Non-invasive Diagnosis Of Lung Cancer Research Articles

Machine Learning in Lung Cancer Radiomics

Lung cancer is the leading cause of cancer-related deaths worldwide. Medical imaging technologies such as computed tomography (CT) and positron emission tomography (PET) are routinely used for non-invasive lung cancer diagnosis. In clinical practice, physicians investigate the characteristics of tumors such as the size, shape and location from CT and PET images to make decisions. Recently, scientists have proposed various computational image features that can capture more information than that directly perceivable by human eyes, which promotes the rise of radiomics. Radiomics is a research field on the conversion of medical images into high-dimensional features with data-driven methods to help subsequent data mining for better clinical decision support. Radiomic analysis has four major steps: image preprocessing, tumor segmentation, feature extraction and clinical prediction. Machine learning, including the high-profile deep learning, facilitates the development and application of radiomic methods. Various radiomic methods have been proposed recently, such as the construction of radiomic signatures, tumor habitat analysis, cluster pattern characterization and end-to-end prediction of tumor properties. These methods have been applied in many studies aiming at lung cancer diagnosis, treatment and monitoring, shedding light on future non-invasive evaluations of the nodule malignancy, histological subtypes, genomic properties and treatment responses. In this review, we summarized and categorized the studies on the general workflow, methods for clinical prediction and clinical applications of machine learning in lung cancer radiomic studies, introduced some commonly-used software tools, and discussed the limitations of current methods and possible future directions.

R.E.A.C.T-Rapid Electro-Analytical graphitic Carbon nitride-based screening Tool for lung cancer – Case study using heptane

Early disease detection and diagnosis through breath-based chemical assessment is broadly studied as a non-invasive tool and used as a cutting-edge opportunity in health care. Breath analytics or Breathomics is based on the recognition of levels of metabolites such as Volatile Organic Compounds (VOCs) and inorganic gases in an exhaled human breath. Lung cancer, one such disease state, alters the concentrations of hydrocarbons released in breath due to oxidative stress and lipid pre-oxidation. Heptane can be utilized as a VOC biomarker for the non-invasive diagnosis of lung cancer. This work outlines the fabrication of a graphitic carbon nitride-based electrochemical sensor platform that possesses increased catalytic activity and can be used for the screening of heptane vapors in the range of 0.45–5 ppm limit of detection of 0.45 ppm with 95% confidence interval. The synthesized material is characterized and validated using various standard analytical methods. Chronoamperometry is employed as an electrochemical technique to examine the diffusion dynamics of the target analyte. We demonstrated the specific sensing responses of the system in the presence of interferants by executing a cross-reactivity study with respect to other commonly found interferants in breath. We effectively established the use of a graphitic carbon nitride-based electrochemical sensor for point-of-care screening (qualitative analysis with p < 0.05) of heptane levels by the development of a prototype device utilizing a commercial microelectronic board. We envision that the in-house fabricated modified electrode platform can aid in early screening for lung cancer, thus leading to a reduction in the mortality rate.

Progress in the Application of Energy-spectrum CT Imaging and Radiomics for the Diagnosis of Lung Cancer and Subtype Differentiation

Lung cancer is the most prevalent malignancy and a serious threat to human health, and the early non-invasive diagnosis of lung cancer has been a hot topic of research in recent years. Lung cancer is subtyped into small cell lung cancer and non-small cell lung cancer, which is closely related to long-term smoking exposure history and chronic lung diseases, and its subtypes are inseparable from the diagnosis and prognosis of lung cancer. With the iterative development of CT technology, energy spectrum CT has been applied. Energy spectral CT of lung cancer can reduce artifacts, improve signal-to-noise ratio and clarity of images, and provide more information for CT diagnosis of lung cancer by combining multi-parameter energy spectral data at the same time. Radiomics refers to the acquisition of a large number of imaging histological features from other imaging images such as CT, ultrasound and MR, and the selection of valuable imaging histological clinical data for analysis. The exploration of disease by energy-spectrum CT combined with radiomics is a hot spot and trend in current research, and this paper will review the research progress of both on lung cancer subtypes.

