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H. Zhu

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    CALC - Chinese Alliance Against Lung Cancer Session (ID 79)

    • Event: WCLC 2013
    • Type: Other Sessions
    • Track: Other Topics
    • Presentations: 1
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      CALC.11 - MicroRNA Biomarkers in Lung Cancer (ID 3878)

      07:30 - 12:00  |  Author(s): H. Zhu

      • Abstract
      • Slides

      ABSTRACT Rationale: Effective treatment for lung cancer requires accuracy in sub-classification of carcinoma subtypes. Objectives: To identify microRNAs in bronchial brushing specimens for discriminating small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) and for further differentiating squamous cell carcinoma (SQ) from adenocarcinoma (AC). Methods: Microarrays were used to screen 723 microRNAs in laser-captured, microdissected cancer cells from 82 snap-frozen surgical lung tissues. Quantitative reverse-transcriptase PCR was performed on 153 macrodissected formalin-fixed, paraffin-embedded (FFPE) surgical lung tissues to evaluate 7 microRNA candidates discovered from microarrays. Two microRNA panels were constructed based on a training cohort (n = 85) and validated using an independent cohort (n = 68). The microRNA panels were applied as differentiators of SCLC from NSCLC and SQ from AC in 207 bronchial brushing specimens. Measurements and Main Results: Two microRNA panels yielded high diagnostic accuracy in discriminating SCLC from NSCLC (miR-29a and miR-375, AUC 0.991 and 0.982 for training and validation dataset, respectively) and in differentiating SQ from AC (miR-205 and miR-34a, AUC 0.977 and 0.982 for training and validation dataset, respectively) in FFPE surgical lung tissues. Moreover, the microRNA panels accurately differentiated SCLC from NSCLC (AUC 0.947) and SQ from AC (AUC 0.962) in bronchial brushing specimens. Conclusion: We found 2 microRNA panels that accurately discriminated between the 3 subtypes of lung carcinoma in bronchial brushing specimens. The microRNA panels could have considerable clinical value in differential diagnosis and play an important role in determining optimal treatment strategies based on the lung carcinoma subtype.

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