Detection of Lung Cancer through Metabolic Changes Measured in Blood Plasma

Impact of the redox-active MnTnHex-2-PyP5+ and cisplatin on the metabolome of non-small cell lung cancer cells

Redox-based cancer therapeutic strategies aim to raise reactive oxygen species (ROS) levels in cancer cells, thus modifying their redox status, and eventually inducing cell death. Promising compounds, known as superoxide dismutase mimics (SODm), e.g. MnTnHex-2-Py5+ (MnTnHex), could increase intracellular H2O2 in cancer cells with deficient ROS removal systems and therefore enhance radio- and chemotherapy efficacy. We have previously shown that MnTnHex was cytotoxic either alone or combined with cisplatin to non-small cell lung cancer (NSCLC) cells. To gain a deeper understanding of the effects and safety of this compound, it is crucial to analyze the metabolic alterations that take place within the cell. Our goal was thus to study the intracellular metabolome (intracellular metabolites) of NSCLC cells (A549 and H1975) using nuclear magnetic resonance (NMR) spectroscopy-based metabolomics to evaluate the changes in cellular metabolism upon exposure to MnTnHex per se or in combination with cisplatin. 1H NMR metabolomics revealed a higher number of significantly altered metabolites in A549 cells exposed to MnTnHex alone or combined with cisplatin in comparison with non-treated cells (nine dysregulated metabolites), suggesting an impact on aminoacyl-tRNA biosynthesis, glycolysis/gluconeogenesis, taurine, hypotaurine, glycerophospholipid, pyruvate, arginine and proline metabolisms. Regarding H1975 cells, significant alterations in the levels of six metabolites were observed upon co-treatment with MnTnHex and cisplatin, suggesting dysregulations in aminoacyl-tRNA biosynthesis, arginine and proline metabolism, pyruvate metabolism, and glycolysis/gluconeogenesis. These findings help us to understand the impact of MnTnHex on NSCLC cells. Importantly, specific altered metabolites, such as taurine, may contribute to the chemosensitizing effects of MnTnHex.

Gut microbial metabolites in lung cancer development and immunotherapy: Novel insights into gut-lung axis

Metabolic derivatives of numerous microorganisms inhabiting the human gut can participate in regulating physiological activities and immune status of the lungs through the gut-lung axis. The current well-established microbial metabolites include short-chain fatty acids (SCFAs), tryptophan and its derivatives, polyamines (PAs), secondary bile acids (SBAs), etc. As the study continues to deepen, the critical function of microbial metabolites in the occurrence and treatment of lung cancer has gradually been revealed. Microbial derivates can enter the circulation system to modulate the immune microenvironment of lung cancer. Mechanistically, oncometabolites damage host DNA and promote the occurrence of lung cancer, while tumor-suppresive metabolites directly affect the immune system to combat the malignant properties of cancer cells and even show considerable application potential in improving the efficacy of lung cancer immunotherapy. Considering the crosstalk along the gut-lung axis, in-depth exploration of microbial metabolites in patients' feces or serum will provide novel guidance for lung cancer diagnosis and treatment selection strategies. In addition, targeted therapeutics on microbial metabolites are expected to overcome the bottleneck of lung cancer immunotherapy and alleviate adverse reactions, including fecal microbiota transplantation, microecological preparations, metabolite synthesis and drugs targeting metabolic pathways. In summary, this review provides novel insights and explanations on the intricate interplay between gut microbial metabolites and lung cancer development, and immunotherapy through the lens of the gut-lung axis, which further confirms the possible translational potential of the microbiome metabolome in lung cancer treatment.

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.

Alterations in Plasma Lipid Profile before and after Surgical Removal of Soft Tissue Sarcoma

Soft tissue sarcoma (STS) is a relatively rare malignancy, accounting for about 1% of all adult cancers. It is known to have more than 70 subtypes. Its rarity, coupled with its various subtypes, makes early diagnosis challenging. The current standard treatment for STS is surgical removal. To identify the prognosis and pathophysiology of STS, we conducted untargeted metabolic profiling on pre-operative and post-operative plasma samples from 24 STS patients who underwent surgical tumor removal. Profiling was conducted using ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry. Thirty-nine putative metabolites, including phospholipids and acyl-carnitines were identified, indicating changes in lipid metabolism. Phospholipids exhibited an increase in the post-operative samples, while acyl-carnitines showed a decrease. Notably, the levels of pre-operative lysophosphatidylcholine (LPC) O-18:0 and LPC O-16:2 were significantly lower in patients who experienced recurrence after surgery compared to those who did not. Metabolic profiling may identify aggressive tumors that are susceptible to lipid synthase inhibitors. We believe that these findings could contribute to the elucidation of the pathophysiology of STS and the development of further metabolic studies in this rare malignancy.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Tuberculosis and lung cancer: metabolic pathways play a key role

Despite the fact that some cases of tuberculosis (TB) are undiagnosed and untreated, it remains a serious global public health issue. In the diagnosis, treatment, and control of latent and active TB, there may be a lack of effectiveness. An understanding of metabolic pathways can be fundamental to treat latent TB infection and active TB disease. Rather than targeting Mycobacterium tuberculosis, the control strategies aim to strengthen host responses to infection and reduce chronic inflammation by effectively enhancing host resistance to infection. The pathogenesis and progression of TB are linked to several metabolites and metabolic pathways, and they are potential targets for host-directed therapies. Additionally, metabolic pathways can contribute to the progression of lung cancer in patients with latent or active TB. A comprehensive metabolic pathway analysis is conducted to highlight lung cancer development in latent and active TB. The current study aimed to emphasize the association between metabolic pathways of tumor development in patients with latent and active TB. Health control programs around the world are compromised by TB and lung cancer due to their special epidemiological and clinical characteristics. Therefore, presenting the importance of lung cancer progression through metabolic pathways occurring upon TB infection can open new doors to improving control of TB infection and active TB disease while stressing that further evaluations are required to uncover this correlation.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

BACKGROUND

Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.

METHODS

A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).

RESULTS

Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.

CONCLUSIONS

The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Metabolic biomarkers in lung cancer screening and early diagnosis (Review)

Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer-associated mortality worldwide. At present, low-dose CT (LDCT) screening in the high-risk population, in which lung cancer incidence is higher than that of the low-risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false-positive rates, resulting in excessive subsequent follow-up procedures and radiation exposure. Complementation of LDCT examination with biofluid-based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low-risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.

