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Summer S Han

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    P1.11 - Screening and Early Detection (Not CME Accredited Session) (ID 943)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/24/2018, 16:45 - 18:00, Exhibit Hall
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      P1.11-05 - Metabolomic Profiling for Second Primary Lung Cancer Among Lung Cancer Survivors (ID 13995)

      16:45 - 18:00  |  Presenting Author(s): Summer S Han

      • Abstract


      Survivors of lung cancer(LC) have a high risk of developing second primary lung cancer(SPLC), the incidence of which is 4-6 times higher than that of initial primary lung cancer(IPLC). While national lung screening guidelines have been established for IPLC, no consensus guidelines exist for LC survivors. Furthermore, the factors that contribute to SPLC risk have not yet been established. The purpose of this study is to examine the potential of metabolomics to identify non-invasive blood-based biomarkers for SPLC screening.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      We applied an untargeted metabolomics approach based on a liquid chromatography-tandem mass spectroscopy(UPLC-MS/MS) method to discover metabolic biomarkers using blood serum samples from the Boston Lung Cancer Study. Our study cohort consisted of 177 subjects diagnosed with IPLC between 1992 and 2012 and who survived >=5 years after the initial diagnosis. The cohort included 82 SPLC cases and 95 matched controls (i.e. IPLC patients without SPLC as of Dec, 2017) based on the age of initial diagnosis, sex, race, and smoking status. We applied random forest and Welch’s t-test to identify metabolomic features associated with SPLC risk and to build a risk prediction model.

      4c3880bb027f159e801041b1021e88e8 Result

      Our analysis detected 1008 named and 316 unnamed metabolites. The metabolites that were statistically significantly associated with SPLC risk (False Discovery Rate q-value<0.05) included 5-methylthioadenosine (MTA), phenylacetylglutamine, and umbelliferone sulfate, which showed 1.4-3.8 fold increases among SPLC cases versus controls (Figure 1). These metabolites were involved in amino acid, peptide, and xenobiotics pathways. The stratification by quintiles of estimated risk using the prediction model based on the metabolites showed that the observed incidence of SPLC was significantly higher in the fifth quintile(69.4%) versus the first-quintile(36.1%;P<0.05).


      8eea62084ca7e541d918e823422bd82e Conclusion

      We identified potential metabolic biomarkers for SPLC among LC survivors. A risk-stratification approach based on metabolic biomarkers can be potentially useful for identifying high-risk LC survivors to be screened by CT.