Author of

• 16751

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  P1.05 - Poster Session with Presenters Present (ID 457)

  • Event: WCLC 2016
  • Type: Poster Presenters Present
  • Track: Early Stage NSCLC
  • Presentations: 1
  • Moderators:
  • Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)

  • 16757

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    P1.05-006 - Identification of miRNAs and mRNAs Associated with Metastasis in Early-Stage Non-Small Cell Lung Cancer (NSCLC) (ID 5829)

    14:30 - 14:30  |  Author(s): N. Pham
    • Abstract

    Background:
    Early-stage NSCLC patients whose tumours can form primary xenografts (XG) in immune deficient mice have significantly shorter disease-free survival and are at a greater risk of early metastasis compared with patients whose tumours do not form xenografts (non-XG). Genomic and proteomic characterization of XG and non-XG-forming primary patient tumours may reveal clinically relevant genetic aberrations that are associated with early metastasis.
    
    Methods:
    miRNA-seq and RNA-seq data of 100 early-stage NSCLC patients with known engraftment status were acquired. The cohort includes 62% adenocarcinoma (ADC) and 38% squamous cell carcinoma (SQCC). Least absolute shrinkage and selection operator (LASSO) was applied to identify features associated with XG status using integrated miRNA and mRNA abundance profiles. Gene Ontology (GO) annotation was subsequently performed to elucidate biological processes that may be altered between the two patient groups.
    
    Results:
    Using miRNA and mRNA data alone, ADC patients were classified as XG and non-XG with 88.7% and 95.2% accuracy. The integration of these two data types classified the patients with 100% accuracy using 20 features (7 miRNAs and 13 mRNAs). While less is known regarding the roles of the identified miRNAs in lung ADC, several of the genes have been suggested to affect the metastatic ability of lung cancer cells; these include PITX1, GPNMB and KRT14. In SQCC, both the miRNA and mRNA data alone and the integrated profiles were able to classify patients into XG and non-XG-forming groups with 100% accuracy. However, the roles of the selected features (1 miRNA and 11 mRNAs) in the metastasis of SQCC are not well defined. GO annotation of the identified mRNAs in ADC revealed enrichment of biological processes related to B cell differentiation, wound healing and regulation of the immune response and signalling pathway, while catabolic and metabolic processes were enriched in SQ.
    
    Conclusion:
    The use of single-dimensional data to classify patients into different prognostic groups may not be sufficient in the presence of heterogeneous patient populations. Integrative analysis of multi-omic data can provide greater insights into clinically relevant genetic aberrations, which can be used to improve the molecular classification of NSCLC.