Urinary analysis based on surface-enhanced Raman scattering for the noninvasive screening of lung cancer

• The AuNPs-based SERS probes were developed for the detection of adenosine • The platform can identify adenosine, uric acid, guanine and creatinine in urine • The assay was successfully applied for the noninvasive screening of lung cancer We developed a reliable simple analysis of adenosine, creatinine and other nucleosides (cytidine and guanosine) with single measurement based on surface-enhanced Raman spectroscopy (SERS) using DSP-AuNPs with Cu 2+ ions probe and applied it to achieve noninvasive screening of lung cancer. The gold nanoparticles (AuNPs) were modified with 3,3′-dithiodipropionic acid di (N-hydroxysuccinimide ester) (DSP), which could react with above mentioned molecules followed with the aggregation of AuNPs at the presence of Cu 2+ , thus form the plasmonic hot spots to dramatically increase their fingerprint Raman signal. The probe of DSP-AuNPs was applied for the detail analysis of urinary adenosine of healthy people and lung cancer patients. The SERS measurement indicated a higher concentration of urinary adenosine for lung cancer patients than that of healthy people, which was consistent with the results measured by high-performance liquid chromatography (HPLC). In combination of Raman spectroscopy with the orthogonal partial least squares discriminant analysis (OPLS-DA) model, we are able to discriminate the lung cancer patients from healthy people with the urinary test. The results indicated that besides of adenosine, other metabolites including uric acid, guanine and creatinine may also be the potential tumor markers in urine for the noninvasive lung cancer diagnosis. Such a method paves a way for the noninvasive cancer screening and it can be further modified for the detection of other molecules on medical diagnosis.

A Portable Fluorescence Sensor with Improved Performance for Aniline Monitoring

AbstractAniline is an important biomarker in human exhaled breath for lung cancer, thus aniline sensing is of paramount importance. However, highly sensitive and selective detection of aniline in high moisture environment of exhaled breath is still challenging. In this work, a new fluorescence sensor for aniline with ultrahigh sensitivity is constructed. The o‐carborane modified pyromellitic diimide derivative (CB‐PMI) is designed and prepared as the sensitive layer for the fluorescence sensor. As‐prepared sensing films exhibit a highly improved response toward aniline vapor. The good sensing performance of the fluorescence films in terms of response speed, selectivity, and reusability is also realized, and the detection limit can be as low as 0.1 ppt. The detection linear slope ranges from 1 to 50 ppt. In addition, a homemade portable sensing platform is successfully constructed, which may be a promising prototype for the future detection of modulating breath samples. The reported fluorescence sensor offers a new paradigm for the simple, low cost, and noninvasive diagnosis of lung cancer.

Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network.

Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (α-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model's good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 102 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.

Smartphone-based detection of lung cancer-related volatile organic compounds (VOCs) using rapid synthesized ZnO nanosheet

Point of care (POC) determination of volatile organic compounds (VOCs) in the ppb level is essential for non-invasive diagnosis applications. Chemical sensors are promising options to monitor VOCs in exhaled breath as disease biomarkers. Gas sensors based on nanostructured ZnO have received significant attention because of their advantages in enhancing gas sensing performance. In this study, a simple direct method is introduced for the rapid synthesis of zinc oxide nanosheet, and its application to fabricate a susceptible sensitive, smartphone-based resistive gas sensor for measurement of VOCs, that, are considered as lung cancer biomarkers. Zinc foil was anodized in KNO3 aqueous electrolyte at room temperature to synthesize nanosheets with approximate widths of 24 nm. The fabricated chemiresistive gas sensor using the synthesized ZnO nanosheet structure showed high sensitivity to measure VOCs such as diethyl ketone, acetone, and Isopropanol. An Arduino Uno with a Bluetooth module was used to read the fabricated ZnO-based sensor and send data to a smartphone. The smartphone-based chemiresistor device exhibited a high response to a low concentration of lung cancer-related volatile organic compounds. High humidity and the presence of hexane, ammonia, and carbon dioxide in the sensor environment didn’t affect the sensor response. The fabricated devices have the potential for non-invasive diagnosis of lung cancer in the early stage by analyzing relative biomarkers in exhaled breath.

