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Lele Zhang



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    P2.11 - Screening and Early Detection (Not CME Accredited Session) (ID 960)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall
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      P2.11-01 - Blood Transcriptomics Enables Detection of Pre-Invasive & Minimally-Invasive Lung Adenocarcinoma (ID 12959)

      16:45 - 18:00  |  Author(s): Lele Zhang

      • Abstract
      • Slides

      Background

      Although the low-dose computed tomography scan has been proved a useful tool for lung cancer screening, its highly false positive rate that usually over 90% limits its effectiveness for early detection of lung cancer. There is an urgent need to develop a non-invasive and cost-effective method to detect lung cancer at early stage.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Peripheral blood samples were collected from patients pathologically diagnosed pre-invasive or minimally-invasive lung adenocarcinomas and healthy volunteers who reported with no any pulmonary disorders. Total mRNA from peripheral whole blood was processed according to PAXgene Blood RNA Kit protocol. In this study, we compared blood gene expression data from 95 samples using microarray analysis. Quantitative PCR was then used to validate biomarker candidates identified by differential expression analysis in microarray hybridization (N = 251). The gene panel finally selected was validated in an independent population (N = 54) using quantitative PCR. Logistic regression was performed on multiple combinations of common probe sets, and data were evaluated in terms of discrimination by computing the area under the receiving operator characteristic curve.

      4c3880bb027f159e801041b1021e88e8 Result

      The lung cancer-specific gene signatures were identified to construct predictive model based on 6-gene panel such as HSP90AA1, UQCRQ, NDUFB2, RPL24, CKLF and GLRX, which correctly classified 29 of 39 pre-invasive or minimally-invasive lung adenocarcinomas, 30 of 38 health controls with 76.6% accuracy in training set, and 7 of 8 lung cancer, 8 of 10 health controls with 83.3% accuracy in test set. Validation by quantitative PCR confirmed the Affymetrix microarray data, with 75.7% accuracy, 75.0% sensitivity, 76..6% specificity, 0.83 of area under curve (AUC) in training set, and 91.9% accuracy, 91.7% sensitivity, 92.3% specificity, 0.94 of AUC in test set. Independent validation testing confirmed these specific gene signatures did not derived from the result of random chance with 83.3% accuracy, 84.6% sensitivity and 79.3% specificity.

      8eea62084ca7e541d918e823422bd82e Conclusion

      Our results indicated the feasibility of blood-based genetic signatures to identify pre-invasive and minimally-invasive lung adenocarcinoma as screening for lung cancer at very early stage.

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