Johannes F Fahrmann1, Dmitry Grapov2, Brian C DeFelice1, Sandra Taylor3, Kyoungmi Kim3, Karen Kelly4, William R Wikoff1, Harvey Pass5, William N Rom6, Oliver Fiehn1,7, Suzanne Miyamoto4. 1. University of California, Davis Genome Center, Davis, California, CA, USA. 2. CDS Creative Data Solutions, Ballwin, MO, USA. 3. Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, California, CA, USA. 4. Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, CA, USA. 5. Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Langone Medical Center, New York University, New York, NY, USA. 6. Division of Pulmonary, Critical Care, and Sleep, NYU School of Medicine, New York, NY, USA. 7. Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia.
Abstract
BACKGROUND: Recent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable. OBJECTIVE: Identify blood-based metabolite biomarkers for diagnosing lung cancer. MEHTODS: Serum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis (pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29). RESULTS: Univariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively. CONCLUSIONS: This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.
BACKGROUND: Recent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable. OBJECTIVE: Identify blood-based metabolite biomarkers for diagnosing lung cancer. MEHTODS: Serum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis (pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29). RESULTS: Univariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively. CONCLUSIONS: This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.
Authors: Alexander Triebl; Jürgen Hartler; Martin Trötzmüller; Harald C Köfeler Journal: Biochim Biophys Acta Mol Cell Biol Lipids Date: 2017-03-22 Impact factor: 4.698