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R. Mjelle

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    P3.02 - Biology/Pathology (ID 620)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Biology/Pathology
    • Presentations: 1
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      P3.02-097a - Metabolic Biomarkers in Serum for the Early Diagnosis of Lung Cancer: First Results from the Cancer-Biomarkers in HUNT Initiative (ID 9792)

      09:30 - 16:00  |  Author(s): R. Mjelle

      • Abstract
      • Slides

      To date there are no clinical biomarkers for the early diagnosis lung cancer. The Cancer-Biomarkers in HUNT initiative analyses serum samples collected two months to five years before diagnosis (prospective HUNT study, Trondheim, Norway) for identifying metabolomics signatures for the early detection of lung cancers.

      Thirty-six serum samples of individuals that subsequently developed adenocarcinoma (n=12), squamous cell carcinoma (n=12) and small-cell lung cancer (n=12) were profiled with LC-MS untargeted (Amide-) metabolites (n = 1042), along with 36 sera from individuals that were cancer-free 5 years after blood sampling matched for smoking status, gender and age. Each cancer subtype as well as adeno plus squamous (non-small cell lung cancer) was contrasted against its respective controls as well as . For each contrast, the moderated t-test implemented in the R package limma was used for performing univariate analysis, while multivariate analyses were performed using the Just Add Data software (Gnosis Data Analysis), which implements a data-analysis pipeline comprehensive of feature selection, non-linear modelers (e.g., Random Forests) and cross-validation with bootstrapping for optimizing algorithms and providing unbiased performance estimation.

      Two non-overlapping signatures, each containing four metabolites were identified by the non-linear data analysis pipeline, the first discriminating adeno patients (AUC 0.71, CI = [0.52, 0.9]) (Figure 1) and the second discriminating adeno and squamous cases from their respective controls (AUC = 0.643, CI = [0.452, 0.803]). No association between metabolites and cancer was identified by the univariate analyses at FDR level 0.1.

      The results suggest that metabolic information in serum may help in detecting lung cancer two months to five years prior to clinical lung cancer diagnosis. This is the first large-scale untargeted metabolomics screening of pre-diagnostic serum of future lung cancer patients. Further studies are in progress for validation of these findings Figure 1

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