Activity-Based Self-Enriched SERS Sensor for Blood Metabolite Monitoring

The monitoring of metabolites in biofluids provides critical clues for disease diagnosis and evaluation. Yet, the quantitative detection of metabolites remains challenging for surface-enhanced Raman spectroscopy (SERS) due to poor reproducibility in preparation and manipulation of SERS nanoprobes. Herein, we develop an activity-based, slippery liquid-infused porous surface SERS (abSLIPSERS) sensor for facile quantification of metabolites with unmodified naked metal nanoparticles (NPs) by integrating biocatalysis-boronate oxidation cascades with SLIPS-driven self-concentration and delivering. Upon mixing the target metabolite with a specific oxidase, a H₂O₂-sensitive phenylboronate probe, and the naked Au NPs, H₂O₂ produced from the biocatalytic reaction oxidizes the phenylboronate probe to phenol, resulting in a ratiometric SERS response. Meanwhile, the SLIPS enables the complete enrichment of molecules and NPs within an evaporating liquid droplet, delivering the probes to the SERS-active sites for Raman amplification. Compared with conventional SERS biosensors, abSLIPSERS avoids multistep synthesis and biofunctionalization of nanoprobes, which significantly simplifies the detection workflow and improves the reproducibility. The abSLIPSERS sensor also shows tunable dynamic range beyond 4 orders of magnitude and allows quantifying any other metabolites with specific enzymes. We demonstrate abSLIPSERS sensing of lactate, glucose, and choline in human serum for exploring energy metabolism in lung cancer. This study opens up a new opportunity for future point-of-care testing of circulating metabolites by SERS and will help to facilitate the translation of SERS bioanalysis to clinical settings.

The Importance of the Immune System and Molecular Cell Signaling Pathways in the Pathogenesis and Progression of Lung Cancer

Lung cancer is a disease that in recent years has become one of the greatest threats to modern society. Every year there are more and more new cases and the percentage of deaths caused by this type of cancer increases. Despite many studies, scientists are still looking for answers regarding the mechanisms of lung cancer development and progression, with particular emphasis on the role of the immune system. The aim of this literature review was to present the importance of disorders of the immune system and the accompanying changes at the level of cell signaling in the pathogenesis of lung cancer. The collected results showed that in the process of immunopathogenesis of almost all subtypes of lung cancer, changes in the tumor microenvironment, deregulation of immune checkpoints and abnormalities in cell signaling pathways are involved, which contribute to the multistage and multifaceted carcinogenesis of this type of cancer. We, therefore, suggest that in future studies, researchers should focus on a detailed analysis of tumor microenvironmental immune checkpoints, and to validate their validity, perform genetic polymorphism analyses in a wide range of patients and healthy individuals to determine the genetic susceptibility to lung cancer development. In addition, further research related to the analysis of the tumor microenvironment; immune system disorders, with a particular emphasis on immunological checkpoints and genetic differences may contribute to the development of new personalized therapies that improve the prognosis of patients.

1H NMR-based metabolomics of paired tissue, serum and urine samples reveals an optimized panel of biofluids metabolic biomarkers for esophageal cancer

INTRODUCTION

The goal of this study was to establish an optimized metabolic panel by combining serum and urine biomarkers that could reflect the malignancy of cancer tissues to improve the non-invasive diagnosis of esophageal squamous cell cancer (ESCC).

METHODS

Urine and serum specimens representing the healthy and ESCC individuals, together with the paralleled ESCC cancer tissues and corresponding distant non-cancerous tissues were investigated in this study using the high-resolution 600 MHz 1H-NMR technique.

RESULTS

We identified distinct 1H NMR-based serum and urine metabolic signatures respectively, which were linked to the metabolic profiles of esophageal-cancerous tissues. Creatine and glycine in both serum and urine were selected as the optimal biofluids biomarker panel for ESCC detection, as they were the overlapping discriminative metabolites across serum, urine and cancer tissues in ESCC patients. Also, the were the major metabolites involved in the perturbation of "glycine, serine, and threonine metabolism", the significant pathway alteration associated with ESCC progression. Then a visual predictive nomogram was constructed by combining creatine and glycine in both serum and urine, which exhibited superior diagnostic efficiency (with an AUC of 0.930) than any diagnostic model constructed by a single urine or serum metabolic biomarkers.

DISCUSSION

Overall, this study highlighted that NMR-based biofluids metabolomics fingerprinting, as a non-invasive predictor, has the potential utility for ESCC detection. Further studies based on a lager number size and in combination with other omics or molecular biological approaches are needed to validate the metabolic pathway disturbances in ESCC patients.

Data-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls

BACKGROUND

Metabolomics is a potential means for biofluid-based lung cancer detection. We conducted a non-targeted, data-driven assessment of plasma from early-stage non-small cell lung cancer (ES-NSCLC) cases versus cancer-free controls (CFC) to explore and identify the classes of metabolites for further targeted metabolomics biomarker development.

METHODS

Plasma from 250 ES-NSCLC cases and 250 CFCs underwent ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive and negative electrospray ionization (ESI) modes. Molecular feature extraction, formula generation, and find-by-ion tools annotated metabolic entities. Analysis was restricted to endogenous metabolites present in ≥ 80% of samples. Unsupervised hierarchical cluster analysis identified clusters of metabolites. The metabolites with the strongest correlation with the principal component of each cluster were included in logistic regression modeling to assess discriminatory performance with and without adjustment for clinical covariates.

RESULTS

A total of 1900 UHPLC-QTOF-MS assessments identified 1667 and 2032 endogenous metabolites in the ESI-positive and ESI-negative modes, respectively. After data filtration, 676 metabolites remained, and 12 clusters of metabolites were identified from each ESI mode. Multivariable logistic regression using the representative metabolite from each cluster revealed effective classification of cases from controls with overall diagnostic accuracy of 91% (ESI positive) and 94% (ESI negative). Metabolites of interest identified for further targeted analysis include the following: 1b, 3a, 12a-trihydroxy-5b-cholanoic acid, pyridoxamine 5'-phosphate, sphinganine 1-phosphate, gamma-CEHC, 20-carboxy-leukotriene B4, isodesmosine, and 18-hydroxycortisol.

