measured in serum through an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). The association between AA concentrations and the stage (advanced versus early) of HNC was estimated using a multivariable logistic regression model. A multivariable Cox regression model was used to evaluate the prognostic significance of each AA.

Results

At the multivariable logistic regression analysis, increased levels of alpha-aminobutyric acid, aminoadipic acid, histidine, proline, and tryptophan were associated with a reduced risk of advanced stage HNC, while high levels of beta-alanine, beta-aminobutyric acid, ethanolamine, glycine, isoleucine, 4-hydroxyproline, and phenylalanine were associated with an increased risk of advanced stage HNC. Furthermore, at multivariate analysis, increased levels of alpha-aminobutyric acid were associated with increased overall survival (OS), while high levels of arginine, ethanolamine, glycine, histidine, isoleucine, 4-hydroxyproline, leucine, lysine, 3-methylhistidine, phenylalanine, and serine were associated with decreased OS.

Conclusions

Our study suggests that AA levels are associated with the stage of disease and prognosis in patients with HNC. More study is necessary to evaluate if serum AA levels may be considered a hallmark of HNC and prove to be clinically useful markers of disease status and prognosis in HNC patients.

Association between Metabolites and the Risk of Lung Cancer: A Systematic Literature Review and Meta-Analysis of Observational Studies

Globally, lung cancer is the most prevalent cancer type. However, screening and early detection is challenging. Previous studies have identified metabolites as promising lung cancer biomarkers. This systematic literature review and meta-analysis aimed to identify metabolites associated with lung cancer risk in observational studies. The literature search was performed in PubMed and EMBASE databases, up to 31 December 2019, for observational studies on the association between metabolites and lung cancer risk. Heterogeneity was assessed using the I2 statistic and Cochran's Q test. Meta-analyses were performed using either a fixed-effects or random-effects model, depending on study heterogeneity. Fifty-three studies with 297 metabolites were included. Most identified metabolites (252 metabolites) were reported in individual studies. Meta-analyses were conducted on 45 metabolites. Five metabolites (cotinine, creatinine riboside, N-acetylneuraminic acid, proline and r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene) and five metabolite groups (total 3-hydroxycotinine, total cotinine, total nicotine, total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (sum of concentrations of the metabolite and its glucuronides), and total nicotine equivalent (sum of total 3-hydroxycotinine, total cotinine and total nicotine)) were associated with higher lung cancer risk, while three others (folate, methionine and tryptophan) were associated with lower lung cancer risk. Significant heterogeneity was detected across most studies. These significant metabolites should be further evaluated as potential biomarkers for lung cancer.

Clinical significance of circulating tumor cells and metabolic signatures in lung cancer after surgical removal

Background

Lung cancer (LC) remains the deadliest form of cancer globally. While surgery remains the optimal treatment strategy for individuals with early-stage LC, what the metabolic consequences are of such surgical intervention remains uncertain.

Methods

Negative enrichment-fluorescence in situ hybridization (NE-FISH) was used in an effort to detect circulating tumor cells (CTCs) in pre- and post-surgery peripheral blood samples from 51 LC patients. In addition, targeted metabolomics analyses, multivariate statistical analyses, and pathway analyses were used to explore surgery-associated metabolic changes.

Results

LC patients had significantly higher CTC counts relative to healthy controls with 66.67% of LC patients having at least 1 detected CTC before surgery. CTC counts were associated with clinical outcomes following surgery. In a targeted metabolomics analysis, we detected 34 amino acids, 147 lipids, and 24 fatty acids. When comparing LC patients before and after surgery to control patients, metabolic shifts were detected via PLS-DA and pathway analysis. Further surgery-associated metabolic changes were identified when comparing LA (LC patients after surgery) and LB (LC patients before surgery) groups. We identified SM 42:4, Ser, Sar, Gln, and LPC 18:0 for inclusion in a biomarker panel for early-stage LC detection based upon an AUC of 0.965 (95% CI 0.900–1.000). This analysis revealed that SM 42:2, SM 35:1, PC (16:0/14:0), PC (14:0/16:1), Cer (d18:1/24:1), and SM 38:3 may offer diagnostic and prognostic benefits in LC.

Conclusions

These findings suggest that CTC detection and plasma metabolite profiling may be an effective means of diagnosing early-stage LC and identifying patients at risk for disease recurrence.

