Project description:Recently, the National Lung Cancer Screen Trial (NLST) demonstrated that low-dose CT (LDCT) screening could reduce mortality due to lung cancer by 20%. However, LDCT screening is largely hindered by high false-positive rates (96%), particularly in high-risk populations (heavy smokers), due to the low prevalence rates (less than 2%) of malignant tumors and high incidence of benign lung nodules. Consequently, complementary biomarkers that can be used in conjunction with LDCT screening to improve diagnostic capacities and reduce false-positive rates are highly desirable. Preferably, such complementary tools should be noninvasive and exhibit high sensitivity and specificity. The application of “-omic” sciences (genomics, transcriptomics, proteomics, and metabolomics) represents valuable tools for the discovery and validation of potential biomarkers that can be used for detection of NSCLC. Of these omic sciences, metabolomics has received considerable attention for its application in cancer. Metabolomics is the assessment of small molecules and biochemical intermediates (metabolites) using analytic instrumentation. Metabolites in blood are the product of all cellular processes, which are highly responsive to conditions of disease and environment, and represent the final output products of all organs forming a detailed systemic representation of an individual's current physiologic state. In this study, we used an untargeted metabolomics approach using gas chromatography time-of-flight mass spectrometry (GCTOFMS) to analyze the metabolome of serum and plasma samples both collected from the same patients that were organized into two independent case–control studies (ADC1 and ADC2). In both studies, only NSCLC adenocarcinoma was investigated. The overall objectives were to (i) determine whether individual or combinations of metabolites could be used as a diagnostic test to distinguish NSCLC adenocarcinoma from controls and (ii) to determine which, plasma or serum, provides more accurate classifiers for the detection of lung cancer. We developed individual and multimetabolite classifiers using a training test from the ADC1 study and evaluated the performance of the constructed classifiers, individually or in combination, in an independent test/validation study (ADC2). This study shows the potential of metabolite-based diagnostic tests for detection of lung adenocarcinoma. Further validation in a larger pool of samples is warranted.

Project description:Lung cancer has been the leading cause of cancer death in the United States and worldwide for many decades. Low dose spiral computerized tomography (LDCT) is likely to become the first approved screening and early detection test in the upcoming year, but it is plagued by a high false-positive rate. There is a need to develop complementary screening and early detection tools. A blood-based lung cancer signature is an attractive solution. Given that our knowledge of the molecular biology of smoking-induced lung cancer has dramatically increased over the past few years, this approach is plausible. To date, this effort has been focused on the identification of genomic and proteomic signatures with limited success. A broader strategy that incorporates additional cancer traits is needed. It is well recognized that wide coverage of cellular metabolism in cancer could help provide valuable diagnostic biomarkers and potentially identify molecular drivers of tumorigenesis. Recent advances in mass spectrometry have enabled comprehensive metabolomic analyses of lipids, carbohydrates, amino acids, and nucleotides within a variety of biologic matrices. Early evidence from metabolomic investigation of cancer has identified many altered biochemical profiles. However, to date, there have been few investigations of lung cancer, and most studies have looked at blood plasma or were limited by small sample sizes with mixed histologies. In the current investigation, gas chromatography time-offlight mass spectrometry (GC-TOF) was used to measure 462 lipid, carbohydrate, amino acid, organic acid, and nucleotide metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage adenocarcinoma. This study cohort represents patient characteristics and tumor histology most likely to be detected with LDCT screening. We hypothesize that identification of cancer-induced cellular and tissue level biochemical changes can offer a robust method for identification of candidate circulating biomarkers and improve our understanding of biochemical changes involved in adenocarcinoma tumorigenesis.

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Metabolomics Workbench ST000385 - GNPS - Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer (training set)

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