CONCLUSIONS

Plasma-based metabolomic detection of early-stage NSCLC appears feasible. Further metabolomics studies targeting phospholipid, steroid, and fatty acid metabolism are warranted to further develop noninvasive metabolomics-based detection of early-stage NSCLC.

Rapid diagnosis of malignant pleural mesothelioma and its discrimination from lung cancer and benign exudative effusions using blood serum

Malignant pleural mesothelioma (MPM), an aggressive cancer associated with exposure to fibrous minerals, can only be diagnosed in the advanced stage because its early symptoms are also connected with other respiratory diseases. Hence, understanding the molecular mechanism and the discrimination of MPM from other lung diseases at an early stage is important to apply effective treatment strategies and for the increase in survival rate. This study aims to develop a new approach for characterization and diagnosis of MPM among lung diseases from serum by Fourier transform infrared spectroscopy (FTIR) coupled with multivariate analysis. The detailed spectral characterization studies indicated the changes in lipid biosynthesis and nucleic acids levels in the malignant serum samples. Furthermore, the results showed that healthy, benign exudative effusion, lung cancer, and MPM groups were successfully separated from each other by applying principal component analysis (PCA), support vector machine (SVM), and especially linear discriminant analysis (LDA) to infrared spectra.

Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review

Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3–62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (¹⁸F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.

Unraveling the Rewired Metabolism in Lung Cancer Using Quantitative NMR Metabolomics

Lung cancer cells are well documented to rewire their metabolism and energy production networks to enable proliferation and survival in a nutrient-poor and hypoxic environment. Although metabolite profiling of blood plasma and tissue is still emerging in omics approaches, several techniques have shown potential in cancer diagnosis. In this paper, the authors describe the alterations in the metabolic phenotype of lung cancer patients. In addition, we focus on the metabolic cooperation between tumor cells and healthy tissue. Furthermore, the authors discuss how metabolomics could improve the management of lung cancer patients.

Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach

Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.

Lung Adenocarcinoma Transcriptomic Analysis Predicts Adenylate Kinase Signatures Contributing to Tumor Progression and Negative Patient Prognosis

The ability to detect and respond to hypoxia within a developing tumor appears to be a common feature amongst most cancers. This hypoxic response has many molecular drivers, but none as widely studied as Hypoxia-Inducible Factor 1 (HIF-1). Recent evidence suggests that HIF-1 biology within lung adenocarcinoma (LUAD) may be associated with expression levels of adenylate kinases (AKs). Using LUAD patient transcriptome data, we sought to characterize AK gene signatures related to lung cancer hallmarks, such as hypoxia and metabolic reprogramming, to identify conserved biological themes across LUAD tumor progression. Transcriptomic analysis revealed perturbation of HIF-1 targets to correlate with altered expression of most AKs, with AK4 having the strongest correlation. Enrichment analysis of LUAD tumor AK4 gene signatures predicts signatures involved in pyrimidine, and by extension, nucleotide metabolism across all LUAD tumor stages. To further discriminate potential drivers of LUAD tumor progression within AK4 gene signatures, partial least squares discriminant analysis was used at LUAD stage-stage interfaces, identifying candidate genes that may promote LUAD tumor growth or regression. Collectively, these results characterize regulatory gene networks associated with the expression of all nine human AKs that may contribute to underlying metabolic perturbations within LUAD and reveal potential mechanistic insight into the complementary role of AK4 in LUAD tumor development.

Identifying metabolic alterations in newly diagnosed small cell lung cancer patients

BACKGROUND

Small cell lung cancer (SCLC) is a malignant disease with poor prognosis. At the time of diagnosis most patients are already in a metastatic stage. Current diagnosis is based on imaging, histopathology, and immunohistochemistry, but no blood-based biomarkers have yet proven to be clinically successful for diagnosis and screening. The precise mechanisms of SCLC are not fully understood, however, several genetic mutations, protein and metabolic aberrations have been described. We aim at identifying metabolite alterations related to SCLC and to expand our knowledge relating to this aggressive cancer.

METHODS

A total of 30 serum samples of patients with SCLC, collected at the time of diagnosis, and 25 samples of healthy controls were included in this study. The samples were analyzed with nuclear magnetic resonance spectroscopy. Multivariate, univariate and pathways analyses were performed.

RESULTS

Several metabolites were identified to be altered in the pre-treatment serum samples of small-cell lung cancer patients compared to healthy individuals. Metabolites involved in tricarboxylic acid cycle (succinate: fold change (FC) = 2.4, p = 0.068), lipid metabolism (LDL triglyceride: FC = 1.3, p = 0.001; LDL-1 triglyceride: FC = 1.3, p = 0.012; LDL-2 triglyceride: FC = 1.4, p = 0.009; LDL-6 triglyceride: FC = 1.5, p < 0.001; LDL-4 cholesterol: FC = 0.5, p = 0.007; HDL-3 free cholesterol: FC = 0.7, p = 0.002; HDL-4 cholesterol FC = 0.8, p < 0.001; HDL-4 apolipoprotein-A1: FC = 0.8, p = 0.005; HDL-4 apolipoprotein-A2: FC ≥ 0.7, p ≤ 0.001), amino acids (glutamic acid: FC = 1.7, p < 0.001; glutamine: FC = 0.9, p = 0.007, leucine: FC = 0.8, p < 0.001; isoleucine: FC = 0.8, p = 0.016; valine: FC = 0.9, p = 0.032; lysine: FC = 0.8, p = 0.004; methionine: FC = 0.8, p < 0.001; tyrosine: FC = 0.7, p = 0.002; creatine: FC = 0.9, p = 0.030), and ketone body metabolism (3-hydroxybutyric acid FC = 2.5, p < 0.001; acetone FC = 1.6, p < 0.001), among other, were found deranged in SCLC.

CONCLUSIONS

This study provides novel insight into the metabolic disturbances in pre-treatment SCLC patients, expanding our molecular understanding of this malignant disease.