The L-Type Amino Acid Transporter LAT1-An Emerging Target in Cancer

Chronic proliferation is a major hallmark of tumor cells. Rapidly proliferating cancer cells are highly dependent on nutrients in order to duplicate their cell mass during each cell division. In particular, essential amino acids are indispensable for proliferating cancer cells. Their uptake across the cell membrane is tightly controlled by membrane transporters. Among those, the L-type amino acid transporter LAT1 (SLC7A5) has been repeatedly found overexpressed in a vast variety of cancers. In this review, we summarize the most recent advances in our understanding of the role of LAT1 in cancer and highlight preclinical studies and drug developments underlying the potential of LAT1 as therapeutic target.

Methods for Absolute Quantification of Human Plasma Free Amino Acids by High-Performance Liquid Chromatography/Electrospray Ionization Mass Spectrometry Using Precolumn Derivatization

Plasma free amino acid (PFAA) concentrations in humans are affected by various diseases. However, the variations caused are not dramatic, so a high accurate and precise method for analyzing PFAAs is required. The PFAA analysis protocol described in this chapter covers blood sampling, sample pretreatment, amino acid derivatization, and LC-MS analysis. Each procedure is important for accurate and precise quantification.In the protocol, a human blood sample is collected using an EDTA-2Na or 2K vacuum collection tube and then immediately cooled in water mixed with crushed ice. The sample is then centrifuged on cooling to allow a plasma sample to be removed. A stable-isotope-labeled internal standard solution is added to the plasma, and then the plasma is deproteinized with acetonitrile. The amino acids in the plasma are then derivatized using 3-aminopyridyl-N-hydroxysuccinimidyl carbamate (APDS) reagent which is designed for LC-MS analysis. The derivatized amino acids are separated by reverse-phase HPLC and detected by electrospray ionization mass spectrometry. Using this method, 21 amino acids in human plasma can be analyzed with a 12 min cycle. The accuracy and precision are both better than the required criteria given by the US Food and Drug Administration in guidance of Bioanalytical Method Validation.

Normalization of abnormal plasma amino acid profile-based indexes in patients with gynecological malignant tumors after curative treatment

BACKGROUND

We developed a novel plasma amino acid profile-based index (API) to detect ovarian, uterine, cervical, and endometrial cancers. In this study, we aimed to evaluate whether abnormal API values could be normalized after curative treatment in patients with gynecological malignant tumors.

METHODS

Patients with gynecological cancer with abnormal API values were included in this study. Pre-operative absolute API values were compared with those after curative treatment. The normalization rates of API values in patients negative for the expression of three well-known tumor markers (SCC, CA125, and CA19-9) were also evaluated. In addition, related amino acid profiles in healthy controls and patients under pre- and post-treatment conditions were analyzed.

RESULTS

Among 94 patients with abnormal pre-operative API values, the median API value was decreased from 9.52 to 2.17 after treatment (normalization rate: 88.3%). The decreased ranges were similar in patients with adenocarcinoma (6.28; 95% confidence interval [CI]: 5.43-6.95) and squamous carcinoma (7.44; 95% CI: 3.04-8.46). In 93.5% (43/46) of patients negative for tumor markers prior to operation, API values were normalized after the successful treatment. In addition, some pre-operative abnormal amino acid profiles, including Ile, Trp, and His, were reversibly normalized after treatment.

CONCLUSION

The API is a promising tumor marker in gynecological malignancies for the diagnosis of remission, particularly in patients negative for general tumor markers. Further studies are needed to explore the mechanisms related to the normalization of abnormal amino acid profiles.

Biosynthetic energy cost for amino acids decreases in cancer evolution

Rapidly proliferating cancer cells have much higher demand for proteinogenic amino acids than normal cells. The use of amino acids in human proteomes is largely affected by their bioavailability, which is constrained by the biosynthetic energy cost in living organisms. Conceptually distinct from gene-based analyses, we introduce the energy cost per amino acid (ECPA) to quantitatively characterize the use of 20 amino acids during protein synthesis in human cells. By analyzing gene expression data from The Cancer Genome Atlas, we find that cancer cells evolve to utilize amino acids more economically by optimizing gene expression profile and ECPA shows robust prognostic power across many cancer types. We further validate this pattern in an experimental evolution of xenograft tumors. Our ECPA analysis reveals a common principle during cancer evolution.

Proliferating cancer cells have a high demand for amino acids. Here, Zhang et al. show that cancer cells evolve towards gene expression profiles that use amino acids with lower biosynthetic energy costs, and demonstrate the potential prognostic utility of quantifying the extent of this adaptation.