CD5L as an Extracellular Vesicle-Derived Biomarker for Liquid Biopsy of Lung Cancer.

Cancer screening and diagnosis can be achieved by analyzing specific molecules within serum-derived extracellular vesicles (EVs). This study sought to profile EV-derived proteins to identify potential lung cancer biomarkers. EVs were isolated from 80 serum samples from healthy individuals and cancer patients via polyethylene glycol (PEG)-based precipitation and immunoaffinity separation using antibodies against CD9, CD63, CD81, and EpCAM. Proteomic analysis was performed using 2-D gel electrophoresis and matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI–TOF MS). The expression of proteins that were differentially upregulated in the EVs or tissue of lung cancer samples was validated by Western blotting. The area under the curve (AUC) was calculated to assess the predictability of each differentially expressed protein (DEP) for lung cancer. A total of 55 upregulated protein spots were selected, seven of which (CD5L, CLEC3B, ITIH4, SERFINF1, SAA4, SERFINC1, and C20ORF3) were found to be expressed at high levels in patient-derived EVs by Western blotting. Meanwhile, only the expression of EV CD5L correlated with that in cancer tissues. CD5L also demonstrated the highest AUC value (0.943) and was found to be the core regulator in a pathway related to cell dysfunction. Cumulatively, these results show that EV-derived CD5L may represent a potential biomarker—detected via a liquid biopsy—for the noninvasive diagnosis of lung cancer.

A novel technology to integrate imaging and clinical markers for non-invasive diagnosis of lung cancer

This study presents a non-invasive, automated, clinical diagnostic system for early diagnosis of lung cancer that integrates imaging data from a single computed tomography scan and breath bio-markers obtained from a single exhaled breath to quickly and accurately classify lung nodules. CT imaging and breath volatile organic compounds data were collected from 47 patients. Spherical Harmonics-based shape features to quantify the shape complexity of the pulmonary nodules, 7th-Order Markov Gibbs Random Field based appearance model to describe the spatial non-homogeneities in the pulmonary nodule, and volumetric features (size) of pulmonary nodules were calculated from CT images. 27 VOCs in exhaled breath were captured by a micro-reactor approach and quantied using mass spectrometry. CT and breath markers were input into a deep-learning autoencoder classifier with a leave-one-subject-out cross validation for nodule classification. To mitigate the limitation of a small sample size and validate the methodology for individual markers, retrospective CT scans from 467 patients with 727 pulmonary nodules, and breath samples from 504 patients were analyzed. The CAD system achieved 97.8% accuracy, 97.3% sensitivity, 100% specificity, and 99.1% area under curve in classifying pulmonary nodules.

Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's.

Noninvasive diagnosis of lung cancer in early stages is one task where radiomics helps. Clinical practice shows that the size of a nodule has high predictive power for malignancy. In the literature, convolutional neural networks (CNNs) have become widely used in medical image analysis. We study the ability of a CNN to capture nodule size in computed tomography images after images are resized for CNN input. For our experiments, we used the National Lung Screening Trial data set. Nodules were labeled into 2 categories (small/large) based on the original size of a nodule. After all extracted patches were re-sampled into 100-by-100-pixel images, a CNN was able to successfully classify test nodules into small- and large-size groups with high accuracy. To show the generality of our discovery, we repeated size classification experiments using Common Objects in Context (COCO) data set. From the data set, we selected 3 categories of images, namely, bears, cats, and dogs. For all 3 categories a 5- × 2-fold cross-validation was performed to put them into small and large classes. The average area under receiver operating curve is 0.954, 0.952, and 0.979 for the bear, cat, and dog categories, respectively. Thus, camera image rescaling also enables a CNN to discover the size of an object. The source code for experiments with the COCO data set is publicly available in Github (https://github.com/VisionAI-USF/COCO_Size_Decoding/).

Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics

Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians’ clinical decision-making. Exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification. In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods, to investigate the ability of exhaled breath to distinguish AC from SCC patients. The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. The result indicated that the KNN classifier combining borderline2-SMOTE and feature reduction methods was the most promising method to discriminate AC from SCC patients and obtained the highest mean area under the receiver operating characteristic curve (0.63) and mean geometric mean (58.50) when compared to others classifiers. The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with multivariate analysis is a promising screening method for informing treatment options and facilitating individualized treatment of lung cancer subtypes patients.

Fine-Grained Lung Cancer Classification from PET and CT Images Based on Multidimensional Attention Mechanism

Lung cancer ranks among the most common types of cancer. Noninvasive computer-aided diagnosis can enable large-scale rapid screening of potential patients with lung cancer. Deep learning methods have already been applied for the automatic diagnosis of lung cancer in the past. Due to restrictions caused by single modality images of dataset as well as the lack of approaches that allow for a reliable extraction of fine-grained features from different imaging modalities, research regarding the automated diagnosis of lung cancer based on noninvasive clinical images requires further study. In this paper, we present a deep learning architecture that combines the fine-grained feature from PET and CT images that allow for the noninvasive diagnosis of lung cancer. The multidimensional (regarding the channel as well as spatial dimensions) attention mechanism is used to effectively reduce feature noise when extracting fine-grained features from each imaging modality. We conduct a comparative analysis of the two aspects of feature fusion and attention mechanism through quantitative evaluation metrics and the visualization of deep learning process. In our experiments, we obtained an area under the ROC curve of 0.92 (balanced accuracy = 0.72) and a more focused network attention which shows the effective extraction of the fine-grained feature from each imaging modality.

Diagnostic value of plasma expression of microRNAs complementary to Drosha and Dicer in lung cancer patients.

Lung cancer (LC) is diagnosed mostly in advanced, non-operable stage, with poor prognosis. The analysis of microRNAs may be a useful tool for early and non-invasive detection of cancer. Dicer and Drosha are enzymes with an essential role for microRNA biogenesis. The aim of our study was to analyze the expression of miRNA-27a-3p, miRNA-31, miRNA-182, miRNA-195 with the ability to reciprocal regulation of Dicer and Drosha expression in lung cancer patients. The relative expression of microRNAs was detected by qPCR in plasma of 160 LC patients. The U-Mann Whitney test was used to compare the relative expression between particular groups of lung cancer patients and healthy individuals. The diagnostic value of microRNAs examination was analyzed using a receiver operating curve. We demonstrated that the plasma levels of miRNA-27, miRNA-31 and miRNA-182 were significantly higher and miRNA-195 significantly lower in the whole group of LC patients and in patients with early stages of NSCLC, in comparison with healthy donors. ROC analysis showed that four studied microRNAs have a potential diagnostic value for early stages of NSCLC with AUC=0.95 for miRNA-27a (94% sensitivity and 81% specificity, p=0.0001), 0.71 for miRNA-31 (73% sensitivity and 61% specificity, p=0.001) 0.77 for miRNA-182 (70% sensitivity and 79% specificity, p=0.0001) and 0.82 for miRNA-195 (74% sensitivity and 80% specificity, p=0.0001). We have proved that the expression of miRNA-27a-3p, miRNA-31, miRNA-182, and miRNA-195 in patients with LC is different from the expression of these molecules in healthy people. The examination of these microRNAs in plasma could be used in non-invasive lung cancer diagnosis.

