Metabolomic-based biomarker discovery for non-invasive lung cancer screening: A case study

AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.

Tuberculosis is associated with sputum metabolome variations, irrespective of patient sex or HIV status: an untargeted GCxGC-TOFMS study

INTRODUCTION

Various studies have identified TB-induced metabolome variations. However, in most of these studies, a large degree of variation exists between individual patients.

OBJECTIVES

To identify differential metabolites for TB, independent of patients' sex or HIV status.

METHODS

Untargeted GCxGC/TOF-MS analyses were applied to the sputum of 31 TB + and 197 TB- individuals. Univariate statistics were used to identify metabolites which are significantly different between TB + and TB- individuals (a) irrespective of HIV status, and (b) with a HIV + status. Comparisons a and b were repeated for (i) all participants, (ii) males only and (iii) females only.

RESULTS

Twenty-one compounds were significantly different between the TB + and TB- individuals within the female subgroup (11% lipids; 10% carbohydrates; 1% amino acids, 5% other and 73% unannotated), and 6 within the male subgroup (20% lipids; 40% carbohydrates; 6% amino acids, 7% other and 27% unannotated). For the HIV + patients (TB + vs. TB-), a total of 125 compounds were significant within the female subgroup (16% lipids; 8% carbohydrates; 12% amino acids, 6% organic acids, 8% other and 50% unannotated), and 44 within the male subgroup (17% lipids; 2% carbohydrates; 14% amino acids related, 8% organic acids, 9% other and 50% unannotated). Only one annotated compound, 1-oleoyl lysophosphaditic acid, was consistently identified as a differential metabolite for TB, irrespective of sex or HIV status. The potential clinical application of this compound should be evaluated further.

CONCLUSIONS

Our findings highlight the importance of considering confounders in metabolomics studies in order to identify unambiguous disease biomarkers.

Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics–AI systems, limitations thereof and recent tools were also discussed.

Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma

Background

The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases.

Methods

Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified.

Results

In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions.

Conclusion

Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.

Machine Learning Solutions Applied to Amyotrophic Lateral Sclerosis Prognosis: A Review

The prognosis of Amyotrophic Lateral Sclerosis (ALS), a complex and rare disease, represents a challenging and essential task to better comprehend its progression and improve patients' quality of life. The use of Machine Learning (ML) techniques in healthcare has produced valuable contributions to the prognosis field. This article presents a systematic and critical review of primary studies that used ML applied to the ALS prognosis, searching for databases, relevant predictor biomarkers, the ML algorithms and techniques, and their outcomes. We focused on studies that analyzed biomarkers commonly present in the ALS disease clinical practice, such as demographic, clinical, laboratory, and imaging data. Hence, we investigate studies to provide an overview of solutions that can be applied to develop decision support systems and be used by a higher number of ALS clinical settings. The studies were retrieved from PubMed, Science Direct, IEEEXplore, and Web of Science databases. After completing the searching and screening process, 10 articles were selected to be analyzed and summarized. The studies evaluated and used different ML algorithms, techniques, datasets, sample sizes, biomarkers, and performance metrics. Based on the results, three distinct types of prediction were identified: Disease Progression, Survival Time, and Need for Support. The biomarkers identified as relevant in more than one study were the ALSFRS/ALSFRS-R, disease duration, Forced Vital Capacity, Body Mass Index, age at onset, and Creatinine. In general, the studies presented promissory results that can be applied in developing decision support systems. Besides, we discussed the open challenges, the limitations identified, and future research opportunities.

Feature Selection Pipelines with Classification for Non-targeted Metabolomics Combining the Neural Network and Genetic Algorithm

Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this work, we present a pipeline that combines a convolutional neural network with traditional statistical approaches and an adaptation of a genetic algorithm. The developed method was applied to a lifestyle intervention cohort data set, where subjects at risk of type 2 diabetes underwent an oral glucose tolerance test. Feature selection is the final result of the pipeline, achieved through classification of the data set via a neural network, with a precision-recall score of over 0.9 on the test set. The features most relevant for the described classification were then chosen via a genetic algorithm. The output of the developed pipeline encompasses approximately 200 features with high predictive scores, providing a fingerprint of the metabolic changes in the prediabetic class on the data set. Our framework presents a new approach which allows to apply complex modeling based on convolutional neural networks for the analysis of high-resolution mass spectrometric data.

