Dataset Information

Metabolomics Workbench ST000396 - GNPS - Lung Cancer Plasma Discovery

ABSTRACT: Recently, major efforts have been directed toward early detection of lung cancer through low-dose computed tomography (LDCT) scanning. Data from the National Lung Screening Trial (NLST) suggest that yearly screening with thoracic LDCT scanning for high-risk current and former smokers reduces lung cancer mortality by 20% and total mortality by 7%. However, issues including indeterminate nodules detected by LDCT and radiation exposure impact the practicality of LDCT-based screening on a national and global basis. A blood-based biomarker or multiplexed marker panel that could complement LDCT would represent a major advance in implementing lung cancer screening. Efforts to develop blood-based biomarkers for lung cancer early detection using a variety of methodologies are currently ongoing. Proteomic studies have led to the identification of several candidate markers including pro-surfactantproteinB(pro-SFTPB), a target of a lineage-survival oncogene in lung cancer, NKX2-1.Validation studies using blood samples collected at the time of LDCT screening for lung cancer substantiated the performance of pro-SFTPB. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB. The area under the curve (AUC) values of the full model with and without pro-SFTPB were 0.741 (95% CI, 0.696 to 0.783) and 0.669 (95%CI, 0.620 to 0.717), respectively (difference in AUC, P_.001). Single markers are unlikely to have sufficient performance for implementation in a screening setting, hence the need to explore several discovery platforms to identify markers that provide complementary performance. Metabolomics represents a global unbiased approach to the profiling of small molecules and has been established as a platform for biomarker discovery for a variety of human biofluids and tissues. Here we used an untargeted liquid chromatography/mass spectrometry (MS) metabolomics approach to identify metabolites that distinguish human sera collected before the diagnosis of lung cancer from matched control sera in a prospective cohort of highrisk patients from the Beta-Carotene and Retinol Efficacy Trial (CARET).

INSTRUMENT(S): Pegasus

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Oliver Fiehn

PROVIDER: MSV000084032 | GNPS | Mon Jul 01 15:14:00 BST 2019

REPOSITORIES: GNPS

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Recently, major efforts have been directed toward early detection of lung cancer through low-dose computed tomography (LDCT) scanning. Data from the National Lung Screening Trial (NLST) suggest that yearly screening with thoracic LDCT scanning for high-risk current and former smokers reduces lung cancer mortality by 20% and total mortality by 7%. However, issues including indeterminate nodules detected by LDCT and radiation exposure impact the practicality of LDCT-based screening on a national and global basis. A blood-based biomarker or multiplexed marker panel that could complement LDCT would represent a major advance in implementing lung cancer screening. Efforts to develop blood-based biomarkers for lung cancer early detection using a variety of methodologies are currently ongoing. Proteomic studies have led to the identification of several candidate markers including pro-surfactantproteinB(pro-SFTPB), a target of a lineage-survival oncogene in lung cancer, NKX2-1.Validation studies using blood samples collected at the time of LDCT screening for lung cancer substantiated the performance of pro-SFTPB. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB. The area under the curve (AUC) values of the full model with and without pro-SFTPB were 0.741 (95% CI, 0.696 to 0.783) and 0.669 (95%CI, 0.620 to 0.717), respectively (difference in AUC, P_.001). Single markers are unlikely to have sufficient performance for implementation in a screening setting, hence the need to explore several discovery platforms to identify markers that provide complementary performance. Metabolomics represents a global unbiased approach to the profiling of small molecules and has been established as a platform for biomarker discovery for a variety of human biofluids and tissues. Here we used an untargeted liquid chromatography/mass spectrometry (MS) metabolomics approach to identify metabolites that distinguish human sera collected before the diagnosis of lung cancer from matched control sera in a prospective cohort of highrisk patients from the Beta-Carotene and Retinol Efficacy Trial (CARET).

Project description:Affordable early screening in subjects with high risk of lung cancer has great potential to improve survival from this deadly disease. We measured gene expression from lung tissue and peripheral whole blood (PWB) from adenocarcinoma cases and controls to identify dysregulated lung cancer genes that could be tested in blood to improve identification of at-risk patients in the future. Genome-wide mRNA expression analysis was conducted in 153 subjects (73 adenocarcinoma cases, 80 controls) from the Environment And Genetics in Lung cancer Etiology (EAGLE) study using PWB and paired snap-frozen tumor and non-involved lung tissue samples. Analyses were conducted using unpaired t-tests, linear mixed effects and ANOVA models. The area under the receiver operating characteristic curve (AUC) was computed to assess the predictive accuracy of the identified biomarkers. We identified 50 dysregulated genes in stage I adenocarcinoma versus control PWB samples (False Discovery Rate â¤0.1, fold change â¥1.5 or â¤0.66). Among them, eight (TGFBR3, RUNX3, TRGC2, TRGV9, TARP, ACP1, VCAN, and TSTA3) differentiated paired tumor versus non-involved lung tissue samples in stage I cases, suggesting a similar pattern of lung cancer-related changes in PWB and lung tissue. These results were confirmed in two independent gene expression analyses in a blood-based case-control study (n=212) and a tumor-non tumor paired tissue study (n=54). The eight genes discriminated patients with lung cancer from healthy controls with high accuracy (AUC=0.81, 95% CI=0.74-0.87). Our finding suggests the use of gene expression from PWB for the identification of early detection markers of lung cancer in the future. Samples from 164 subjects were initially included in the study. Two samples with poor quality profile based on quality assessment (described in Supplemental Material 2) were excluded before normalization. The remaining 162 samples were processed and normalized with the Robust Multichip Average (RMA) method. Nine additional subjects were excluded after data normalization because of reclassification to non-adenocarcinoma morphology during histologic review. The final analyses were based on 73 adenocarcinoma cases and 80 controls. All 22,277 probe sets based on RMA summary measures were used in the analyses.

Dataset Information

Metabolomics Workbench ST000396 - GNPS - Lung Cancer Plasma Discovery

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets