Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma

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.

Integrated omics and gene expression analysis identifies the loss of metabolite-metabolite correlations in small cell lung cancer

BACKGROUND AND OBJECTIVE

Small cell lung cancer (SCLC) is the most aggressive type of lung carcinoma with high metastatic potential and chemoresistance upon relapse. Cancer cells remodel the existing metabolic pathways for their benefits and the perturbations in cellular metabolism are the hallmark of cancer. However, the extent of these changes remains largely unknown for SCLC.

MATERIALS AND METHODS

We characterized the metabolic perturbations in SCLC cells (SCLCC) by metabolomics. Large-scale correlation analysis was performed between metabolites. Targeted proteomics and gene expression analysis were employed to investigate the changes of key enzymes and genes in the disturbed pathways.

RESULTS

We found dramatic decrease of metabolite-metabolite correlations in SCLCC compared with normal control cells and non-small cell lung cancer cells. Pathway analysis revealed that the loss of correlations was associated with the alternations of fatty acid oxidation, urea cycle, and purine salvage pathway in SCLCC. Targeted proteomics and gene expression analysis confirmed significant changes of the expression for the key enzymes and genes in the pathways in SCLCC including the upregulation of carbamoyl phosphate synthase 1 (urea cycle) and carnitine palmitoyltransferase 1A (fatty acid oxidation), and the downregulation of hypoxanthine-guanine phosphoribosyltransferase and adenine phosphoribosyltransferase in purine salvage pathway.

CONCLUSION

We demonstrated the loss of metabolite-metabolite correlations in SCLCC associated with the upregulation of fatty acid oxidation and urea cycle and the downregulation of purine salvage pathways. Our findings provide insights into the metabolic reprogramming in SCLCC and highlight the potential therapeutic targets for the treatment of SCLC.

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.

Simultaneous quantification of serum monounsaturated and polyunsaturated phosphatidylcholines as potential biomarkers for diagnosing non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the most common malignancies worldwide. In this study, we investigated Ultrahigh Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry and Gas Chromatography Time-of-Flight/Mass Spectrometry-based non-targeted metabolomic profiles of serum samples obtained from early-stage NSCLC patients and healthy controls (HC). Metabolic pathways and the biological relevance of potential biomarkers were extensively studied to gain insights into dysregulated metabolism in NSCLC. The identified biomarker candidates were further externally validated via a targeted metabolomics analysis. The global metabolomics profiles could clearly distinguish NSCLC patients from HC. Phosphatidylcholine (PC) levels were found to be dysregulated in glycerophospholipid (GPL) metabolism, which was the top altered pathway in early-stage NSCLC. Compared with those in HC, significant increases in the levels of saturated and monounsaturated PCs such as PC (15:0/18:1), PC (18:0/16:0) and PC (18:0/20:1) were observed in NSCLC. Additionally, relative to those in HC, the levels of 9 polyunsaturated PCs, namely, PC (17:2/2:0), PC (18:4/3:0), and PC (15:0/18:2), and so on were significantly decreased in NSCLC patients. A panel of 12 altered PCs had good diagnostic performance in differentiating early-stage NSCLC patients from HC, and these PCs may thus be used as serum biomarkers for the early diagnosis of NSCLC.

Upregulation of lactate dehydrogenase a by 14-3-3ζ leads to increased glycolysis critical for breast cancer initiation and progression

Metabolic reprogramming is a hallmark of cancer. Elevated glycolysis in cancer cells switches the cellular metabolic flux to produce more biological building blocks, thereby sustaining rapid proliferation. Recently, new evidence has emerged that metabolic dysregulation may occur at early-stages of neoplasia and critically contribute to cancer initiation. Here, our bioinformatics analysis of microarray data from early-stages breast neoplastic lesions revealed that 14-3-3ζ expression is strongly correlated with the expression of canonical glycolytic genes, particularly lactate dehydrogenase A (LDHA). Experimentally, increasing 14-3-3ζ expression in human mammary epithelial cells (hMECs) up-regulated LDHA expression, elevated glycolytic activity, and promoted early transformation. Knockdown of LDHA in the 14-3-3ζ-overexpressing hMECs significantly reduced glycolytic activity and inhibited transformation. Mechanistically, 14-3-3ζ overexpression activates the MEK-ERK-CREB axis, which subsequently up-regulates LDHA. In vivo, inhibiting the activated the MEK/ERK pathway in 14-3-3ζ-overexpressing hMEC-derived MCF10DCIS.COM lesions led to effective inhibition of tumor growth. Therefore, targeting the MEK/ERK pathway could be an effective strategy for intervention of 14-3-3ζ-overexpressing early breast lesions. Together, our data demonstrate that overexpression of 14-3-3ζ in early stage pre-cancerous breast epithelial cells may trigger an elevated glycolysis and transcriptionally up-regulating LDHA, thereby contributes to human breast cancer initiation.

