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jing Lin



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    P2.11 - Screening and Early Detection (ID 178)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Screening and Early Detection
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.11-31 - DNA Methylation Markers for Prediction of Recurrence in Stage I Non-Small Cell Lung Cancers (Now Available) (ID 2505)

      10:15 - 18:15  |  Author(s): jing Lin

      • Abstract
      • Slides

      Background

      Surgery with curative intent is the standard of care for patients with stage I non-small cell lung cancer (NSCLC). However, many patients die of recurrent disease despite of lesion resection. The value of DNA methylation for predicting the recurrence of early-stage, resected NSCLC remains to be determined. The aim of this study was to find DNA methylation markers for recurrence prediction.

      Method

      39 paired tumor tissues and adjacent normal tissues from stage Ia NSCLCs were sequenced using bisulfite sequencing panel which covers 80,672 CpG sites and spans 1.05 mega base of human genome. The average sequencing depth was 1000X. Methylation blocks (MBs) were defined as the genomic region between the neighboring CpG sites and 8312 MBs were generated using the linkage disequilibrium and statistical modeling. Methylmean indicates the mean methylation value of MB, and methyentropy denotes the randomness of DNA methylation pattern of MB.

      Result

      A total of 726 tumor-specific MBs shared by 1098 Methylentropy and 1316 Methylmean variates were obtained from matched tumor tissues and adjacent normal tissues using t-test (P<0.05). Then the multivariable analysis, conducted via the Cox regression model, generated 15 significant disease-free survival (DFS)-related MBs which shared by 56 methylentropy and 46 methylmean variates. A final model was selected using a backward step-down process and 6 significant DFS-related MBs were selected (1 methylentropy and 5 methylmean variates). A nomogram model that incorporated the 6 MBs was established to predict the DFS, and it performed well (C index=0.729, Figure 1).

      figure 1. dfs_specific_nomogram.png

      Conclusion

      We established an effective nomogram that may well distinguish the potential subgroup of patients with different DFS based on DNA methylation. Further perspective study should be conducted to validate this nomogram model in larger cohorts.

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