Limits...
Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry.

Miyamoto S, Taylor SL, Barupal DK, Taguchi A, Wohlgemuth G, Wikoff WR, Yoneda KY, Gandara DR, Hanash SM, Kim K, Fiehn O - Metabolites (2015)

Bottom Line: Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05).Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased.Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

View Article: PubMed Central - PubMed

Affiliation: Division of Hematology/Oncology, UC Davis Cancer Center, 4501 X Street, Room 3016, Sacramento, CA 95817, USA. smiyamoto@ucdavis.edu.

ABSTRACT
Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

No MeSH data available.


Related in: MedlinePlus

Multivariate PLS separates lung cancer patients and controls in two independent studies by the global metabolomic profiles. (A) PLS of Study 1 data results with gender and age adjusted; (B) PLS of Study 1 without gender and age adjusted; (C) PLS of Study 2 with gender and age adjusted; (D) PLS of Study 2 without gender and age adjusted. Red squares denote control cases and solid blue circles denote cancer cases.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4495369&req=5

metabolites-05-00192-f002: Multivariate PLS separates lung cancer patients and controls in two independent studies by the global metabolomic profiles. (A) PLS of Study 1 data results with gender and age adjusted; (B) PLS of Study 1 without gender and age adjusted; (C) PLS of Study 2 with gender and age adjusted; (D) PLS of Study 2 without gender and age adjusted. Red squares denote control cases and solid blue circles denote cancer cases.

Mentions: Multivariate analysis [23] using partial least square (PLS) [24] with linear discriminant analysis (LDA) with and without adjusting for age and gender was also performed to determine whether the blood metabolome as a combination of all metabolites identified in this study could discriminate cancer cases from control samples. PLS was used to reduce the 437 spectral peaks, each representing a metabolite, to a smaller number of latent components that distinguished cancer cases and controls and then determined which peaks were most influential in separating cases and controls as possible biomarkers. Metabolomic results from the cases and controls of Study 1 were separated by the first and second components when adjusted for age and sex (Figure 2A) that also showed good separation when the results were not adjusted for co-variants (Figure 2C). Based on leave-one-out cross-validation (LOOCV), we attempted to assess the performance of the metabolites to correctly identify the cancer cases. Sixty-three % of Study 1 samples were correctly classified , with 66.7% sensitivity and 60% specificity when two latent components were used to predict cancer status with adjustment for age and gender (Table S3A) and without adjustment for age and gender (Table S3B).


Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry.

Miyamoto S, Taylor SL, Barupal DK, Taguchi A, Wohlgemuth G, Wikoff WR, Yoneda KY, Gandara DR, Hanash SM, Kim K, Fiehn O - Metabolites (2015)

Multivariate PLS separates lung cancer patients and controls in two independent studies by the global metabolomic profiles. (A) PLS of Study 1 data results with gender and age adjusted; (B) PLS of Study 1 without gender and age adjusted; (C) PLS of Study 2 with gender and age adjusted; (D) PLS of Study 2 without gender and age adjusted. Red squares denote control cases and solid blue circles denote cancer cases.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495369&req=5

metabolites-05-00192-f002: Multivariate PLS separates lung cancer patients and controls in two independent studies by the global metabolomic profiles. (A) PLS of Study 1 data results with gender and age adjusted; (B) PLS of Study 1 without gender and age adjusted; (C) PLS of Study 2 with gender and age adjusted; (D) PLS of Study 2 without gender and age adjusted. Red squares denote control cases and solid blue circles denote cancer cases.
Mentions: Multivariate analysis [23] using partial least square (PLS) [24] with linear discriminant analysis (LDA) with and without adjusting for age and gender was also performed to determine whether the blood metabolome as a combination of all metabolites identified in this study could discriminate cancer cases from control samples. PLS was used to reduce the 437 spectral peaks, each representing a metabolite, to a smaller number of latent components that distinguished cancer cases and controls and then determined which peaks were most influential in separating cases and controls as possible biomarkers. Metabolomic results from the cases and controls of Study 1 were separated by the first and second components when adjusted for age and sex (Figure 2A) that also showed good separation when the results were not adjusted for co-variants (Figure 2C). Based on leave-one-out cross-validation (LOOCV), we attempted to assess the performance of the metabolites to correctly identify the cancer cases. Sixty-three % of Study 1 samples were correctly classified , with 66.7% sensitivity and 60% specificity when two latent components were used to predict cancer status with adjustment for age and gender (Table S3A) and without adjustment for age and gender (Table S3B).

Bottom Line: Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05).Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased.Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

View Article: PubMed Central - PubMed

Affiliation: Division of Hematology/Oncology, UC Davis Cancer Center, 4501 X Street, Room 3016, Sacramento, CA 95817, USA. smiyamoto@ucdavis.edu.

ABSTRACT
Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

No MeSH data available.


Related in: MedlinePlus