Metabolomic screening of pre-diagnostic serum samples identifies association between α- and γ-tocopherols and glioblastoma risk

Glioblastoma is associated with poor prognosis with a median survival of one year. High doses of ionizing radiation is the only established exogenous risk factor. To explore new potential biological risk factors for glioblastoma, we investigated alterations in metabolite concentrations in pre-diagnosed serum samples from glioblastoma patients diagnosed up to 22 years after sample collection, and undiseased controls. The study points out a latent biomarker for future glioblastoma consisting of nine metabolites (γ-tocopherol, α-tocopherol, erythritol, erythronic acid, myo-inositol, cystine, 2-keto-L-gluconic acid, hypoxanthine and xanthine) involved in antioxidant metabolism. We detected significantly higher serum concentrations of α-tocopherol (p=0.0018) and γ-tocopherol (p=0.0009) in future glioblastoma cases. Compared to their matched controls, the cases showed a significant average fold increase of α- and γ-tocopherol levels: 1.2 for α-T (p=0.018) and 1.6 for γ-T (p=0.003). These tocopherol levels were associated with a glioblastoma odds ratio of 1.7 (α-T, 95% CI:1.0-3.0) and 2.1 (γ-T, 95% CI:1.2-3.8). Our exploratory metabolomics study detected elevated serum levels of a panel of molecules with antioxidant properties as well as oxidative stress generated compounds. Additional studies are necessary to confirm the association between the observed serum metabolite pattern and future glioblastoma development.

Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review

Lung cancer is the leading cause of cancer-related death. Next-generation metabolomics is becoming a powerful emerging technology for studying the systems biology and chemistry of health and disease. This mini review summarized the main platforms of next-generation metabolomics and its main applications in lung cancer including early diagnosis, pathogenesis, classifications and precision medicine. The period covers between 2009 and August, 2017. The major issues and future directions of metabolomics in lung cancer research and clinical applications were also discussed.

Integration of metabolomics, transcriptomics, and microRNA expression profiling reveals a miR-143-HK2-glucose network underlying zinc-deficiency-associated esophageal neoplasia

Esophageal squamous cell carcinoma (ESCC) in humans is a deadly disease associated with dietary zinc (Zn)-deficiency. In the rat esophagus, Zn-deficiency induces cell proliferation, alters mRNA and microRNA gene expression, and promotes ESCC. We investigated whether Zn-deficiency alters cell metabolism by evaluating metabolomic profiles of esophageal epithelia from Zn-deficient and replenished rats vs sufficient rats, using untargeted gas chromatography time-of-flight mass spectrometry (n = 8/group). The Zn-deficient proliferative esophagus exhibits a distinct metabolic profile with glucose down 153-fold and lactic acid up 1.7-fold (P < 0.0001), indicating aerobic glycolysis (the "Warburg effect"), a hallmark of cancer cells. Zn-replenishment rapidly increases glucose content, restores deregulated metabolites to control levels, and reverses the hyperplastic phenotype. Integration of metabolomics and our reported transcriptomic data for this tissue unveils a link between glucose down-regulation and overexpression of HK2, an enzyme that catalyzes the first step of glycolysis and is overexpressed in cancer cells. Searching our published microRNA profile, we find that the tumor-suppressor miR-143, a negative regulator of HK2, is down-regulated in Zn-deficient esophagus. Using in situ hybridization and immunohistochemical analysis, the inverse correlation between miR-143 down-regulation and HK2 overexpression is documented in hyperplastic Zn-deficient esophagus, archived ESCC-bearing Zn-deficient esophagus, and human ESCC tissues. Thus, to sustain uncontrolled cell proliferation, Zn-deficiency reprograms glucose metabolism by modulating expression of miR-143 and its target HK2. Our work provides new insight into critical roles of Zn in ESCC development and prevention.

