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Khulan Batbayar



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    P58 - Tumor Biology and Systems Biology - Basic and Translational Science - Epigenomics (ID 196)

    • Event: WCLC 2020
    • Type: Posters
    • Track: Tumor Biology and Systems Biology - Basic and Translational Science
    • Presentations: 1
    • Moderators:
    • Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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      P58.02 - Bronchial Field Progenitor Basal Cells Show Methylome-Wide Characteristics Reflective of Lung Cancer Case-Control, Age, and Smoking Status (ID 3705)

      00:00 - 00:00  |  Presenting Author(s): Khulan Batbayar

      • Abstract
      • Slides

      Introduction

      The “cancer field,” adjacent to or even remote from tumors, entails an epithelium that might broadly display a “field effect,” entailing normal appearing cells that exhibit tumor-associated genomic or phenotypic alterations. Smoking-related cancers, such as NSCLC, demonstrate large cancer fields which may exhibit numerous abnormal molecular features. It can be hypothesized that the only cells that accumulate sufficient genomic changes to transform must be long-lived (years-decades), implying that the long-term resident progenitor “basal” cells (BC) have the replicative potential and therefore residency for such transformation.

      Methods

      We characterized the methylome changes in cytologically normal bronchial progenitor “basal” cells collected from 54 consented bronchoscopy specimen donors at standard airway locations during clinically indicated bronchoscopy, and then outgrown in primary cultures. A recently developed enzymatic methylation sequencing (EM-Seq) [1], which robustly preserves template and maps at single CpG resolution at very high fidelity was employed. Average conversion rate of unmethylated cytosine was virtually 100%; coverage was virtually complete, with depth to 15-20x. Approximately 30% of reads were excluded, based on mapping quality, duplications, unpaired reads, base quality and overlaps. CpG methylation rate was averaged in 50 bp tiled windows (regions). Multivariate analyses accounting for cancer status, age and smoking status were performed.

      Results

      The methylation status of approximately 13K regions out of 9 million were significantly affected by above listed variables alone or in combination (adjusted p < 0.01). Of the 13K 50bp-window regions, 4135 were affected significantly by cancer case vs. control status (after adjustment of age and smoking status), and this association tended towards increased methylation. This effect was more apparent in regions overlapping with genes. Cancer pathway genes were most significantly represented in affected regions (IPA) (Top five disease and function association list: Abdominal carcinoma (p=3.44E-26), Carcinoma (p=1.78E-25), Head and neck carcinoma (p= 4.07E-25), Thyroid carcinoma (p=4.93E-25), and Tumorigenesis of tissue (p= 5.24E-25)). The number of regions significantly affected by age and smoking was 2947 and 2900, respectively. There was no apparent bias towards increased or decreased methylation for both age and smoking. Interestingly, among age-affected regions overlapping with genes, there was very significant association with cancer pathways (IPA analysis is pending for smoking). The affected regions are not mutually exclusive among these three (cancer case, age, smoking) variables.

      Conclusion

      Enzymatic DNA methylation sequencing of primary cultured, cytologically normal basal progenitor cells is a powerful method allowing complete methylome coverage and corollary analysis of the impact from lung cancer case, age and smoking status of donors. Given that the analysis was performed on normal (non-cancerous) cells, these data are consistent with the existence of a field of progenitor cell DNA methylomes that may be skewed toward carcinogenesis. from individuals with anatomically remote lung cancers at time of collection, as well as those of advanced age and significant smoking history. This implies potential for developing an epigenetic molecular fingerprint of lung cancer risk, and recurrence.

      References:

      1. Vaisvila R, et al. (2019) EM-seq: Detection of DNA methylation at single base resolution from picograms of DNA. bioRxiv; DOI: 10.1101/2019.12.20.884692

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