Author of

• 16637

  +

  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)

  • 16639

    +

    P1.03-002 - Can We Discriminate between Different Subtypes of Non-Small Cell Cancer Patients Based on Plasma Metabolic Fingerprint? (ID 4598)

    14:30 - 14:30  |  Author(s): M. Ciborowski
    • Abstract

    Background:
    Progress in understanding the pathogenesis of non-small cell lung cancer (NSCLC) led to the development of molecularly targeted agents, including those targeting selected growth factors: vascular endothelial (VEGF), platelet-derived (PDGF), epidermal (EGF), insulin-like I (IGF-I), and anaplastic lymphoma kinase (ALK) signaling. Interestingly, clinical trials of targeted agents and newer chemotherapy agents yielded differences in outcomes according to histologic subgroups providing a rationale for histology-based treatment approaches. Consequently, a correct histologic diagnosis is becoming increasingly important. However, even the most careful examination of biopsies by expert pulmonary pathologists leave about 10-30% of NSCLC as not specified. Metabolomics is widely used for biomarkers discovery and patients stratification. This novel tool may provide additional diagnostic markers, which could support proper NSCLC subtyping. In the present study plasma samples obtained from patients with the major NSCLC histologic subtypes and controls were fingerprinted by liquid chromatography - mass spectrometry (LC-MS) method.
    
    Methods:
    The study was performed on the group of 63 NSCLC patients and 32 controls. Based on the histology evidence the patients were classified as ADC (n=20), LCC (n=13), and SCC (n=30). Individuals in all studied groups were matched in age (62±10 years), sex (15-38%F) and BMI (26±3). Metabolites extracted from plasma were analyzed by use of LC-QTOF-MS in positive and negative ion modes. Metabolic features repetitively measured in QC samples (RSD<20%) and present in at least 90% of the samples were forwarded to statistical analysis. Depending on data distribution t-test or U-test were used to select metabolites significantly different between controls and NSCLC patients. ANOVA was used to select metabolites discriminating between NSCLC subtypes. Multivariate analysis was used to show subtype-related patients’ classification.
    
    Results:
    Identification of plasma metabolites significantly different between controls and NSCLC showed differences in phospholipids (e.g. PC 34:3, +53% in NSCLC, p=0.01; PC 36:4, +50% in NSCLC, p=0.001; Lyso PC 18:3, +37% in NSCLC, p=0.02; PE 34:2, +36% in NSCLC, p=0.0002) and docosahexaenoic fatty acid (-34% in NSCLC, p=0.02). Based on metabolites significant after ANOVA analysis it was possible to build good quality (R[2]=0.652, Q[2]=0.408) partial least square discriminant analysis (PLS-DA) model separating NSCLC subtypes. Among metabolites responsible for this separation sphinganine (p=0.009), anandamide (p=0.009), malonyl carnitine (p=0.001), and Lyso PE 20:5 (p=0.02) can be mentioned.
    
    Conclusion:
    Metabolic fingerprinting of plasma samples allowed for selection a panel of metabolites able to discriminate between NSCLC subtypes. Although promising, obtained results require further validation with target methods and on larger cohort of patients. Acknowledgements: The study was funded by National Science Centre, Poland (2014/13/B/NZ5/01256).