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel ¹H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the ¹H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

The Lipid Composition of Serum-Derived Small Extracellular Vesicles in Participants of a Lung Cancer Screening Study

Simple Summary

Molecular components of extracellular vesicles present in serum are potential biomarkers of lung cancer, however, none of them have been validated in the context of an actual early detection of lung cancer. Here, we compared the lipid profiles of vesicles obtained from participants in a lung cancer screening study, including patients with screening-detected cancer and individuals with benign pulmonary nodules or without pathological changes. A few lipids whose levels were different between compared groups were detected, including ceramide Cer(42:1) upregulated in vesicles from cancer patients. Furthermore, a high heterogeneity of lipid profiles of extracellular vesicles was observed, which impaired the performance of classification models based on specific compounds.

Abstract

Molecular components of exosomes and other classes of small extracellular vesicles (sEV) present in human biofluids are potential biomarkers with possible applicability in the early detection of lung cancer. Here, we compared the lipid profiles of serum-derived sEV from three groups of lung cancer screening participants: individuals without pulmonary alterations, individuals with benign lung nodules, and patients with screening-detected lung cancer (81 individuals in each group). Extracellular vesicles and particles were purified from serum by size-exclusion chromatography, and a fraction enriched in sEV and depleted of low-density lipoproteins (LDLs) was selected (similar sized vesicles was observed in all groups: 70–100 nm). The targeted mass-spectrometry-based approach enabled the detection of 352 lipids, including 201 compounds used in quantitative analyses. A few compounds, exemplified by Cer(42:1), i.e., a ceramide whose increased plasma/serum level was reported in different pathological conditions, were upregulated in vesicles from cancer patients. On the other hand, the contribution of phosphatidylcholines with poly-unsaturated acyl chains was reduced in vesicles from lung cancer patients. Cancer-related features detected in serum-derived sEV were different than those of the corresponding whole serum. A high heterogeneity of lipid profiles of sEV was observed, which markedly impaired the performance of classification models based on specific compounds (the three-state classifiers showed an average AUC = 0.65 and 0.58 in the training and test subsets, respectively).

1 H NMR-based metabolomics of paired esophageal tumor tissues and serum samples identifies specific serum biomarkers for esophageal cancer

Serum metabolites of healthy controls and esophageal cancer (EC) patients have previously been compared to predict cancer-specific profiles. However, the association between metabolic alterations in serum samples and esophageal tissues in EC patients remains unclear. Here, we analyzed 50 pairs of EC tissues and distant noncancerous tissues, together with patient-matched serum samples, using ¹ H NMR spectroscopy and pattern recognition algorithms. EC patients could be differentiated from the controls based on the metabolic profiles at tissue and serum levels. Some overlapping discriminatory metabolites, including valine, alanine, glucose, acetate, citrate, succinate and glutamate, were identified in both matrices. These results suggested deregulation of metabolic pathways, and potentially revealed the links between EC and several metabolic pathways, such as the tricarboxylic acid cycle, glutaminolysis, short-chain fatty acid metabolism, lipometabolism and pyruvate metabolism. Perturbation of the pyruvate metabolism was most strongly associated with EC progression. Consequently, an optimal serum metabolite biomarker panel comprising acetate and pyruvate was developed, as these two metabolites are involved in pyruvate metabolism, and changes in their serum levels were significantly correlated with alterations in the levels of some other esophageal tissue metabolites. In comparison with individual biomarkers, this panel exhibited better diagnostic efficiency for EC, with an AUC of 0.948 in the test set, and a good predictive ability of 82.5% in the validation set. Analysis of key genes related to pyruvate metabolism in EC patients revealed patterns corresponding to the changes in serum pyruvate and acetate levels. These correlation analyses demonstrate that there were distinct metabolic characteristics and pathway aberrations in the esophageal tumor tissue and in the serum. Changes in the serum metabolic signatures could reflect the alterations in the esophageal tumor profile, thereby emphasizing the importance of distinct serum metabolic profiles as potential noninvasive biomarkers for EC.

Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts

Serum metabolome is a promising source of molecular biomarkers that could support early detection of lung cancer in screening programs based on low-dose computed tomography. Several panels of metabolites that differentiate lung cancer patients and healthy individuals were reported, yet none of them were validated in the population at high-risk of developing cancer. Here we analyzed serum metabolome profiles in participants of two lung cancer screening studies: MOLTEST-BIS (Poland, n = 369) and SMAC-1 (Italy, n = 93). Three groups of screening participants were included: lung cancer patients, individuals with benign pulmonary nodules, and those without any lung alterations. Concentrations of about 400 metabolites (lipids, amino acids, and biogenic amines) were measured by a mass spectrometry-based approach. We observed a reduced level of lipids, in particular cholesteryl esters, in sera of cancer patients from both studies. Despite several specific compounds showing significant differences between cancer patients and healthy controls within each study, only a few cancer-related features were common when both cohorts were compared, which included a reduced concentration of lysophosphatidylcholine LPC (18:0). Moreover, serum metabolome profiles in both noncancer groups were similar, and differences between cancer patients and both groups of healthy participants were comparable. Large heterogeneity in levels of specific metabolites was observed, both within and between cohorts, which markedly impaired the accuracy of classification models: The overall AUC values of three-state classifiers were 0.60 and 0.51 for the test (MOLTEST) and validation (SMAC) cohorts, respectively. Therefore, a hypothetical metabolite-based biomarker for early detection of lung cancer would require adjustment to lifestyle-related confounding factors that putatively affect the composition of serum metabolome.

Metabolomic profiling for second primary lung cancer: A pilot case-control study

OBJECTIVES

Lung cancer survivors have a high risk of developing a second primary lung cancer (SPLC). While national screening guidelines have been established for initial primary lung cancer (IPLC), no consensus guidelines exist for SPLC. Furthermore, the factors that contribute to SPLC risk have not been established. This study examines the potential for using serum metabolomics to identify metabolite biomarkers that differ between SPLC cases and IPLC controls.

MATERIAL AND METHODS

In this pilot case-control study, we applied an untargeted metabolomics approach based on ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) to serum samples of 82 SPLC cases and 82 frequency matched IPLC controls enrolled in the Boston Lung Cancer Study. Random forest and unconditional logistic regression models identified metabolites associated with SPLC. Candidate metabolites were integrated into a SPLC risk prediction model and the model performance was evaluated through a risk stratification approach.