Post-operative AICS status in completely resected lung cancer patients with pre-operative AICS abnormalities: predictive significance of disease recurrence

The AminoIndex^TM Cancer Screening (AICS) system, a plasma-free amino acid (PFAA)-based multivariate discrimination index, is a blood screening test for lung cancer based on the comparison of PFAA concentrations between patients with lung cancer and healthy controls. Pre- and post-operative AICS values were compared among 72 patients who underwent curative resection for lung cancer. Post-operative changes in PFAA concentrations were also evaluated. AICS values were classified as rank A (0.0–4.9), B (5.0–7.9), or C (8.0–10.0). Rank B–C patients were evaluated for outcomes and post-operative changes in their AICS values. Twenty-three of the 44 pre-operative rank B–C patients experienced post-operative reductions in AICS rank. Only one patient experienced cancer recurrence. Post-operative changes in PFAA concentrations were associated with the risk of post-operative cancer recurrence (p = 0.001). Multivariate analysis revealed that the absence of a post-operative reduction in AICS rank independently predicted cancer recurrence (hazard ratio: 14.28; p = 0.012). The majority of patients had high pre-operative AICS values and exhibited a reduction in AICS rank after curative resection. However, the absence of a post-operative reduction in AICS rank was associated with cancer recurrence, suggesting that AICS rank may be a sensitive marker of post-operative recurrence.

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.

A targeted metabolomic procedure for amino acid analysis in different biological specimens by ultra-high-performance liquid chromatography-tandem mass spectrometry

INTRODUCTION

Amino acid analysis in biological fluids is essential for the study of inborn errors of metabolism (IEM) and other diseases.

OBJECTIVES

Our aim was to develop a UPLC-MS/MS procedure for the analysis of 25 amino acids and identification of 17 related compounds.

METHODS

Sample treatment conditions were optimized for plasma, urine, cerebrospinal fluid (CSF) and dried blood spots. Amino acids and related compounds were analyzed on a Waters ACQUITY UPLC H-class instrument with a reversed-phase C-18 column using water and acetonitrile with 0.1% formic acid as the mobile phases (run time = 9 min). The detection was performed with a Waters Xevo TQD triple-quadrupole mass spectrometer using positive electrospray ionization in the multiple reaction monitoring mode.

RESULTS

The method linearity, intra-assay and inter-assay precision, detection limit, quantification limit and trueness analysis displayed adequate results in both physiological and pathological conditions. Method comparison was performed between UPLC-MS/MS and ion exchange chromatography (IEC) with ninhydrin derivatization, and the methods showed good agreement, except for 4-hydroxyproline, aspartate and citrulline. Paediatrics age-related reference values in plasma, urine and CSF were established and patients with different IEM were easily identified.

CONCLUSION

We report a modified UPLC-MS/MS procedure for the analysis of 42 amino acids and related compounds in different specimens. The method is fast, sensitive and robust, and it has been validated to be an alternative to the traditional IEC procedure as the routine method used in metabolic laboratories. The method greatly decreases the run time of the analysis while displaying good metrological results.

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.

Multiplatform plasma fingerprinting in cancer cachexia: a pilot observational and translational study

BACKGROUND

Cachexia is a metabolic syndrome that affects up to 50-80% of cancer patients. The pathophysiology is characterized by a variable combination of reduced food intake and abnormal metabolism, including systemic inflammation and negative protein and energy balance. Despite its high clinical significance, defined diagnostic criteria and established therapeutic strategies are lacking. The 'omics' technologies provide a global view of biological systems. We hypothesize that blood-based metabolomics might identify findings in cachectic patients that could provide clues to gain knowledge on its pathophysiology, and eventually postulate new therapeutic strategies.

METHODS

This is a cross-sectional observational study in two cohorts of cancer patients, with and without cachexia. Patients were consecutively recruited from routine clinical practice of a General Oncology Department at '12 de Octubre' University Hospital. Selected clinical and biochemical features were collected. Blood metabolite fingerprinting was performed using three analytical platforms, gas chromatography coupled to mass spectrometry (GC-MS), capillary electrophoresis coupled to mass spectrometry (CE-MS), and liquid chromatography coupled to mass spectrometry (LC-MS). Besides, we performed pathway-based metabolite analyses to obtain more information on biological functions.

