Article type: Research Article
Authors: Fahrmann, Johannes F.a | Grapov, Dmitryb | DeFelice, Brian C.a | Taylor, Sandrac | Kim, Kyoungmic | Kelly, Karend | Wikoff, William R.a | Pass, Harveye | Rom, William N.f | Fiehn, Olivera; g | Miyamoto, Suzanned; *
Affiliations: [a] University of California, Davis Genome Center, Davis, California, CA, USA | [b] CDS Creative Data Solutions, Ballwin, MO, USA | [c] Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, California, CA, USA | [d] Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, CA, USA | [e] Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Langone Medical Center, New York University, New York, NY, USA | [f] Division of Pulmonary, Critical Care, and Sleep, NYU School of Medicine, New York, NY, USA | [g] Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia
Correspondence:
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Corresponding author: Suzanne Miyamoto, Division of Hematology/Oncology, UC Davis Cancer Center, 4501 X Street, Room 3016, Sacramento, CA 95817, USA. Tel.: +1 916 734 3769; Fax: +1 916 734 5415; E-mail:smiyamoto@ucdavis.edu
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.
Keywords: Lung cancer, metabolomics, phospholipids, biomarkers, solitary pulmonary nodules
DOI: 10.3233/CBM-160602
Journal: Cancer Biomarkers, vol. 16, no. 4, pp. 609-617, 2016