RESULTS

The untargeted analysis detected 1008 named and 316 unnamed metabolites among all study participants. Metabolites that were significantly associated with SPLC (False Discovery Rate q-value < 0.2) included 5-methylthioadenosine (odds ratio [OR] = 2.04, 95 % confidence interval [CI] 1.39-3.01; P = 2.8 × 10^-4) and phenylacetylglutamine (OR = 2.65, 95 % CI 1.56-4.51; P = 3.2 × 10^-4), each exhibiting approximately 1.5-fold increased levels among SPLC cases versus IPLC controls. In stratifying the study participants across quartiles of estimated SPLC risk, the risk prediction model identified a significantly higher proportion of SPLC cases in the fourth compared to the first quartile (68.3 % versus 39.0 %; P = 0.044).

CONCLUSION

SPLC cases may have distinct metabolomic profiles compared to those in IPLC patients without SPLC. A risk stratification approach integrating metabolomics may be useful for distinguishing patients based on SPLC risk. Prospective validation studies are needed to further evaluate the potential for leveraging metabolomics in SPLC surveillance and screening.

Roles of Lipid Profiles in Human Non-Small Cell Lung Cancer

Aims: This review aims to identify lipid biomarkers of non-small cell lung cancer (NSCLC) in human tissue samples and discuss the roles of lipids in tissue molecular identification, the discovery of potential biomarkers, and surgical margin assessment. Methods: A review of the literature focused on lipid-related research using mass spectrometry (MS) techniques in human NSCLC tissues from January 1, 2015, to November 20, 2020, was conducted. The quality of included studies was assessed using the QUADAS-2 tool. Results: Twelve studies met the inclusion criteria and were included in the review. The risk of bias was unclear in the majority of the studies. The contents of lipids including fatty acids, phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl inositol, cardiolipin, phosphatidyl serine, phosphatidyl glycerol, ceramide, lysophosphatidylethanolamine, lysophosphatidylcholine, and lysophosphatidylglycerol differed significantly between cancer and healthy tissues. The sensitivity or specificity of the discrimination model was reported in 8 studies, and the sensitivity and specificity varied among the reported methods. The lipid profiles differed between adenocarcinoma and squamous cell carcinoma NSCLC subtypes. Conclusion: In preclinical studies, MS analysis and multiple discrimination models can be combined to distinguish NSCLC tissues from healthy tissues based on lipid profiles, which provides a new opportunity to evaluate the surgical margin and cancer subtype intraoperatively. Future studies should provide guidance for selecting patients and discrimination models to develop an improved method for clinical application.

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.

Analysis of differential metabolites in lung cancer patients based on metabolomics and bioinformatics

Aim: Based on metabonomics, the metabolic markers of lung cancer patients were analyzed, combined with bioinformatics to explore the underlying disease mechanism. Materials & methods: Based on case-control design, using UPLC-Q-TOF/MS, urine metabolites were detected in discovery and validation set. Multivariate statistical analysis were performed to identify potential markers for lung cancer. A network analysis was constructed to integrate lung cancer disease targets with the above metabolic markers, and its possible mechanism and biological significance were explained. Results: A total of 35 potential markers were identified, 11 of which overlapped. Five key markers have a good linear correlation with serum biochemical indicators. Conclusion: The occurrence and development of lung cancer are closely related to disturbance of D-Glutamine and D-glutamate metabolism, amino acid imbalance. This test was registered on China clinical trial registration center (www.chictr.org.cn/index.aspx), registration number was ChiCTR1900025543.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

Quantitative metabolic biomarker analysis of mild cognitive impairment in eastern U.P. and Bihar population

Mild cognitive impairment (MCI) is a transition phase between healthy individuals and Alzheimer's disease (AD). Therefore, diagnosis of MCI at early stage will help to delay or prevent its progression to disease. In the present study, we aim to identify the metabolic biomarkers, which can help in the diagnosis of MCI. We have screened 2000 elderly individuals from north India, out of which 200 were identified as MCI. We continued our study on 10 MCI individuals who regularly participated in the follow-up. The age and gender matched 10 healthy individuals were taken as control. These control and MCI individuals were subjected to neuropsychological examination such as Hindi mental state examination (HMSE) and Montreal cognitive assessment (MOCA) followed by ¹H Nuclear Magnetic Resonance (NMR) analysis. Remarkable changes were noted between control and MCI individuals at metabolic level. In silico study showed the involvement of eight metabolites in MCI. We found higher level of lactate, N-acetyl aspartate, histidine and lower level of formate, choline, alanine, creatinine and glucose in blood plasma of MCI individuals compared to control. Further, In silico study showed that choline might be directly associated with MCI or AD. Such In silico study with quantitative metabolite analysis of plasma could be used as diagnostic biomarkers for the identification of MCI.

Non-invasive exploration of metabolic profile of lung cancer with Magnetic Resonance Spectroscopy and Mass Spectrometry

BACKGROUND

Lung cancer is a major cause of global morbidity and mortality. Current low dose CT screening is invasive and its role remains contentious. There are no known biomarkers to monitor treatment response, detect disease recurrence and patient selection for adjuvant treatment after curative surgical resection. Hence there is an urgent need to explore non-conventional and non-invasive tools to develop novel biomarkers to improve the outcome of this lethal cancer.

METHODS

This is an ongoing exploratory and translational study involving collection of bio fluids from 50 patients with early stage non-small cell lung cancer before and after surgical resection. The primary objective is to identify cancer specific metabolome in body fluids - sputum, exhaled breath condensate, blood and urine of the patients with early stage non-small cell lung cancer using Magnetic Resonance Spectroscopy and Mass Spectroscopy.

CONCLUSION

The trajectory of change in metabolic profile of body fluids before and after surgical resection may have potential clinical applications in lung cancer screening, as biomarkers for disease recurrence and exploration of novel targets for therapeutic intervention.

Application of NMR metabolomics to search for human disease biomarkers in blood

Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.

The Metabolic Landscape of Lung Cancer: New Insights in a Disturbed Glucose Metabolism

Metabolism encompasses the biochemical processes that allow healthy cells to keep energy, redox balance and building blocks required for cell development, survival, and proliferation steady. Malignant cells are well-documented to reprogram their metabolism and energy production networks to support rapid proliferation and survival in harsh conditions via mutations in oncogenes and inactivation of tumor suppressor genes. Despite the histologic and genetic heterogeneity of tumors, a common set of metabolic pathways sustain the high proliferation rates observed in cancer cells. This review with a focus on lung cancer covers several fundamental principles of the disturbed glucose metabolism, such as the “Warburg” effect, the importance of the glycolysis and its branching pathways, the unanticipated gluconeogenesis and mitochondrial metabolism. Furthermore, we highlight our current understanding of the disturbed glucose metabolism and how this might result in the development of new treatments.

