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Shantanu Banerji

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    MA17 - Molecular Mechanisms and Therapies (ID 143)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Biology
    • Presentations: 1
    • Now Available
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      MA17.12 - Discussant - MA17.09, MA17.10, MA17.11 (Now Available) (ID 3788)

      15:45 - 17:15  |  Presenting Author(s): Shantanu Banerji

      • Abstract
      • Presentation
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      Abstract not provided

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    P2.11 - Screening and Early Detection (ID 178)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Screening and Early Detection
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.11-10 - Discovery of Potential Biomarkers That Discriminate Early Stage NSCLC from Controls by Non-Targeted Metabolomics Profiling (ID 1857)

      10:15 - 18:15  |  Author(s): Shantanu Banerji

      • Abstract


      Detection of NSCLC at the early stage is a potential means to reduce mortality and morbidity of lung cancer. Development of an accurate, non-invasive, economical, and safe test to detect early stage NSCLC remains a challenge. We explored metabolomics profiling of plasma to discriminate early stage NSCLC cases from Cancer-Free Controls (CFC).


      Frozen plasma samples collected from 2004 to 2014 from 250 patients with clinical early stage NSCLC (drawn prior to surgical resection) and 250 CFCs were obtained from a provincial biorepository. Samples were thawed, extracted, and analyzed in duplicate by blinded laboratory personnel using non-targeted Ultra High Performance Liquid Chromatography/Quadrupole Time-Of-Flight Mass Spectrometry (UHPLC-QTOF-MS). Individual metabolic entities were identified and quantified using Mass Profiler Professional Software (Agilent Technologies, CA, USA). Analysis was restricted to known human metabolites identified by the Metlin and Human Metabolome databases. Candidate metabolites quantified in less than 20% of samples were dropped; missing values were replaced with one-half of the smallest measurement for each metabolite. Final candidate metabolites were screened for differential abundance (DA) between NSCLC cases and CFCs using: (1) False discovery rate (FDR)-adjusted p-values less than 1% after controlling for age, sex and smoking status in linear regression; (2) <1% change in DA due to covariates; (3) up-regulation in NSCLC.


      Of the 250 NSCLC Cases, 185 (74%) had adenocarcinoma, 65 (26%) had Squamous Cell Carcinoma; 204 (81.6%) had pathological Stage I/II disease (AJCC 7th ed) and 46 (18.4%) had stage III/IV disease. Median age was 70 (range 46-88) in NSCLC cases and 56 (20-89) in CFCs (p<0.001), and NSCLC cases had more males compared to CFCs (46.4% vs 31.2%, p <0.001). NSCLC patients had a higher proportion of current (27.2% vs 6%) or ex-smokers (64.8% vs 20.8%) compared to CFCs (p<0.001).

      A total of 1,209 known human metabolites were detected using UHPLC-QTOF-MS technique, of which 676 were present in a minimum of 80% of all samples and were used for modeling. Table 1 lists candidate metabolomics biomarkers strongly upregulated in NSCLC cases versus CFCs which were unaffected by covariates of age, sex, and smoking. A multiple logistic regression model using the top 3 metabolites correctly classified NSCLC case from CFC with an overall accuracy of 93.6% and an area under the curve of 0.975.

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      Metabolomics profiling of plasma represents a potential means to distinguish NSCLC cases from CFCs. Further targeted metabolomics analyses of specific classes of metabolites in larger cohorts are warranted.