RESULTS

A total of 15 subjects were included in this study, 8 cachectic and 7 non-cachectic patients. Metabolomic analyses were able to correctly classify their samples in 80% (GC-MS), 97% (CE-MS), 96% [LC-MS (positive mode)], and 89% [LC-MS (negative mode)] of the cases. The most prominent metabolic alteration in plasma of cachectic patients was the decrease of amino acids and derivatives [especially arginine, tryptophan, indolelactic acid, and threonine, with 0.4-fold change (FC) compared with non-cachectic patients], along with the reduction of glycerophospholipids [mainly lysophosphatidylcholines(O-16:0) and lysophosphatidylcholines(20:3) sn-1, FC = 0.1] and sphingolipids [SM(d30:0), FC = 0.5]. The metabolite with the highest increase was cortisol (FC = 1.6). Such alterations suggest a role of the following metabolic pathways in the pathophysiology of cancer cachexia: arginine and proline metabolism; alanine, aspartate, and glutamate metabolism; phenylalanine metabolism; lysine degradation; aminoacyl-tRNA biosynthesis; fatty acid elongation in mitochondria; tricarboxylic acids cycle; among others.

CONCLUSIONS

These findings suggest that plasma amino acids and lipids profiling has great potential to find the mechanisms involved in the pathogenesis of cachexia. Metabolic profiling of plasma from cancer patients show differences between cachexia and non-cachexia in amino acids and lipids that might be related to mechanisms involved in its pathophysiology. A better understanding of these mechanisms might identify novel therapeutic approaches to palliate this unmet medical condition.

Protein restriction and cancer

Protein restriction without malnutrition is currently an effective nutritional intervention known to prevent diseases and promote health span from yeast to human. Recently, low protein diets are reported to be associated with lowered cancer incidence and mortality risk of cancers in human. In murine models, protein restriction inhibits tumor growth via mTOR signaling pathway. IGF-1, amino acid metabolic programing, FGF21, and autophagy may also serve as potential mechanisms of protein restriction mediated cancer prevention. Together, dietary intervention aimed at reducing protein intake can be beneficial and has the potential to be widely adopted and effective in preventing and treating cancers.

The construction of a panel of serum amino acids for the identification of early chronic kidney disease patients

BACKGROUND

Serum creatinine, urea, and cystatin-c are standardly used for the evaluation of renal function in the clinic. However, some patients have chronic kidney disease but still retain kidney function; a conventional serum index in these patients can be completely normal. Serum amino acid levels can reflect subtle changes in metabolism and are closely related to renal function. Here, we investigated how amino acids change as renal impairment increases.

METHODS

Subjects were divided into three groups by renal function glomerular filtration rate: healthy controls, patients with chronic kidney disease with normal kidney function, and patients with chronic kidney disease with decreased kidney function group. We identified 11 amino acids of interest using LC-MS/MS on MRM (+) mode.

RESULTS

Statistical analysis indicated that alanine (ALA), valine (VAL), and tyrosine (TYR) decrease with renal function impairment, whereas phenylalanine (PHE) and citrulline (CIT) increase. We tried to construct a diagnostic model utilizing a combination of amino acids capable of identifying early chronic kidney disease patients. The accuracy, specificity, and sensitivity of the combining predictors were 86.9%, 84.6%, and 90.9%, respectively, which is superior to the reported values for serum creatinine, urea, and cystatin-c.

CONCLUSION

Our data suggest that serum amino acid levels may supply important information for the early detection of chronic kidney disease. We are the first to establish a diagnostic model utilizing serum levels of multiple amino acids for the diagnosis of patients with early-stage chronic kidney disease.

A pilot study identifying a potential plasma biomarker for determining EGFR mutations in exons 19 or 21 in lung cancer patients

The most common type of lung cancer is non-small cell lung cancer (NSCLC), which is frequently characterized by a mutation in the epidermal growth factor receptor (EGFR). Determining the presence of an EGFR mutation in lung cancer is important, as it determines the type of treatment that a patients will receive. Therefore, the aim of the present study was to apply high-resolution metabolomics (HRM) using liquid chromatography-mass spectrometry to identify significant compounds in human plasma samples obtained from South Korean NSCLC patients, as potential biomarkers for providing early detection and diagnosis of minimally-invasive NSCLC. The metabolic differences between lung cancer patients without EGFR mutations were compared with patients harboring EGFR mutations. Univariate analysis was performed, with a false discovery rate of q=0.05, in order to identify significant metabolites between the two groups. In addition, hierarchical clustering analysis was performed to discriminate between the metabolic profiles of the two groups. Furthermore, the significant metabolites were identified and mapped using Mummichog software, in order to generate a potential metabolic network model. Using metabolome-wide association studies, metabolic alterations were identified. Linoleic acid [303.23 m/z, (M+Na)⁺], 5-methyl tetrahydrofolate [231.10 m/z, (M+2H)⁺] and N-succinyl-L-glutamate-5 semialdehyde [254.06 m/z, (M+Na)⁺], were observed to be elevated in patients harboring EGFR mutations, whereas tetradecanoyl carnitine [394.29 m/z, (M+Na)⁺] was observed to be reduced. This suggests that these compounds may be affected by the EGFR mutation. In conclusion, the present study identified four potential biomarkers in patients with EGFR mutations, using HRM combined with pathway analysis. These results may facilitate the development of novel diagnostic tools for EGFR mutation detection in patients with lung cancer.