Untargeted Urinary 1H NMR-Based Metabolomic Pattern as a Potential Platform in Breast Cancer Detection

Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC patients from healthy controls (variable importance in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification of some metabolites with the highest sensitivities and specificities to discriminate BC patients from healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic pathway analysis indicated several metabolism pathway disruptions, including amino acid and carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained results support the high throughput potential of NMR-based urinary metabolomics patterns in discriminating BC patients from CTL. Further investigations could unravel novel mechanistic insights into disease pathophysiology, monitor disease recurrence, and predict patient response towards therapy.

Correlations between the metabolic profile and 18F-FDG-Positron Emission Tomography-Computed Tomography parameters reveal the complexity of the metabolic reprogramming within lung cancer patients

Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by ¹⁸F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between ¹H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose (¹⁸F-FDG) in lung cancer tissue. PET parameters of interest are standard maximal uptake values (SUV_max), total body metabolic active tumor volumes (MATV_WTB) and total body total lesion glycolysis (TLG_WTB) values. Patients with high values of these parameters have higher plasma concentrations of N-acetylated glycoproteins which suggest an upregulation of the hexosamines biosynthesis. High MATV_WTB and TLG_WTB values are associated with higher concentrations of glucose, glycerol, N-acetylated glycoproteins, threonine, aspartate and valine and lower levels of sphingomyelins and phosphatidylcholines appearing at the surface of lipoproteins. These higher concentrations of glucose and non-carbohydrate glucose precursors such as amino acids and glycerol suggests involvement of the gluconeogenesis pathway. The lower plasma concentration of those phospholipids points to a higher need for membrane synthesis. Our results indicate that the metabolic reprogramming in cancer is more complex than the initially described Warburg effect.

Raising to the Challenge: Building a Federated Biobank to Accelerate Translational Research-The University Biobank Limburg

Irreproducibility of research results is one of the major contributing factors to the failure of translating basic research results into tangible bedside progress. To address this, the University Biobank Limburg (UBiLim) was founded by a collaboration between Hasselt University, the Hospital East-Limburg, and the Jessa Hospital. This paper describes the evolution of this process and the barriers encountered on the way. UBiLim evolved from an archival collection over a single-site biobank into a federated structure, supporting translational research at the founding institutions. Currently, UBiLim is a federated biobank, with an established organizational structure and processing, and storage facilities at each of the three sites. All activities are integrated in an ISO15189-accredited Quality Management System and based on (inter)national biobank guidelines. Common methods for processing and storage of a plethora of sample types, suitable for state-of-the-art applications, were validated and implemented. Because the biobank is embedded in two hospitals, the request of researchers to include certain sample types or enroll specific patient groups can quickly be met. Funding has been a major challenge in each step of its evolution and remains the biggest issue for long-term biobank sustainability. To a lesser extent, the Belgian legislation and the operational cost of information management system are also concerns for smooth biobank operations. Nonetheless, UBiLim serves as a facilitator and accelerator for translational research in the Limburg area of Belgium that, given the fields of research, may have an impact on international patient care.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

Metabolic profiles of serum samples from ground glass opacity represent potential diagnostic biomarkers for lung cancer

Background

Lung cancer is a leading cause of cancer deaths worldwide. Low-dose computed tomography (LDCT) screening trials indicated that LDCT is effective for the early detection of lung cancer, but the findings were accompanied by high false positive rates. Therefore, the detection of lung cancer needs complementary blood biomarker tests to reduce false positive rates.

Methods

In order to evaluate the potential of metabolite biomarkers for diagnosing lung cancer and increasing the effectiveness of clinical interventions, serum samples from subjects participating in a low-dose CT-scan screening were analyzed by using untargeted liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Samples were acquired from 34 lung patients with ground glass opacity diagnosed lung cancer and 39 healthy controls.

Results

In total, we identified 9 metabolites in electron spray ionization (ESI)(+) mode and 7 metabolites in ESI(-) mode. L-(+)-gulose, phosphatidylethanolamine (PE)(22:2(13Z,16Z)/15:0), cysteinyl-glutamine, S-japonin, threoninyl-glutamine, chlorate, 3-oxoadipic acid, dukunolide A, and malonic semialdehyde levels were observed to be elevated in serum samples of lung cancer cases when compared to those of healthy controls. By contrast, 1-(2-furanylmethyl)-1H-pyrrole, 2,4-dihydroxybenzoic acid, monoethyl carbonate, guanidinosuccinic acid, pseudouridine, DIMBOA-Glc, and 4-feruloyl-1,5-quinolactone levels were lower in serum samples of lung cancer cases compared with those of healthy controls.

Conclusions

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant ground glass opacity from benign ground glass opacity. Further studies in larger pools of samples are warranted.

The impact of the method of extracting metabolic signal from 1H-NMR data on the classification of samples: A case study of binning and BATMAN in lung cancer

Nuclear magnetic resonance (NMR) spectroscopy is a principal analytical technique in metabolomics. Extracting metabolic information from NMR spectra is complex due to the fact that an immense amount of detail on the chemical composition of a biological sample is expressed through a single spectrum. The simplest approach to quantify the signal is through spectral binning which involves subdividing the spectra into regions along the chemical shift axis and integrating the peaks within each region. However, due to overlapping resonance signals, the integration values do not always correspond to the concentrations of specific metabolites. An alternate, more advanced statistical approach is spectral deconvolution. BATMAN (Bayesian AuTomated Metabolite Analyser for NMR data) performs spectral deconvolution using prior information on the spectral signatures of metabolites. In this way, BATMAN estimates relative metabolic concentrations. In this study, both spectral binning and spectral deconvolution using BATMAN were applied to 400 MHz and 900 MHz NMR spectra of blood plasma samples from lung cancer patients and control subjects. The relative concentrations estimated by BATMAN were compared with the binning integration values in terms of their ability to discriminate between lung cancer patients and controls. For the 400 MHz data, the spectral binning approach provided greater discriminatory power. However, for the 900 MHz data, the relative metabolic concentrations obtained by using BATMAN provided greater predictive power. While spectral binning is computationally advantageous and less laborious, complementary models developed using BATMAN-estimated features can add complementary information regarding the biological interpretation of the data and therefore are clinically useful.

