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Showing content from https://pubmed.ncbi.nlm.nih.gov/24736543/ below:

Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer

. 2014 Jun 15;74(12):3259-70. doi: 10.1158/0008-5472.CAN-14-0109. Epub 2014 Apr 15. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer Ewy A Mathé¹, Andrew D Patterson², Majda Haznadar², Soumen K Manna², Kristopher W Krausz², Elise D Bowman², Peter G Shields², Jeffrey R Idle², Philip B Smith², Katsuhiro Anami², Dickran G Kazandjian², Emmanuel Hatzakis², Frank J Gonzalez², Curtis C Harris³

Affiliations

¹ Authors' Affiliations: Laboratory of Molecular Immunogenomics, Genomic and Immunity Section, NIAMS/NIH; Laboratories of Human Carcinogenesis, and Metabolism, National Cancer Institute, NIH, Bethesda, Maryland; Department of Veterinary and Biomedical Sciences and Center for Molecular Toxicology and Carcinogenesis; Metabolomics Core Facility; Nuclear Magnetic Resonance Spectroscopy, The Pennsylvania State University, University Park, Pennsylvania; Ohio State University Comprehensive Cancer Center, Columbus, Ohio; and Department of Clinical Research, University of Bern, Bern, SwitzerlandAuthors' Affiliations: Laboratory of Molecular Immunogenomics, Genomic and Immunity Section, NIAMS/NIH; Laboratories of Human Carcinogenesis, and Metabolism, National Cancer Institute, NIH, Bethesda, Maryland; Department of Veterinary and Biomedical Sciences and Center for Molecular Toxicology and Carcinogenesis; Metabolomics Core Facility; Nuclear Magnetic Resonance Spectroscopy, The Pennsylvania State University, University Park, Pennsylvania; Ohio State University Comprehensive Cancer Center, Columbus, Ohio; and Department of Clinical Research, University of Bern, Bern, Switzerland.
² Authors' Affiliations: Laboratory of Molecular Immunogenomics, Genomic and Immunity Section, NIAMS/NIH; Laboratories of Human Carcinogenesis, and Metabolism, National Cancer Institute, NIH, Bethesda, Maryland; Department of Veterinary and Biomedical Sciences and Center for Molecular Toxicology and Carcinogenesis; Metabolomics Core Facility; Nuclear Magnetic Resonance Spectroscopy, The Pennsylvania State University, University Park, Pennsylvania; Ohio State University Comprehensive Cancer Center, Columbus, Ohio; and Department of Clinical Research, University of Bern, Bern, Switzerland.
³ Authors' Affiliations: Laboratory of Molecular Immunogenomics, Genomic and Immunity Section, NIAMS/NIH; Laboratories of Human Carcinogenesis, and Metabolism, National Cancer Institute, NIH, Bethesda, Maryland; Department of Veterinary and Biomedical Sciences and Center for Molecular Toxicology and Carcinogenesis; Metabolomics Core Facility; Nuclear Magnetic Resonance Spectroscopy, The Pennsylvania State University, University Park, Pennsylvania; Ohio State University Comprehensive Cancer Center, Columbus, Ohio; and Department of Clinical Research, University of Bern, Bern, Switzerland Curtis_Harris@nih.gov.

PMID: 24736543
PMCID: PMC4100625
DOI: 10.1158/0008-5472.CAN-14-0109

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Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer

Ewy A Mathé et al. Cancer Res. 2014.

. 2014 Jun 15;74(12):3259-70. doi: 10.1158/0008-5472.CAN-14-0109. Epub 2014 Apr 15. Authors Ewy A Mathé¹, Andrew D Patterson², Majda Haznadar², Soumen K Manna², Kristopher W Krausz², Elise D Bowman², Peter G Shields², Jeffrey R Idle², Philip B Smith², Katsuhiro Anami², Dickran G Kazandjian², Emmanuel Hatzakis², Frank J Gonzalez², Curtis C Harris³ Affiliations

¹ Authors' Affiliations: Laboratory of Molecular Immunogenomics, Genomic and Immunity Section, NIAMS/NIH; Laboratories of Human Carcinogenesis, and Metabolism, National Cancer Institute, NIH, Bethesda, Maryland; Department of Veterinary and Biomedical Sciences and Center for Molecular Toxicology and Carcinogenesis; Metabolomics Core Facility; Nuclear Magnetic Resonance Spectroscopy, The Pennsylvania State University, University Park, Pennsylvania; Ohio State University Comprehensive Cancer Center, Columbus, Ohio; and Department of Clinical Research, University of Bern, Bern, SwitzerlandAuthors' Affiliations: Laboratory of Molecular Immunogenomics, Genomic and Immunity Section, NIAMS/NIH; Laboratories of Human Carcinogenesis, and Metabolism, National Cancer Institute, NIH, Bethesda, Maryland; Department of Veterinary and Biomedical Sciences and Center for Molecular Toxicology and Carcinogenesis; Metabolomics Core Facility; Nuclear Magnetic Resonance Spectroscopy, The Pennsylvania State University, University Park, Pennsylvania; Ohio State University Comprehensive Cancer Center, Columbus, Ohio; and Department of Clinical Research, University of Bern, Bern, Switzerland.
² Authors' Affiliations: Laboratory of Molecular Immunogenomics, Genomic and Immunity Section, NIAMS/NIH; Laboratories of Human Carcinogenesis, and Metabolism, National Cancer Institute, NIH, Bethesda, Maryland; Department of Veterinary and Biomedical Sciences and Center for Molecular Toxicology and Carcinogenesis; Metabolomics Core Facility; Nuclear Magnetic Resonance Spectroscopy, The Pennsylvania State University, University Park, Pennsylvania; Ohio State University Comprehensive Cancer Center, Columbus, Ohio; and Department of Clinical Research, University of Bern, Bern, Switzerland.
³ Authors' Affiliations: Laboratory of Molecular Immunogenomics, Genomic and Immunity Section, NIAMS/NIH; Laboratories of Human Carcinogenesis, and Metabolism, National Cancer Institute, NIH, Bethesda, Maryland; Department of Veterinary and Biomedical Sciences and Center for Molecular Toxicology and Carcinogenesis; Metabolomics Core Facility; Nuclear Magnetic Resonance Spectroscopy, The Pennsylvania State University, University Park, Pennsylvania; Ohio State University Comprehensive Cancer Center, Columbus, Ohio; and Department of Clinical Research, University of Bern, Bern, Switzerland Curtis_Harris@nih.gov.

