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Alessia Di Gilio



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    MA10 - Emerging Technologies for Lung Cancer Detection (ID 129)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Screening and Early Detection
    • Presentations: 1
    • Now Available
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      MA10.05 - Breath Analysis: New Key-Challenges for Early Detection of Lung and Pleural Neoplasms (Now Available) (ID 959)

      15:15 - 16:45  |  Author(s): Alessia Di Gilio

      • Abstract
      • Presentation
      • Slides

      Background

      The growing interest about breath analysis relies on the need of tools to get an early diagnosis of respiratory pathologies with high mortality rate such as lung cancer (LC) and malignant pleural mesothelioma (MPM). Nowadays the key-challenge of the scientific community is the search for non-invasive diagnostic biomarkers able to identify patients at risk of developing cancer or with early stage cancer. A diagnostic progress would be crucial to improve the survival outcome of these neoplasms, generally detected at an advanced stage. The analysis of Volatile Organic Compounds (VOCs) pattern in human breath for early detection and follow-up of diseases such as cancer is low-cost, non-invasive and promising alternative to traditional exams (i.e., colonoscopy, biopsy).

      Method

      This study is based on the development and validation of a methodological approach aimed to the identification of VOCs breath pattern to discriminate between patients affected by both LC and MPM, and healthy controls (CTRL). A total of 80 breath samples from 36 patients with LC, 14 patients with MPM and 30 CTRL have been collected into inert Tedlar bags, transferred to sorbent tubes (biomonitoring, Markes) and analysed by TD-GC/MS (TD Markes Unity 2 - GC Agilent 7890/MS Agilent 5975).

      Result

      Non parametric test as Wilcoxon/Kruskal Wallis tests (R version 3.5.1) allowed to identify the most weighting variables in discrimination between LC, MPM and HC breath samples. On the basis of p-values lower than 0.05 (selection between CTRL and LC, and between CTRL and MPM) and current knowledge on metabolic processes, a multivariate statistics (Principal Components Analyses (PCA) -PAST 3.20) has been applied on breath samples, considering only selected variables. The preliminary statistical elaboration by PCA of data collected from the analysis of LC and CTRL samples have shown two principal components: PC1 characterized by higher loadings of benzoic acid, methylcyclohexane and hexanal, and PC2 characterized by high loadings for dimethyldecane, pentane and pentanal. Similar results were obtained by PCA applied to MPM and CTRL breath samples considering 2-methylpentane, cyclopentane, hexane and 2-butanone as discriminant variables.

      Conclusion

      PCA was able to discriminate between LC and CTRL and between MPM and CTRL breath samples. Leave-one-out cross-validation method was applied to calculate the prediction accuracy obtaining good sensitivity (88%), accuracy (86%) and specificity (92%).

      Further investigation about breath analysis is strongly warranted, due to the need of biomarkers potentially useful both for the screening of high-risk subjects and for the early diagnosis of lung and pleural neoplasms.

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