P1.11-03 MicroRNA with Ability to Reciprocal Regulation of Dicer and Drosha – Plasma Expression Status and Diagnostic Value in Non-Small Cell Lung Cancer

Examination of microRNAs expression in plasma could be useful in screening and in early detection of non-small cell lung cancer (NSCLC). One of the most important enzymes in miRNA biogenesis are Dicer and Drosha. Disrupted expression of miRNA with ability to reciprocal regulation of Dicer and Drosha could participate in cancer development. Plasma expression of miR-27-3p, miR-31, miR-182 and miR-195 were analysed in 138 Polish NSCLC patients (median age 65 years, 83 male and 55 female) and in 45 healthy people (median age 62 years, 28 male and 17 female). 57 (41.3%) NSCLC patient were in I-IIIA stage and 81 (58.7%) patients were in IIIB-IV stage. Relative expression of microRNAs between studied groups was compared using U Mann-Whitney test. For assessment of diagnostic accuracy (test sensitivity and specificity), the receiver operating curves (ROC) with area under curve (AUC) analysis were generated. We demonstrated that plasma levels of miR-27-3p, miR-31 and miR-182 were significantly higher (p<0.000001, p=0.00008, p=0.006 respectively) and miR-195 significantly lower (p=0.000002) in NSCLC patients in comparison with healthy donors. Moreover, patients with early stages (I-IIIA) of NSCLC showed significantly higher expression of miR-27a-3p (p<0.000001), miR-31 (p=0.0003) and miR-182 (p=0.000003) than healthy persons. Expression of miR-195 was significantly lower in patients with early stages (I-IIIA) of NSCLC than in healthy donors (p<0.000001). AUC for miR-27a was 0.95 (94% sensitivity and 81% specificity, p<0.00001), for miR-31 was 0.71 (73% sensitivity and 61% specificity, p=0.001), for miR-182 was 0.77 (70% sensitivity and 79% specificity, p<0.00001) and for miR-195 was 0.82 (74% sensitivity and 80% specificity, p=0.00001). Expression of miR-27a, miR-31, miR-182 and miR-195 could distinguish patients with NSCLC from healthy people. The examination of these microRNAs in plasma could be used in non-invasive lung cancer diagnosis. Deregulation of Dicer and Drosha expression by microRNAs could have oncogenic character.

Identification of beta-arrestin-1 as a diagnostic biomarker in lung cancer

BackgroundDistinguishing lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) has a tremendous therapeutic implication. Sometimes, the commonly used immunohistochemistry (IHC) markers fail to discriminate between them, urging for the identification of new diagnostic biomarkers.MethodsWe performed IHC on tissue microarrays from two cohorts of lung cancer patients to analyse the expression of beta-arrestin-1, beta-arrestin-2 and clinically used diagnostic markers in ADC and SCC samples. Logistic regression models were applied for tumour subtype prediction. Parallel reaction monitoring (PRM)-based mass spectrometry was used to quantify beta-arrestin-1 in plasma from cancer patients and healthy donors.ResultsBeta-arrestin-1 expression was significantly higher in ADC versus SCC samples. Beta-arrestin-1 displayed high sensitivity, specificity and negative predictive value. Its usefulness in an IHC panel was also shown. Plasma beta-arrestin-1 levels were considerably higher in lung cancer patients than in healthy donors and were higher in patients who later experienced a progressive disease than in patients showing complete/partial response following EGFR inhibitor therapy.ConclusionsOur data identify beta-arrestin-1 as a diagnostic marker to differentiate ADC from SCC and indicate its potential as a plasma biomarker for non-invasive diagnosis of lung cancer. Its utility to predict response to EGFR inhibitors is yet to be confirmed.

SC18.02 Integrating Lung Cancer Biomarkers into Future Screening Programs

SC18.02 Integrating Lung Cancer Biomarkers into Future Screening Programs

The Pursuit of Noninvasive Diagnosis of Lung Cancer.

The noninvasive diagnosis of lung cancer remains a formidable challenge. Although tissue diagnosis will remain the gold standard for the foreseeable future, questions pertaining to the risks and costs associated with invasive diagnostic procedures are of prime relevance. This review addresses new modalities for improving the noninvasive evaluation of suspicious lung nodules. Ultimately, the goal is to translate early diagnosis into early treatment. We discuss how biomarkers could assist in distinguishing benign from malignant nodules and aggressive from indolent tumors. The field of biomarkers is rapidly expanding and progressing, and efforts are well underway to apply molecular diagnostics to address the shortcomings of current lung cancer diagnostic tools.