Glutamine Addiction and Therapeutic Strategies in Lung Cancer

Lung cancer cells are well-documented to rewire their metabolism and energy production networks to support rapid survival and proliferation. This metabolic reorganization has been recognized as a hallmark of cancer. The increased uptake of glucose and the increased activity of the glycolytic pathway have been extensively described. However, over the past years, increasing evidence has shown that lung cancer cells also require glutamine to fulfill their metabolic needs. As a nitrogen source, glutamine contributes directly (or indirectly upon conversion to glutamate) to many anabolic processes in cancer, such as the biosynthesis of amino acids, nucleobases, and hexosamines. It plays also an important role in the redox homeostasis, and last but not least, upon conversion to α-ketoglutarate, glutamine is an energy and anaplerotic carbon source that replenishes tricarboxylic acid cycle intermediates. The latter is generally indicated as glutaminolysis. In this review, we explore the role of glutamine metabolism in lung cancer. Because lung cancer is the leading cause of cancer death with limited curative treatment options, we focus on the potential therapeutic approaches targeting the glutamine metabolism in cancer.

The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer

Background

Pulmonary imaging often identifies suspicious abnormalities resulting in supplementary diagnostic procedures. This study aims to investigate whether the metabolic fingerprint of plasma allows to discriminate between patients with lung inflammation and patients with lung cancer.

Methods

Metabolic profiles of plasma from 347 controls, 269 cancer patients and 108 patients with inflammation were obtained by ¹H-NMR spectroscopy. Models to discriminate between groups were trained by PLS-LDA. A test set was used for independent validation. A ROC curve was built to evaluate the diagnostic performance of potential biomarkers.

Results

Sensitivity, specificity, PPV and NPV of PET-CT to diagnose cancer are 96, 23, 76 and 71%. Metabolic profiles differentiate between cancer and inflammation with a sensitivity of 89%, a specificity of 87% and a MCE of 12%. Removal of the glutamate metabolite results in an increase of MCE (38%) and a decrease of both sensitivity and specificity (62%), demonstrating the importance of glutamate for discrimination. At the cut-off point 0.31 on the ROC curve, the relative glutamate concentration discriminates between cancer and inflammation with a sensitivity of 85%, a specificity of 81%, and an AUC of 0.88. PPV and NPV are 92 and 69%. In PET-positive patients with a relative glutamate level ≤ 0.31 the sensitivity to diagnose cancer reaches 100% with a PPV of 94%. In PET-negative patients, a relative glutamate level > 0.31 increases the specificity of PET from 23% to 58% and results in a high NPV of 100%. In case of discrepancy between SUV_max and the glutamate concentration, lung cancer is missed in 19% of the cases.

Conclusion

This study indicates that the ¹H-NMR-derived relative plasma concentration of glutamate allows discrimination between lung cancer and lung inflammation. A glutamate level ≤ 0.31 in PET-positive patients corresponds to the diagnosis of lung cancer with a higher specificity and PPV than PET-CT. Glutamate levels > 0.31 in patients with PET negative lung lesions is likely to correspond with inflammation. Caution is needed for patients with conflicting SUV_max values and glutamate concentrations. Confirmation is needed in a prospective study with external validation and by another analytical technique such as HPLC-MS.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4755-1) contains supplementary material, which is available to authorized users.

Blood based biomarkers beyond genomics for lung cancer screening

While there is considerable interest at the present time in the development of so-called liquid biopsy approaches for cancer detection based notably on circulating tumor DNA, there are other types of potential biomarkers that show promise for lung cancer screening and early detection. Here we review approaches and some of the promising markers based on proteomics, metabolomics and the immune response to tumor antigens in the form of autoantibodies.

Identification of metabolic phenotypes in childhood obesity by 1H NMR metabolomics of blood plasma

Aim:

To identify the plasma metabolic profile associated with childhood obesity and its metabolic phenotypes.

Materials & methods:

The plasma metabolic profile of 65 obese and 37 normal-weight children was obtained using proton NMR spectroscopy. NMR spectra were rationally divided into 110 integration regions, which reflect relative metabolite concentrations, and were used as statistical variables.

Results:

Obese children show increased levels of lipids, N-acetyl glycoproteins, and lactate, and decreased levels of several amino acids, α-ketoglutarate, glucose, citrate, and cholinated phospholipids as compared with normal-weight children. Metabolically healthy children show lower levels of lipids and lactate, and higher levels of several amino acids and cholinated phospholipids, as compared with unhealthy children.

Conclusion:

This study reveals new valuable findings in the field of metabolomics and childhood obesity. Although validation should be performed, the proof of principle looks promising and justifies a deeper investigation of the diagnostic possibilities of proton NMR metabolomics in follow-up studies.

Trial registration: NCT03014856. Registered January 9, 2017.

The plasma metabolic profile of childhood obesity and its metabolic phenotypes was identified using untargeted proton NMR spectroscopy combined with multivariate statistics. Obese children show increased plasma levels of lipids, N-acetyl glycoproteins and lactate, next to decreased levels of several amino acids, α-ketoglutarate, glucose, citrate and cholinated phospholipids as compared with normal-weight children. In addition, the metabolic profile of healthy and unhealthy obese children could be discriminated and although further validation should be performed, these findings might pave the way to a detailed diagnostic metabolic signature in children.

Synergy between the KEAP1/NRF2 and PI3K Pathways Drives Non-Small-Cell Lung Cancer with an Altered Immune Microenvironment

The lung presents a highly oxidative environment, which is tolerated through engagement of tightly controlled stress response pathways. A critical stress response mediator is the transcription factor nuclear factor erythroid-2-related factor 2 (NFE2L2/NRF2), which is negatively regulated by Kelch-like ECH-associated protein 1 (KEAP1). Alterations in the KEAP1/NRF2 pathway have been identified in 23% of lung adenocarcinomas, suggesting that deregulation of the pathway is a major cancer driver. We demonstrate that inactivation of Keap1 and Pten in the mouse lung promotes adenocarcinoma formation. Notably, metabolites identified in the plasma of Keap1^f/f/Pten^f/f tumor-bearing mice indicate that tumorigenesis is associated with reprogramming of the pentose phosphate pathway. Furthermore, the immune milieu was dramatically changed by Keap1 and Pten deletion, and tumor regression was achieved utilizing immune checkpoint inhibition. Thus, our study highlights the ability to exploit both metabolic and immune characteristics in the detection and treatment of lung tumors harboring KEAP1/NRF2 pathway alterations.