AIR-Net: A novel multi-task learning method with auxiliary image reconstruction for predicting EGFR mutation status on CT images of NSCLC patients

Automated and accurate EGFR mutation status prediction using computed tomography (CT) imagery is of great value for tailoring optimal treatments to non-small cell lung cancer (NSCLC) patients. However, existing deep learning based methods usually adopt a single task learning strategy to design and train EGFR mutation status prediction models with limited training data, which may be insufficient to learn distinguishable representations for promoting prediction performance. In this paper, a novel multi-task learning method named AIR-Net is proposed to precisely predict EGFR mutation status on CT images. First, an auxiliary image reconstruction task is effectively integrated with EGFR mutation status prediction, aiming at providing extra supervision at the training phase. Particularly, we adequately employ multi-level information in a shared encoder to generate more comprehensive representations of tumors. Second, a powerful feature consistency loss is further introduced to constrain semantic consistency of original and reconstructed images, which contributes to enhanced image reconstruction and offers more effective regularization to AIR-Net during training. Performance analysis of AIR-Net indicates that auxiliary image reconstruction plays an essential role in identifying EGFR mutation status. Furthermore, extensive experimental results demonstrate that our method achieves favorable performance against other competitive prediction methods. All the results executed in this study suggest that the effectiveness and superiority of AIR-Net in precisely predicting EGFR mutation status of NSCLC.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Metabolomic Laboratory-Developed Tests: Current Status and Perspectives

Laboratory-developed tests (LDTs) are a subset of in vitro diagnostic devices, which the US Food and Drug Administration defines as "tests that are manufactured by and used within a single laboratory". The review describes the emergence and history of LDTs. The current state and development prospects of LDTs based on metabolomics are analyzed. By comparing LDTs with the scientific metabolomics study of human bio samples, the characteristic features of metabolomic LDT are shown, revealing its essence, strengths, and limitations. The possibilities for further developments and scaling of metabolomic LDTs and their potential significance for healthcare are discussed. The legal aspects of LDT regulation in the United States, European Union, and Singapore, demonstrating different approaches to this issue, are also provided. Based on the data presented in the review, recommendations were made on the feasibility and ways of further introducing metabolomic LDTs into practice.

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.

TFAP2C increases cell proliferation by downregulating GADD45B and PMAIP1 in non-small cell lung cancer cells

Background

Non-small cell lung cancer (NSCLC) is one of the leading causes of death in the world. NSCLC diagnosed at an early stage can be highly curable with a positive prognosis, but biomarker limitations make it difficult to diagnose lung cancer at an early stage. To identify biomarkers for lung cancer development, we previously focused on the oncogenic roles of transcription factor TFAP2C in lung cancers and revealed the molecular mechanism of several oncogenes in lung tumorigenesis based on TFAP2C-related microarray analysis.

Results

In this study, we analyzed microarray data to identify tumor suppressor genes and nine genes downregulated by TFAP2C were screened. Among the nine genes, we focused on growth arrest and DNA-damage-inducible beta (GADD45B) and phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1) as representative TFAP2C-regulated tumor suppressor genes. It was observed that overexpressed TFAP2C resulted in inhibition of GADD45B and PMAIP1 expressions at both the mRNA and protein levels in NSCLC cells. In addition, downregulation of GADD45B and PMAIP1 by TFAP2C promoted cell proliferation and cell motility, which are closely associated with NSCLC tumorigenesis.

Conclusion

This study indicates that GADD45B and PMAIP1 could be promising tumor suppressors for NSCLC and might be useful as prognostic markers for use in NSCLC therapy.

Electronic supplementary material

The online version of this article (10.1186/s40659-019-0244-5) contains supplementary material, which is available to authorized users.

