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G. Qing
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P2.01 - Poster Session with Presenters Present (ID 461)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Biology/Pathology
- Presentations: 1
- Moderators:
- Coordinates: 12/06/2016, 14:30 - 15:45, Hall B (Poster Area)
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P2.01-038 - Discrimination of NSCLC Cases from Cancer-Free Controls and Adenocarcinoma from Squamous Cell Carcinoma Using Plasma Metabolomics Profiles (ID 5838)
14:30 - 14:30 | Author(s): G. Qing
- Abstract
Background:
Non-diagnostic bronchoscopic or image guided biopsies of small, solitary pulmonary nodules are a common and frustrating clinical conundrum which can cause thoracic oncologists and patients to opt for radical therapy without a pathological diagnosis. As a result, some patients with benign conditions have unnecessarily undergone radical treatment. This study sought to determine if metabolomics profiling of plasma could be used to distinguish early stage NSCLC cases from cancer-free controls. Furthermore, we sought to determine if phenotypic subtypes of NSCLC could be distinguished from one another using metabolomics profiling.
Methods:
Frozen preoperative plasma samples from a cohort of 48 early stage NSCLC patients (24 Adenocarcinomas, 24 Squamous Cell Carcinomas) and 24 cancer-free controls obtained prior to surgical resection were randomly selected from a provincial lung cancer biorepository for metabolomics analysis. Plasma samples were uniformly thawed, extracted, and analyzed in triplicate by blinded personnel using ultra high performance liquid chromatography/quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS). After data mining, metabolomics profiles were quantified and normalized using Mass Profiler Professional (Agilent Technologies, CA, USA) and individual metabolites were identified using the Metlin Database. Partial least square discrimination (PLSD) was used as a prediction model to identify metabolomics profiles which effectively clustered NSCLC cases from Controls and Adenocarcinomas from Squamous Cell Carcinomas.
Results:
More than 17,500 entities were detected using the UHPLC-QTOF-MS technique of which 250 potential metabolomics biomarkers were present in all 72 samples. The PLSD analysis using all detected entities effectively distinguished NSCLC cases from controls with 97% overall accuracy. Several endogenous metabolites were statistically significantly affected in Adenocarcinoma and Squamous Cell Carcinomas cases compared to control samples. Some of the identified compounds include biliverdin IX, serotonin, PE(15:0/20:2) and 3-ketosphingasine. In addition, 3-acetamidopropanal, 9,10-dihydroxy-hexadecanoic acid and anandamide (20:5, n-3) were found in high concentrations in Adenocarcinoma cases compared to Squamous Cell Carcinomas.
Conclusion:
Differences in the metabolomics profiles were apparent and demonstrated the preliminary feasibility of distinguishing early stage NSCLC cases from controls and adenocarcinomas from squamous cell carcinomas using metabolomics techniques. Validation of this methodology on a larger, prospectively accrued, cohort is planned in order to optimize model fit and to assess the diagnostic and NSCLC subtype discriminatory performance in the clinical setting.