Analysis of RTEL1 and PCDHGB6 promoter methylation in circulating-free DNA of lung cancer patients using liquid biopsy: A pilot study

ABSTRACTBackground: Analysis of epigenetic alterations such as methylation of circulating-free DNA (cf-DNA) expression significantly broadened perspectives of lung cancer (LC) screening. Moreover, methylation of tumor suppressor genes may be analyzed with non-invasive manner in patients' blood samples (liquid biopsy), what underline necessity of detailed investigation of tumor cf-DNA. Material and Methods: The purpose of current study was to assess methylation of RTEL1 and PCDHGB6 promoter regions in cf-DNA of 70 LC patients and 80 healthy individuals using qMSP-PCR technique. Methylation status of both genes has not been investigated in cf-DNA of LC patients before. Results: PCDHGB6 promoter methylation was found in 41.4% of LC patients and in 1.3% of healthy individuals, whereas promoter of RTEL1 was found methylated in 51.4% of LC patients and in 8.8% of healthy individuals. Combined analysis of two markers improved test sensitivity up to 62.9% and specificity up to 90% with area under the curve (AUC) in receiver operating curve (ROC) of 0.755. Conclusions: The evaluation of RTEL1 and PCDHGB6 promoter methylation may be an useful tool for non-invasive diagnosis of LC in liquid biopsy.

Application of plasma circulating microRNA-448, 506, 4316, and 4478 analysis for non-invasive diagnosis of lung cancer.

Aberrant expression of microRNAs (miRNAs) in cancer patients compared to healthy people as well as possibility of detection of these molecules in blood samples make them potential biomarkers of various cancers. In the present study, we investigated the potential role of four miRNAs as lung cancer (LC) biomarkers: miRNA-448, 506, 4316, and 4478. Using quantitative reverse transcription polymerase chain reaction (qRT-PCR) technique, we assessed expression of studied miRNAs in plasma samples of 90 lung cancer patients and 85 healthy individuals. Receiver operating curves (ROC) with area under the curve (AUC) were used to assess accuracy of studied miRNAs for distinguishing LC patients from healthy individuals. The miRNA-448 and 4478 were significantly overexpressed in lung cancer patients compared to healthy people and these two molecules were qualified for further analysis. Combination ROC analysis of both biomarkers reached 90% of sensitivity and 76.3% of specificity (AUC = 0.896) for distinguishing operable (stage IA-IIB) non-small cell lung cancer (NSCLC) patients from healthy subjects. Our results suggest that the examination of miRNAs could be considered as potential lung cancer, non-invasive biomarkers.

Comparative analyses of volatile organic compounds (VOCs) from patients, tumors and transformed cell lines for the validation of lung cancer-derived breath markers

Breath analysis for the purpose of non-invasive diagnosis of lung cancer has yielded numerous candidate compounds with still questionable clinical relevance. To arrive at suitable volatile organic compounds our approach combined the analysis of different sources: isolated tumor samples compared to healthy lung tissues, and exhaled breath from lung cancer patients and healthy controls. Candidate compounds were further compared to substances previously identified in the comparison of transformed and normal lung epithelial cell lines. For human studies, a breath sampling device was developed enabling automated and CO2-controlled collection of the end-tidal air. All samples were first preconcentrated on multibed sorption tubes and analyzed with gas chromatography mass spectrometry (GC-MS). Significantly (p < 0.05) higher concentrations in all three types of cancer samples studied were observed for ethanol and n-octane. Additional metabolites (inter alia 2-methylpentane, n-hexane) significantly released by lung cancer cells were observed at higher levels in cancer lung tissues and breath samples (compared to respective healthy controls) with statistical significance (p < 0.05) only in breath samples. The results obtained confirmed the cancer-related origin of volatile metabolites, e.g. ethanol and octane that were both detected at significantly (p < 0.05) elevated concentrations in all three kinds of cancer samples studied. This work is an important step towards identification of volatile breath markers of lung cancer through the demonstration of cancer-related origin of certain volatile metabolites.