Plasma-free amino acid profiles are predictors of cancer and diabetes development

Type 2 diabetes (T2D) and cancers are two major causes of morbidity and mortality worldwide. Nowadays, there is convincing evidence of positive associations between T2D and the incidence or prognosis of a wide spectrum of cancers, for example, breast, colon, liver and pancreas. Many observational studies suggest that certain medications used to treat hyperglycemia (or T2D) may affect cancer cells directly or indirectly. The potential mechanisms of the direct T2D cancer links have been hypothesized to be hyperinsulinemia, hyperglycemia and chronic inflammation; however, the metabolic pathways that lead to T2D and cancers still remain elusive. Plasma-free amino acid (PFAA) profiles have been highlighted in their associations with the risks of developing T2D and cancers in individuals with different ethnic groups and degree of obesity. The alterations of PFAAs might be predominately caused by the metabolic shift resulted from insulin resistance. The underlying mechanisms have not been fully elucidated, in particular whether the amino acids are contributing to these diseases development in a causal manner. This review addresses the molecular and clinical associations between PFAA alterations and both T2D and cancers, and interprets possible mechanisms involved. Revealing these interactions and mechanisms may improve our understanding of the complex pathogenesis of diabetes and cancers and improve their treatment strategies.

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.

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.

Real-Time Quantification of Amino Acids in the Exhalome by Secondary Electrospray Ionization–Mass Spectrometry: A Proof-of-Principle Study

BACKGROUND

Amino acids are frequently determined in clinical chemistry. However, current analysis methods are time-consuming, invasive, and suffer from artifacts during sampling, sample handling, and sample preparation. We hypothesized in this proof-of-principle study that plasma concentrations of amino acids can be estimated by measuring their concentrations in exhaled breath. A novel breath analysis technique described here allows such measurements to be carried out in real-time and noninvasively, which should facilitate efficient diagnostics and give insights into human physiology.

METHODS

The amino acid profiles in 37 individuals were determined by ion-exchange HPLC in blood plasma and simultaneously in breath by secondary electrospray ionization coupled to high-resolution mass spectrometry. Participants were split into training and test sets to validate the analytical accuracy. Longitudinal profiles in 3 individuals were additionally obtained over a 12-h period.

RESULTS

Concentrations of 8 slightly volatile amino acids (A, V, I, G, P, K, F, Orn) could be determined in exhaled breath with a CV of &lt;10%. Exhalome validation studies yielded high accuracies for each of these amino acids, on average only 3% less compared to plasma concentrations (95% CI ±13%). Higher variations were found only for amino acids with a low plasma concentration.

CONCLUSIONS

This study demonstrates for the first time that amino acids can be quantified in the human breath and that their concentrations correlate with plasma concentrations. Although this noninvasive technique needs further investigation, exhalome analysis may provide significant benefits over traditional, offline analytical methods.

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.

Perioperative dynamics and significance of amino acid profiles in patients with cancer

Background

Metabolome analysis including amino acid profile is under investigation as an approach in cancer screening. The present study aims to analyze plasma free amino acid (PFAA) profiles in cancer patients and investigate their potential as biomarkers of malignancy.

Methods

Plasma samples from 56 gastric cancer patients, 28 breast cancer patients, 33 thyroid cancer patients, and 137 age-matched healthy controls were collected in the study. PFAA levels were measured and their perioperative alterations were analyzed. Biological effects of ten cancer-related amino acids were further validated in gastric and breast cancer cells.