PMID: 24736543
PMCID: PMC4100625
DOI: 10.1158/0008-5472.CAN-14-0109

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Abstract

Lung cancer remains the most common cause of cancer deaths worldwide, yet there is currently a lack of diagnostic noninvasive biomarkers that could guide treatment decisions. Small molecules (<1,500 Da) were measured in urine collected from 469 patients with lung cancer and 536 population controls using unbiased liquid chromatography/mass spectrometry. Clinical putative diagnostic and prognostic biomarkers were validated by quantitation and normalized to creatinine levels at two different time points and further confirmed in an independent sample set, which comprises 80 cases and 78 population controls, with similar demographic and clinical characteristics when compared with the training set. Creatine riboside (IUPAC name: 2-{2-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-oxolan-2-yl]-1-methylcarbamimidamido}acetic acid), a novel molecule identified in this study, and N-acetylneuraminic acid (NANA) were each significantly (P < 0.00001) elevated in non-small cell lung cancer and associated with worse prognosis [HR = 1.81 (P = 0.0002), and 1.54 (P = 0.025), respectively]. Creatine riboside was the strongest classifier of lung cancer status in all and stage I-II cases, important for early detection, and also associated with worse prognosis in stage I-II lung cancer (HR = 1.71, P = 0.048). All measurements were highly reproducible with intraclass correlation coefficients ranging from 0.82 to 0.99. Both metabolites were significantly (P < 0.03) enriched in tumor tissue compared with adjacent nontumor tissue (N = 48), thus revealing their direct association with tumor metabolism. Creatine riboside and NANA may be robust urinary clinical metabolomic markers that are elevated in tumor tissue and associated with early lung cancer diagnosis and worse prognosis.

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Conflict of interest statement

The authors disclose no potential conflicts of interest.

Figures
Figure 1

Receiver Operating Characteristic (ROC) analysis…

Figure 1

Receiver Operating Characteristic (ROC) analysis of individual metabolites and their combination in the…

Figure 1

Receiver Operating Characteristic (ROC) analysis of individual metabolites and their combination in the training set in all cases (top), and in stage I–II (bottom) cases.

Figure 2

Kaplan-Meier survival estimates in the…

Figure 2

Kaplan-Meier survival estimates in the training set are depicted for the top four…

Figure 2

Kaplan-Meier survival estimates in the training set are depicted for the top four predictive metabolites in A) all lung cancer patients. The P values reported in the Kaplan-Meier plots reflect the maximum likelihood estimates generated using a univariate Cox model, taking into account left truncation (the lag time between diagnosis and time of urine collection). B) The combination of the top four predictive metabolites is shown for all cases. Only metabolites that showed statistically significant associations with survival, independent of clinical cofactors (see Materials and Methods), were combined. Metabolite levels were dichotomized into high and low based on the 75^th percentile of population controls abundances.

Figure 3

Abundance and validation of metabolites…

Figure 3

Abundance and validation of metabolites that were top contributors in the classification of…

Figure 3

Abundance and validation of metabolites that were top contributors in the classification of patients as lung cancer or healthy controls. Untargeted and MSTUS normalized UPLC-MS abundances (mean and standard error of the mean (SEM)) are depicted for A) the training set containing 469 lung cancer cases and 536 controls, B) the validation set comprising 80 cases and 78 controls. Quantitated UPLC-MS/MS abundances (mean and SEM) in C) a subset of the training set containing 92 cases and 106 controls. FC=fold change

Figure 4

Linking urinary metabolites to lung…

Figure 4

Linking urinary metabolites to lung cancer tissue metabolome. A) Levels of creatine riboside,…

Figure 4

Linking urinary metabolites to lung cancer tissue metabolome. A) Levels of creatine riboside, N-acetylneuraminic acid and creatine in a paired tumor/adjacent non-tumor tissue set containing 48 stage I adenocarcinoma and squamous cell carcinoma tumors and 48 adjacent non-tumor samples. B) Correlation between creatine riboside and creatine quantitated in tumor tissue samples.