Short overview on metabolomics approach to study pathophysiology of oxidative stress in cancer

Association of oxidative stress with carcinogenesis is well known, but not understood well, as is pathophysiology of oxidative stress generated during different types of anti-cancer treatments. Moreover, recent findings indicate that cancer associated lipid peroxidation might eventually help defending adjacent nonmalignant cells from cancer invasion. Therefore, untargeted metabolomics studies designed for advanced translational and clinical studies are needed to understand the existing paradoxes in oncology, including those related to controversial usage of antioxidants aiming to prevent or treat cancer. In this short review we have tried to put emphasis on the importance of pathophysiology of oxidative stress and lipid peroxidation in cancer development in relation to metabolic adaptation of particular types of cancer allowing us to conclude that adaptation to oxidative stress is one of the main driving forces of cancer pathophysiology. With the help of metabolomics many novel findings are being achieved thus encouraging further scientific breakthroughs. Combined with targeted qualitative and quantitative methods, especially immunochemistry, further research might reveal bio-signatures of individual patients and respective malignant diseases, leading to individualized treatment approach, according to the concepts of modern integrative medicine.

Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.

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.

Noninvasive Respiratory Metabolite Analysis Associated with Clinical Disease in Cetaceans: A Deepwater Horizon Oil Spill Study

Health assessments of wild cetaceans can be challenging due to the difficulty of gaining access to conventional diagnostic matrices of blood, serum and others. While the noninvasive detection of metabolites in exhaled breath could potentially help to address this problem, there exists a knowledge gap regarding associations between known disease states and breath metabolite profiles in cetaceans. This technology was applied to the largest marine oil spill in U.S. history (The 2010 Deepwater Horizon oil spill in the Gulf of Mexico). An accurate analysis was performed to test for associations between the exhaled breath metabolome and sonographic lung abnormalities as well as hematological, serum biochemical, and endocrine hormone parameters. Importantly, metabolites consistent with chronic inflammation, such as products of lung epithelial cellular breakdown and arachidonic acid cascade metabolites were associated with sonographic evidence of lung consolidation. Exhaled breath condensate (EBC) metabolite profiles also correlated with serum hormone concentrations (cortisol and aldosterone), hepatobiliary enzyme levels, white blood cell counts, and iron homeostasis. The correlations among breath metabolites and conventional health measures suggest potential application of breath sampling for remotely assessing health of wild cetaceans. This methodology may hold promise for large cetaceans in the wild for which routine collection of blood and respiratory anomalies are not currently feasible.

Exhaled breath condensate to discriminate individuals with different smoking habits by GC-TOF/MS

Smoking is a crucial factor in respiratory diseases and lung inflammation, which are the reasons for high mortality worldwide. Despite the negative impact that tobacco consumption causes on health, few metabolomics studies have compared the composition of biofluids from smoker and non-smoker individuals. Exhaled breath condensate (EBC) is one of the biofluids less employed for clinical studies despite its non-invasive sampling and the foreseeable relationship between its composition and respiratory diseases. EBC was used in this research as clinical sample to compare three groups of individuals: current smokers (CS), former smokers (FS) and never smokers (NS). Special attention was paid to the cumulative consumption expressed as smoked pack-year. The levels of 12 metabolites found statistically significant among the three groups of individuals were discussed to find an explanation to their altered levels. Significant compounds included monoacylglycerol derivatives, terpenes and other compounds, the presence of which could be associated to the influence of smoking on the qualitative and quantitative composition of the microbiome.

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.

Metabox: A Toolbox for Metabolomic Data Analysis, Interpretation and Integrative Exploration

Similar to genomic and proteomic platforms, metabolomic data acquisition and analysis is becoming a routine approach for investigating biological systems. However, computational approaches for metabolomic data analysis and integration are still maturing. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. With the number of options provided in each analysis module, it also supports data analysis of other 'omic' families. The toolbox is an R-based web application, and it is freely available at http://kwanjeeraw.github.io/metabox/ under the GPL-3 license.