Results

We found that PFAA profiles of cancer patients differed significantly from those of healthy controls. Decreased concentrations of PFAAs were associated with lymph node metastases in gastric cancer. Levels of PFAAs such as aspartate and alanine increased after tumor resection. PFAA levels correlated with clinical tumor markers in gastric cancer patients and pathological immunohistochemistry markers in breast cancer patients. Specifically, alanine, arginine, aspartate and cysteine had proliferative effects on breast cancer cells. Proliferation of gastric cancer cells was promoted by cysteine, but inhibited by alanine and glutamic acid. Furthermore, alanine treatment decreased total and stable fraction of gastric cancer cells, and alanine and glutamic acid induced apoptosis of gastric cancer cells.

Conclusions

PFAA patterns in cancer patients are altered perioperatively. Tumor-related amino acids identified by dynamic study of PFAA patterns may have the potential to be developed as novel biomarkers for diagnosis and prognosis of cancer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0408-1) contains supplementary material, which is available to authorized users.

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3-(CH2)n-) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol.

Plasma free amino acid profiling of esophageal cancer using high-performance liquid chromatography spectroscopy

AIM: To perform plasma free amino acid (PFAA) profiling of esophageal squamous cell carcinoma (ESCC) patients at different pathological stages and healthy subjects.

METHODS: Plasma samples from ESCC patients (n = 51) and healthy control adults (n = 60) were analyzed by high-performance liquid chromatography (HPLC). The ESCC patients included moderate/poorly-differentiation (n = 24), lymph node metastasis (n = 17) and clinical stage > Ib2 (n = 36). Partial least squares discriminant analysis was performed to demonstrate that the PFAA metabolic patterns enabled discrimination between ESCC patients and controls, and the Student t test was applied to assess significant differences in PFAA concentrations between the two groups.

RESULTS: There were significant differences in the PFAA profiles between controls and ESCC patients. Compared with healthy controls, the levels of Asp, Glu, Gly, His, Thr, Tau, Ala, Met, Ile, Leu, and Phe were decreased in ESCC patients, but Cys was increased. There exists a strong correlation between PFAA profiles and clinicopathological characteristics in ESCC patients. The levels of many PFAAs (i.e., Glu, Asp, Ser, Gly, Tau, Ala, Tyr, Val, Ile, and Leu) were related to pathological grading, lymph node metastasis, and ESCC clinical stage. Very good discrimination between ESCC patients and control subjects was achieved by multivariate modeling of plasma profiles.

CONCLUSION: HPLC-based plasma profiling analysis was shown to be an effective approach to differentiate between ESCC patients and controls. PFAA profiles may have potential value for screening or diagnosing ESCC.

Metabolic profiling of biofluids: potential in lung cancer screening and diagnosis

The knowledge that the organism's metabolome is a potentially informative mirror of the impact of disease and its dynamics has led to promising developments in cancer research, strongly geared toward the discovery of new biomarkers of disease onset and progression. The present text reviews the advances made in the last 10 years in lung cancer research making use of the metabolomics strategies, particularly concerning metabolite profiling of human biofluids (blood serum and plasma, urine and others), expected to reflect the deviant metabolic behavior of lung tumors. The main goal of this article is to provide the reader with an up-to-date summary of the main metabolic variations taking place in biofluids, in relation to lung cancer, as well as of the analytical strategies employed to unveil them. Furthermore, particular needs and challenges are identified and possible developments envisaged.

Tumor-dependent increase of serum amino acid levels in breast cancer patients has diagnostic potential and correlates with molecular tumor subtypes

Background

Malignancies induce changes in the levels of serum amino acids (AA), which may offer diagnostic potential. Furthermore, changes in AA levels are associated with immune cell function. In this study, serum AA levels were studied in breast cancer patients versus patients with benign breast lesions.

Methods

In a prospective study, serum levels of 15 AA were measured by high performance liquid chromatography before and after surgery in 41 breast cancer patients (BrCA) and nine patients with benign breast lesions (healthy donors, HD). Results were analyzed in relation to clinical tumor data and tested against immunological flow cytometry data. Principal component analysis was performed and the accuracy of AA levels as a potential diagnostic tool was tested.

Results

Pre- but not postoperative serum AA levels were increased in BrCA in eight out of 15 AA compared with HD. Serum AA levels were highest in the most aggressive (basal-like) as compared with the least aggressive tumor subtype (luminal A). A principal component (PC1) of all measured AA correlated with a mainly pro-inflammatory immune profile, while a second one (PC2, selectively considering AA preoperatively differing between HD and BrCA) could predict health state with an area under the curve of 0.870.

Conclusions

Breast cancer shows a tumor-dependent impact on serum AA levels, which varies with intrinsic tumor subtypes and is associated with a pro-inflammatory state. Serum AA levels need further evaluation as a potential diagnostic tool.