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:Recently, the National Lung Cancer Screen Trial (NLST) demonstrated that low-dose CT (LDCT) screening could reduce mortality due to lung cancer by 20percent. However, LDCT screening is largely hindered by high false-positive rates (96 percent), particularly in high risk populations (heavy smokers), due to the low prevalence rates (less than 2percent) 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:Background: There is a paucity of non-invasive markers for early detection of brain metastasis (BM) in patients with non-small cell lung cancer (NSCLC). Secretory proteomic approach based on quantitative tandem mass tag is believed to offer bright prospects for screening potential serum biomarkers for diseases. Methods: We used the secretory proteomics to identify up-regulated proteins in the highly brain metastatic cell line compared to the parental lung cancer cell line. Enzyme linked immunoassay and immunohistochemistry were performed on non-small cell lung cancer patients with brain metastasis and control samples from each cohort to verify the expression levels of candidate proteins. A logistic regression model was used to develop a biomarker panel composed of Cathepsin F (CTSF) and Fibulin-1 (FBLN1), and then an independent validation data set was used for verification. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curve analysis. Changes in serum CTSF levels of patients were tracked to monitor the effect of treatment, and progression-free survival (PFS) and overall survival (OS) were analyzed to assess their prognostic relevance. Results: CTSF and FBLN1 levels were specifically upregulated in sera and tissues of patients with NSCLC BM. Importantly, the combined diagnostic performance of CTSF and FBLN1 was superior to their individual diagnostic performance (sensitivity and specificity). CTSF serum changes were found to reflect the therapeutic response of patients with NSCLC BM and the trends of resistance and progression were detected earlier than the MRI changes. Elevated expression of CTSF in NSCLC BM tissues was associated with poor PFS. Moreover, CTSF tissue expression was found to be an independent prognostic factor. Conclusions: CTSF and FBLN1 are potential novel and specific serum markers for early diagnosis of BM in patients with NSCLC. CTSF can also be used as a biomarker for monitoring therapeutic efficacy and prognostic assessment.

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Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer

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