Novel biomarker identification using metabolomic profiling to differentiate radiation necrosis and recurrent tumor following Gamma Knife radiosurgery

OBJECTIVE Following an initial response of brain metastases to Gamma Knife radiosurgery, regrowth of the enhancing lesion as detected on MRI may represent either radiation necrosis (a treatment-related inflammatory change) or recurrent tumor. Differentiation of radiation necrosis from tumor is vital for management decision making but remains difficult by imaging alone. In this study, gas chromatography with time-of-flight mass spectrometry (GC-TOF) was used to identify differential metabolite profiles of the 2 tissue types obtained by surgical biopsy to find potential targets for noninvasive imaging. METHODS Specimens of pure radiation necrosis and pure tumor obtained from patient brain biopsies were flash-frozen and validated histologically. These formalin-free tissue samples were then analyzed using GC-TOF. The metabolite profiles of radiation necrosis and tumor samples were compared using multivariate and univariate statistical analysis. Statistical significance was defined as p ≤ 0.05. RESULTS For the metabolic profiling, GC-TOF was performed on 7 samples of radiation necrosis and 7 samples of tumor. Of the 141 metabolites identified, 17 (12.1%) were found to be statistically significantly different between comparison groups. Of these metabolites, 6 were increased in tumor, and 11 were increased in radiation necrosis. An unsupervised hierarchical clustering analysis found that tumor had elevated levels of metabolites associated with energy metabolism, whereas radiation necrosis had elevated levels of metabolites that were fatty acids and antioxidants/cofactors. CONCLUSIONS To the authors' knowledge, this is the first tissue-based metabolomics study of radiation necrosis and tumor. Radiation necrosis and recurrent tumor following Gamma Knife radiosurgery for brain metastases have unique metabolite profiles that may be targeted in the future to develop noninvasive metabolic imaging techniques.

Proteomic profiling of lung adenocarcinoma indicates heightened DNA repair, antioxidant mechanisms and identifies LASP1 as a potential negative predictor of survival

BACKGROUND

Lung cancer is the leading cause of cancer mortality in the United States. Non-small cell lung cancer accounts for 85% of all lung cancers for which adenocarcinoma is the most common histological type. Management of lung cancer is hindered by high false-positive rates due to difficulty resolving between benign and malignant tumors. Better molecular analysis comparing malignant and non-malignant tissues will provide important evidence of the underlying biology contributing to tumorigenesis.

METHODS

We utilized a proteomics approach to analyze 38 malignant and non-malignant paired tissue samples obtained from current or former smokers with early stage (Stage IA/IB) lung adenocarcinoma. Statistical mixed effects modeling and orthogonal partial least squares discriminant analysis were used to identify key cancer-associated perturbations in the adenocarcinoma proteome. Identified proteins were subsequently assessed against clinicopathological variables.

RESULTS

Top cancer-associated protein alterations were characterized by: (1) elevations in APEX1, HYOU1 and PDIA4, indicative of increased DNA repair machinery and heightened anti-oxidant defense mechanisms; (2) increased LRPPRC, STOML2, COPG1 and EPRS, suggesting altered tumor metabolism and inflammation; (3) reductions in SPTB, SPTA1 and ANK1 implying dysregulation of membrane integrity; and (4) decreased SLCA41 suggesting altered pH regulation. Increased protein levels of HYOU1, EPRS and LASP1 in NSCLC adenocarcinoma was independently validated by tissue microarray immunohistochemistry. Immunohistochemistry for HYOU1 and EPRS indicated AUCs of 0.952 and 0.841, respectively, for classifying tissue as malignant. Increased LASP1 correlated with poor overall survival (HR 3.66 per unit increase; CI 1.37-9.78; p = 0.01).

CONCLUSION

These results reveal distinct proteomic changes associated with early stage lung adenocarcinoma that may be useful prognostic indicators and therapeutic targets.

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.

Identification of IgM as a contaminant in lectin-FLISA assays for HCC detection

Liver disease, in the form of hepatocellular carcinoma (HCC) accounts for > 700,000 deaths worldwide. A major reason for this is late diagnosis of HCC. The currently used biomarker, serum alpha-fetoprotein (AFP) is elevated in 40-60% of those with HCC and other markers that can either compliment or replace AFP are desired. Our previous work has identified a number of proteins that contain altered glycans in HCC. Specifically, these altered glycans were increased levels of core and outer arm fucosylation. To determine the clinical usefulness of those identified glycoproteins, a plate based assay was developed that allowed for the detection of fucosylated glycoforms. While this method was applicable to a number of independent patient sets, it was unable to specifically detect fucosylated glycoforms in many patient samples. That is, some material was present in serum that led to non-specific signal in the lectin- fluorescence -linked immunosorbent assay (lectin-FLISA). To address this issue, a systematic process was undertaken to identify the material. This material was found to be increased levels of lectin reactive IgM. Removal of both IgG and IgM using a multi-step protein A/G incubation and filtration step removed the contaminating signal and allowed for the analysis of specific protein glycoforms. This assay was subsequently used on two sample sets, one that was shown previously to be unable to be tested via a lectin FLISA and in a larger independent sample set. The clinical usefulness of this assay in the early detection of HCC is discussed.

