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S.Y. Lee
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P1.03 - Poster Session with Presenters Present (ID 455)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.03-005 - High Resolution Metabolomics in Discovering Plasma Biomarkers of Lung Cancer Patients with EGFR Common Mutations (Exon 19 or 21) (ID 4978)
14:30 - 15:45 | Author(s): S.Y. Lee
- Abstract
Background:
The epidermal growth factor receptor (EGFR) is a key target in the treatment of advanced non-small cell lung cancer (NSCLC). The presence of EGFR mutations predicts the sensitivity to EGFR tyrosine kinase inhibitors (TKIs) of NSCLC patients. For this reason, EGFR mutation test is required to provide personalized treatment options. Currently available DNA sources are mainly small biopsies and cytological samples, providing limited and low-quality material. So, there is the need of new biomarker discovery. Recently, metabolomics, which is the comprehensive study of low molecular weight metabolites, has also been developed. Integration of transcriptomic and metabolomic data has enabled deeper analysis of chemosensitive pathways. In this regard, this study aims to apply high resolution metabolomics (HRM) to detect significant compounds that might contribute in inducing EGFR mutations.
Methods:
Plasma samples from 2 healthy volunteers and 15 lung cancer patients were analyzed to detect biomarkers which can predict EGFR mutations. The comparison was made between healthy control and lung cancer groups for metabolic differences. Ten patients had EGFR mutations and five patients had wild type EGFR (n=5) in tumor tissue. The EGFR mutation group was divided into exon 21 deletion group (n=6), and exon 19 deletion group (n=4). Differences in metabolic profiles of EGFR mutation lung cancer populations and EGFR wild type lung cancer patients were examined. Metabolites were separated using Agilent 1200 High Performance Liquid Chromatography and Agilent 6530 quadrupole time-of-flight LC/MS.
Results:
From 216 metabolites, 112 metabolites were found to be significantly different. Relative concentration of L-valine, linoleate, tetradecanoyl carnitine, 5-MTHF, and N-succinyl-L-Glutamate-5 semialdehyde showed significant difference (p<0.05). Linoleic acid was elevated in mutation group while tetradecanoyl carnitine was found to be lowered in mutation group. These two compounds are related to the alteration of mitochondrial energy metabolism (carnitine shuttle). Compounds related to amino acid metabolism, L-valine and N-succinyl-L-Glutamate-5 semi aldehyde were increased in mutation group.
Conclusion:
Our results show that HRM with the combination of pathway analysis from significant metabolites was able to discriminate an EGFR mutation positive patients (exon 19 or exon 21) from wild type advanced NSCLC patients. Therefore, high resolution metabolomics can be the potential non-invasive tool to utilize clinically to detect the EGFR mutations in NSCLC patients.
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P1.05 - Poster Session with Presenters Present (ID 457)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Early Stage NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.05-026 - High Resolution Metabolomics on Exhaled Breath Condensate to Discover Lung Cancer's Biomarker (ID 5617)
14:30 - 15:45 | Author(s): S.Y. Lee
- Abstract
Background:
Early and non-invasive detection of lung cancer is a desirable prognostic tool for prevention of lung cancer at early stages. Previously, unusual human breath smell of lung cancer patients detected by trained dogs played an important role in early detection of lung cancer. Which suggests that exhaled breath condensate (EBC) is a promising source for searching potential biomarkers in lung cancer patient.
Methods:
EBC sample collected using specific device called R-tube, containing both the volatile organic compounds (VOCs) and non-volatile organic compounds (NVOCs), were obtained from patients with lung cancer (n = 20) and control healthy individuals (n = 5). The EBC samples were applied to high resolution metabolomics (HRM) based LC-MS for comparison of metabolic differences among healthy people and lung cancer patients in order to detect potential biomarkers. The multivariate statistical analysis was performed, including a false discovery rate (FDR) of q=0.05, to determine the significant metabolites between the groups. 2-way hierarchical clustering analysis (HCA) was done for determining the classification of significant features between the control healthy and lung cancer patients. The significant features were annotated using Metlin database (metlin.scripps.edu/) and the identified features were then mapped on the human metabolic pathway of the Kyoto Encyclopedia of Genes and Genomes (KEGG). This study was approved by Korea University Guro Hosipital Institutional review board (KUGH14273)
Results:
Using metablomics-wide associated study (MWAS), metabolic changes among healthy group and lung cancer patients were determined. The 2-way HCA identified the different metabolic profile in lung cancer patients from healthy control. The identified potential biomarkers are Acetophenone (m/z 103.0542, [M+H-H~2~O][+]), P-tolualdehyde (m/z 138.0914, [M+NH4][+]), 2,4,6-Trichlorophenol (m/z 218.9134, [M+Na][+]) and 11(R)-HETE (m/z 343.2233, [M+Na][+]). The top 5 of affected KEGG pathways are Arachidonic acid metabolism, Glycerophospholipid metabolism, Bile secretion, Inflammatory mediator regulation of TRP channels and Tyrosine metabolism.
Conclusion:
Our result shows that Acetophenone, P-tolualdehyde, 2,4,6-Trichlorophenol and 11(R)-HETE are significantly higher in lung cancer patients. Acetophenone and 2,4,6-Trichlorophenol are classified as a Group D human carcinogen approved by US Environmental Protection Agency (EPA), while 11(R)-HETE is associated with Arachidonic acid metabolism and P-tolualdehyde is related to xylene degradation pathway and degradation of aromatic compounds pathway. Our identified metabolites can be the potential biomarkers in EBC for the early and non-invasive detection of lung cancer.