Integration of metabolomic and transcriptomic profiles to identify biomarkers in serum of lung cancer

We used blood serum samples collected from 31 lung cancer (LC) patients and 29 healthy volunteers in this study. Levels of serum metabolites were qualitative quantified with gas chromatography-mass spectrometry (GC-MS), and the data were analyzed by partial least-squares discrimination analysis (PLS-DA). Based on the Kyoto Encyclopedia of Genes and Genomes database, we performed pathway-based analysis utilizing metabolites presented at differential abundance between the LC serum samples and the normal healthy serum samples for systematical investigation on the metabolic alterations associated with LC pathogenesis. Finally, we analyzed the significantly enriched pathways as well as their relevant differentially expressed messenger RNAs, and drawn a correlation network plot to identify the serum metabolic biomarkers and the significantly altered metabolic pathways for LC. GC-MS analysis showed that 23 of the 169 metabolites identified were significantly different. PLS-DA model revealed that 13 of these metabolites were with variable importance > 1, and particularly five were with area under curve > 0.9. Pathway-based analysis demonstrated that five of eight enriched metabolic pathways were statistically significant with false discovery rate < 0.05. Lastly, the correlation networks between these pathways and their related genes suggested that 29 genes had correlation degree > 10, which were mainly engaged in the purine metabolism. In conclusion, we identified indole-3-lactate, erythritol, adenosine-5-phosphate, paracetamol and threitol as serum metabolic biomarkers for LC through metabolomics analysis. Besides, we identified the purine metabolism as the significantly altered metabolic pathway in LC with the help of transcriptomics analysis.

[An Overview of the Application of Artificial Neural Networks in Lung Cancer Research]

肺癌是目前全世界发病率、死亡率最高的肿瘤，目前的诊疗手段效果有限，精准医学的全面开展为提高肺癌诊疗水平带来了新的契机。但临床医生很难对精准医学需要的多维度多角度的资料（生物组学、临床检测指标以及非生物的环境背景资料等）进行有效的整合和利用，难以为患者选择最优的诊治方案。借助计算机技术的发展，以人工神经网络（artificial neural networks, ANNs）为代表的人工智能具有高容错性、智能性和具有自我学习能力的特点，其强大的信息整合能力可以对精准医学的发展与应用起到很大的帮助，在肺癌的基础研究和临床实践中发挥巨大的作用。本文对肺癌领域ANNs应用的现状进行综述。

Mathematical models of amino acid panel for assisting diagnosis of children acute leukemia

BACKGROUND

The altered concentrations of amino acids were found in the bone marrow or blood of leukemia patients. Metabolomics technology combining mathematical model of biomarkers could be used for assisting the diagnosis of pediatric acute leukemia (AL).

METHODS

The concentrations of 17 amino acids was measured by targeted liquid chromatograph-tandem mass spectrometry in periphery blood collected using dried blood spots. After evaluation, the mathematical models were further evaluated by prospective clinical validation cohort for AL diagnosis.

RESULTS

The concentrations of 13 in 17 amino acids were statistically different between the periphery blood dried serum dots measured by targeted LC-MS/MS. The receiver operating characteristic analysis for the models of amino acid panel showed that the area under curve for AL diagnosis were 0.848, 0.834 and 0.856 by SVM, RF and XGBoost. The Kappa values in further prospectively evaluated clinical cohort were 0.697, 0.703 and 0.789 (p > 0.05) respectively, and the accuracies for the models were 84.86%, 85.20% and 89.46% respectively with further clinical validation.

CONCLUSIONS

The established mathematical model is a faster, cheaper and more convenient way than conventional methods, and no significant difference on the effect of diagnosis comparing with conventional methods. The mathematical model can be clinically useful for assisting pediatric AL diagnosis.

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

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

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.

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.

