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Poster Display session 2

4897 - Early detection of pancreatic ductal adenocarcinoma using methylation signatures in circulating tumor DNA


29 Sep 2019


Poster Display session 2


Tumour Site

Pancreatic Adenocarcinoma


Xiao-ding Liu


Annals of Oncology (2019) 30 (suppl_5): v253-v324. 10.1093/annonc/mdz247


X. Liu1, H. Wu1, Y. Li2, X. Liu1, Z. Zhang1, L. Yu1, Z. Qin3, Z. Su3, R. Liu3, Q. He3, M. Dai2, Z. Liang1

Author affiliations

  • 1 Pathology, PUMCH-Peking Union Medical College Hospital (East), 100730 - Beijing/CN
  • 2 General Surgery, PUMCH-Peking Union Medical College Hospital (East), 100730 - Beijing/CN
  • 3 Research And Development, Singlera Genomics Inc, 201203 - Shanghai/CN


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Abstract 4897


A noninvasive cfDNA test to detect early-stage pancreatic ductal adenocarcinoma (PDAC) is highly desirable to reduce PDAC’s high mortality rate. DNA methylation markers are promising to differentiate PDAC tissues from benign pancreas tissues, which may lead to liquid biopsy tests for PDAC early screening.


We have collected freshly frozen clinical PDAC tissues (N = 46), para-tumour pancreas tissues (N = 30), PDAC plasma samples (N = 120), chronical pancreatitis plasma samples (N = 90), and normal plasma samples (N = 100). Genomic DNA and circulating tumour DNA (ctDNA) samples were isolated and purified. DNA methylation profiles were generated by reduced representation bisulfite sequencing (RRBS). We quantified DNA methylation status of Methylation Haplotype Blocks (MHB) using Methylation Haplotype Load (MHL). Tumour-specific DNA methylation signatures were identified for PDAC by comparing MHL scores between PDAC and para-tumour pancreas tissues, PDAC tissues with normal cell-free circulating DNA (cfDNA), and PDAC ctDNA with normal cfDNA samples.


We identified 350 of MHBs by combining unique MHBs identified in each comparison. Using MHB selected from tissue-vs-tissue comparison to build classifiers, we reiterated training and cross-validating PDAC tissue classification models using SVM method, and achieved the average sensitivity of 86% specificity of 88%, and AUC of 0.91. Other machine learning methods generated similar results. We further compared our markers with previously published DNA methylation markers for PDAC, and found that genes associated with published markers are highly enriched in the genes associated with our markers (p < 1E-20; hypergeometric test), indicating they may co-regulate a same set of genes that are involved in PDAC pathology. We have further developed NGS based panels and algorithms to classify PDAC patients using cell-free DNA (cfDNA) samples based on the identified methylation markers.


Using multiple metrics on MHBs, we identified PDAC-specific DNA methylation markers, some of which are functionally overlapped with previously reported markers. These markers are candidate biomarkers for non-invasive PDAC screening.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Peking Union Medical College Hospital and Singlera Genomics Inc.


China Science and Technology Exchange Center.


All authors have declared no conflicts of interest.

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