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  P3.02 - Poster Session 3 - Novel Cancer Genes and Pathways (ID 149)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Biology
  • Presentations: 1
  • Moderators:
  • Coordinates: 10/30/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

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    P3.02-002 - Identification of CpG island methylator phenotype predicts the prognosis of small cell lung cancer (ID 42)

    09:30 - 09:30  |  Author(s): H. Aburatani
    • Abstract

    Background
    Small cell lung cancer (SCLC) accounts for 13-15% of new lung cancer cases in worldwide and has the poor therapeutic outcomes with a median survival of just over one year. A CpG island methylate phenotype (CIMP) is well known as a methylator phenotype with characteristic promoter DNA methylation alterations, in colorectal cancers, glioblastoma and breast cancers, although there has been no report about any CIMP of SCLC. We investigated whether DNA methylation profiles can provide useful molecular subtyping of SCLC in terms of etiology and prognosis of SCLC.

    Methods
    We analyzed 28 fresh frozen samples from pure SCLC patients and 13 noncancerous lung tissues. All patients underwent surgical lung resection at the Cancer Institute Hospital, nine patients among them were treated with chemotherapy before surgery. After genomic DNA was treated with sodium bisulfite, bisulfite-converted genomic DNA was analyzed using Illumina’s Infinium HumanMethylation27 BeadChip. And, total RNA was extracted from twenty-five SCLC tumor samples and mRNA expression of these samples were analyzed by Agilent’s SurePrint G3 Human CGH Microarray. Next, we matched these two data sets by Gene Symbol, and identified fifty-five most differentially methylated CpG sites (corresponding to 46 genes) with a FDR p value cut off of 0.05. Gene ontology analysis was performed using DAVID Bioinformatics Resources.

    Results
    We selected a total of 1741 most differentially methylated CpG sites (s.d. > 0.20) across the 28 SCLC tumor tissues in each DNA methylation platform, after an elimination of the probes related with the X- and Y- chromosome. Unsupervised hierarchical clustering of methylation data from SCLC samples reveals two major subgroups with different prognosis: the 5 years disease-free interval (DFI) rate of patients in cluster 1 (11.1%) was lower than that of patients in cluster 2 (61.57%) (p = 0.001). By multivariate analysis for DFI, both postoperative chemotherapy and cluster 1 were a significant prognostic factor (p = 0.002 and 0.002; respectively). Next, among 1220 genes with methylation and expression data both available, the CpG sites were ranked on the basis of the spearman’s correlation coefficient between cluster 1 and cluster 2 into an ascending order. Finally, we identified that fifty-five CpG sites were nagetively correlated and found that apoptosis pathway was a most differentially expressed.

    Conclusion
    By comprehensive DNA methylation profiling, two distinct subgroups with different molecular and clinical phenotype were identified to evoke a CIMP of SCLC. We found some promoter markers in the apoptosis pathway could make a difference between the two groups, and we hope that our data can contribute to provide a useful resource for the construction of therapeutic strategy and the development of a new chemotherapeutic agent.