Obstructive sleep apnea patients can be identified by ion mobility spectrometry-derived smell prints of different biological materials

BACKGROUND

The analysis of obstructive sleep apnoea syndrome (OSAS) is time- and cost-intensive. A number of studies demonstrated that the non-invasive analysis of exhaled breath (EB) may be suitable to distinguish between OSAS patients and healthy subjects (HS). Methods/Population: We included OSAS patients (n = 15) and HS (n = 15) in this diagnostic proof-of-concept-study. All participants underwent polygraphy to verify or exclude OSAS and performed spirometry to exclude pulmonary ventilatory diseases. The volatile organic compound profile of EB and of the headspaces over EB condensate, pharyngeal washing fluid, and serum was measured using ion mobility spectrometry (IMS) (BioScout^®) and an e-nose (Cyranose^® 320). For the statistical analysis, we fitted classification tree models using recursive partitioning, followed by a leave-one-out cross-validation. For the cross-validated predictions we calculated descriptive classification statistics, p-values from a [Formula: see text]-test with continuity correction, as well as ROC curves.

RESULTS

Using IMS, OSAS patients and HS could be distinguished with high accuracy (values ranged from 79% to 97%). The results of the e-nose-derived analyses (with the exception of EB) were less accurate. However, the cross-validated accuracy for EB was very good (0.9), reflecting a positive predictive value of 100% and a negative predictive value of 83%. For each material, we identified the best five substances that may be used for diagnostic purposes. 2-Methylfluran was found in three different biological materials to be discriminative between OSAS and HS.

CONCLUSION

The results strengthen the hypothesis that substances detectable in headspace measurements of different airway and blood materials may undergo a transition from blood into the alveoli (and EB) or vice versa. This means that substances from different compartments could be used to distinguish patients with airway diseases (in this case OSAS) from healthy controls.

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.

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites (p < 0.05) in RBC metabolism using only measurements of these five biomarkers. The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. The ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.

While deep-coverage omics data sets are allowing for more complete characterization of biological systems, there has been a concerted effort to identify a subset of measurements that are representative of qualitative network-level behavior. For some systems—like the human red blood cell (RBC)—such biomarkers have already been identified. Using the concentration profiles of these biomarkers as input to a statistical model, we predict quantitative concentration profiles of other metabolites in the RBC network. These results demonstrate that if good biomarkers are available for a biological system, it is possible to use these measurements to gain insight into the quantitative state of the rest of the network.

Metabolic Signatures of Lung Cancer in Sputum and Exhaled Breath Condensate Detected by 1H Magnetic Resonance Spectroscopy: A Feasibility Study

OBJECTIVES

Lung cancer is one of the most lethal cancers. Currently, there are no biomarkers for early detection, monitoring treatment response, and detecting recurrent lung cancer. We undertook this study to determine if 1H magnetic resonance spectroscopy (MRS) of sputum and exhaled breath condensate (EBC), as a noninvasive tool, can identify metabolic biomarkers of lung cancer.

MATERIALS AND METHODS

Sputum and EBC samples were collected from 20 patients, comprising patients with pathologically confirmed non-small cell lung cancer (n = 10) and patients with benign respiratory conditions (n = 10). Both sputum and EBC samples were collected from 18 patients; 2 patients provided EBC samples only. 1H MR spectra were obtained on a Bruker Avance 400 MHz nuclear magnetic resonance (NMR) spectrometer. Sputum samples were further confirmed cytologically to distinguish between true sputum and saliva.

RESULTS

In the EBC samples, median concentrations of propionate, ethanol, acetate, and acetone were higher in lung cancer patients compared to the patients with benign conditions. Median concentration of methanol was lower in lung cancer patients (0.028 mM) than in patients with benign conditions (0.067 mM; P = 0.028). In the combined sputum and saliva and the cytologically confirmed sputum samples, median concentrations of N-acetyl sugars, glycoprotein, propionate, lysine, acetate, and formate were lower in the lung cancer patients than in patients with benign conditions. Glucose was found to be consistently absent in the combined sputum and saliva samples (88%) as well as in the cytologically confirmed sputum samples (86%) of lung cancer patients.

CONCLUSION

Absence of glucose in sputum and lower concentrations of methanol in EBC of lung cancer patients discerned by 1H MRS may serve as metabolic biomarkers of lung cancer for early detection, monitoring treatment response, and detecting recurrence.