Screening for early stage lung cancer and its correlation with lung nodule detection

Currently, the most effective way of reducing lung cancer mortality is early diagnosis of lung cancer. The National Lung Screening Trial has proved the efficacy of lung cancer screening using low-dose computed tomography to reduce lung cancer mortality. However, many questions remain surrounding lung cancer screening implementation, among which include how to select the optimal risk population, the personalized screening interval based different levels of risk, methods to improve diagnostic discrimination between malignant and benign disease in detected lung nodules, and the roles of biomolecular markers in stratifying risk and in guiding the management of indeterminate nodules. This review concentrates on the latest developments of lung cancer screening and provides an overview of the main unanswered questions on lung nodule detection.

Models including plasma levels of sphingomyelins and phosphatidylcholines as diagnostic and prognostic biomarkers of endometrial cancer

In endometrial cancer, biomarkers for preoperative identification of patients with low risk for disease progression would enable stratification according to the extent of surgery needed, and would avoid the complications that can be associated with radical surgery. A panel of proteins, amino acids, enzymes, and miRNA has been investigated as potential biomarkers for endometrial cancer. At the time of the manuscript submission targeted metabolomics/lipidomics approaches have not been applied to biomarker research in endometrial cancer. Using electrospray ionization-tandem mass spectrometry we quantified 163 metabolites in 126 plasma samples (61 patients with endometrial cancer, 65 control patients). Three single phosphatidylcholines were identified with significantly decreased levels in patients with endometrial cancer. A diagnostic model was defined as the ratio between acylcarnitine C16 and phosphatidylcholine PCae C40:1, the ratio between proline and tyrosine, and the ratio between the two phosphatidylcholines PCaa C42:0 and PCae C44:5; which provided sensitivity of 85.25%, specificity of 69.23%, and AUC of 0.837. Addition of smoking status further improved the constructed diagnostic model (AUC = 0.855). The presence of the major prognostic factors of deep myometrial invasion and lymphovascular invasion were also associated with altered metabolite concentrations. A prognostic model for deep myometrial invasion included the ratio between two hydroxysphingomyelins SMOH C14:1 and SMOH C24:1, and the ratio between two phosphatidylcholines PCaa C40:2 and PCaa C42:6, which provided sensitivity of 81.25%, specificity of 86.36%, and AUC of 0.857. The model for lymphovascular invasion included the ratio between two phosphatidylcholines PCaa C34:4 and PCae C38:3, and the ratio between acylcarnitine C16:2 and phosphatidylcholine PCaa C38:1, which provided sensitivity of 88.89%, specificity of 84.31%, and AUC of 0.935.

Innovative methods for biomarker discovery in the evaluation and development of cancer precision therapies

The discovery of biomarkers able to detect cancer at an early stage, to evaluate its aggressiveness, and to predict the response to therapy remains a major challenge in clinical oncology and precision medicine. In this review, we summarize recent achievements in the discovery and development of cancer biomarkers. We also highlight emerging innovative methods in biomarker discovery and provide insights into the challenges faced in their evaluation and validation.

Using the New CellCollector to Capture Circulating Tumor Cells from Blood in Different Groups of Pulmonary Disease: A Cohort Study

Circulating tumor cells (CTCs) are promising biomarkers for clinical application. Cancer screening with Low-Dose Computed Tomography (LDCT) and CTC detections in pulmonary nodule patients has never been reported. The aim of this study was to explore the effectiveness of the combined methods to screen lung cancer. Out of 8313 volunteers screened by LDCT, 32 ground-glass nodules (GGNs) patients and 19 healthy volunteers were randomly selected. Meanwhile, 15 lung cancer patients also enrolled. CellCollector, a new CTC capturing device, was applied for CTCs detection. In GGNs group, five CTC positive patients with six CTCs were identified, 15.6% were positive (range, 1–2). In lung cancer group, 73.3% of the analyzed CellCollector cells were positive (range, 1–7) and no “CTC-like” events were detected in healthy group. All CTCs detected from GGNs group were isolated from the CellCollector functional domain and determined by whole genomic amplification for next-generation sequencing(NGS) analysis. NGS data showed that three cancer-related genes contained mutations in five CTC positive patients, including KIT, SMARCB1 and TP53 genes. In four patients, 16 mutation genes existed. Therefore, LDCT combined with CTC analysis by an in vivo device in high-risk pulmonary nodule patients was a promising way to screen early stage lung cancer.

Metabolic phenotyping of human plasma by 1 H-NMR at high and medium magnetic field strengths: a case study for lung cancer

Accurate identification and quantification of human plasma metabolites can be challenging in crowded regions of the NMR spectrum with severe signal overlap. Therefore, this study describes metabolite spiking experiments on the basis of which the NMR spectrum can be rationally segmented into well-defined integration regions, and this for spectrometers having magnetic field strengths corresponding to ¹ H resonance frequencies of 400 MHz and 900 MHz. Subsequently, the integration data of a case-control dataset of 69 lung cancer patients and 74 controls were used to train a multivariate statistical classification model for both field strengths. In this way, the advantages/disadvantages of high versus medium magnetic field strength were evaluated. The discriminative power obtained from the data collected at the two magnetic field strengths is rather similar, i.e. a sensitivity and specificity of respectively 90 and 97% for the 400 MHz data versus 88 and 96% for the 900 MHz data. This shows that a medium-field NMR spectrometer (400-600 MHz) is already sufficient to perform clinical metabolomics. However, the improved spectral resolution (reduced signal overlap) and signal-to-noise ratio of 900 MHz spectra yield more integration regions that represent a single metabolite. This will simplify the unraveling and understanding of the related, disease disturbed, biochemical pathways. Copyright © 2017 John Wiley & Sons, Ltd.