Urinary Metabolite Risk Biomarkers of Lung Cancer: A Prospective Cohort Study

BACKGROUND

Lung cancer is a major health burden causing 160,000 and 1.6 million deaths annually in the United States and worldwide, respectively.

METHODS

While seeking to identify stable and reproducible biomarkers in noninvasively collected biofluids, we assessed whether previously identified metabolite urinary lung cancer biomarkers, creatine riboside (CR), N-acetylneuraminic acid (NANA), cortisol sulfate, and indeterminate metabolite 561+, were elevated in the urines of subjects prior to lung cancer diagnosis in a well-characterized prospective Southern Community Cohort Study (SCCS). Urine was examined from 178 patients and 351 nondiseased controls, confirming that one of four metabolites was associated with lung cancer risk in the overall case-control set, whereas two metabolites were associated with lung cancer risk in European-Americans.

RESULTS

OR of lung cancer associated with elevated CR levels, and adjusted for smoking and other potential confounders, was 2.0 [95% confidence interval (CI), 1.2-3.4; P= 0.01]. In European-Americans, both CR and NANA were significantly associated with lung cancer risk (OR = 5.3; 95% CI, 1.6-17.6; P= 0.006 and OR=3.5; 95% CI, 1.5-8.4; P= 0.004, respectively). However, race itself did not significantly modify the associations. ROC analysis showed that adding CR and NANA to a model containing previously established lung cancer risk factors led to a significantly improved classifier (P= 0.01). Increasing urinary levels of CR and NANA displayed a positive association with increasing tumor size, strengthening a previously established link to altered tumor metabolism.

CONCLUSION AND IMPACT

These replicated results provide evidence that identified urinary metabolite biomarkers have a potential utility as noninvasive, clinical screening tools for early diagnosis of lung cancer. Cancer Epidemiol Biomarkers Prev; 25(6); 978-86. ©2016 AACR.

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.

Genomic, Proteomic, and Metabolomic Data Integration Strategies

Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these challenges. This review focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data using a variety of approaches including biochemical pathway-, ontology-, network-, and empirical-correlation-based methods.

Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer

BACKGROUND

Untargeted metabolomics was used in case-control studies of adenocarcinoma (ADC) lung cancer to develop and test metabolite classifiers in serum and plasma as potential biomarkers for diagnosing lung cancer.

METHODS

Serum and plasma were collected and used in two independent case-control studies (ADC1 and ADC2). Controls were frequency matched for gender, age, and smoking history. There were 52 adenocarcinoma cases and 31 controls in ADC1 and 43 adenocarcinoma cases and 43 controls in ADC2. Metabolomics was conducted using gas chromatography time-of-flight mass spectrometry. Differential analysis was performed on ADC1 and the top candidates (FDR < 0.05) for serum and plasma used to develop individual and multiplex classifiers that were then tested on an independent set of serum and plasma samples (ADC2).

RESULTS

Aspartate provided the best accuracy (81.4%) for an individual metabolite classifier in serum, whereas pyrophosphate had the best accuracy (77.9%) in plasma when independently tested. Multiplex classifiers of either 2 or 4 serum metabolites had an accuracy of 72.7% when independently tested. For plasma, a multimetabolite classifier consisting of 8 metabolites gave an accuracy of 77.3% when independently tested. Comparison of overall diagnostic performance between the two blood matrices yielded similar performances. However, serum is most ideal given higher sensitivity for low-abundant metabolites.

CONCLUSION

This study shows the potential of metabolite-based diagnostic tests for detection of lung adenocarcinoma. Further validation in a larger pool of samples is warranted.

IMPACT

These biomarkers could improve early detection and diagnosis of lung cancer.

MetaMapR: pathway independent metabolomic network analysis incorporating unknowns

Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/.

CONTACT

ofiehn@ucdavis.edu.