Aberrant circulating tumour DNA methylations as biomarkers for early detection of pancreatic ductal adenocarcinoma: a retrospective study

Pancreatic ductal adenocarcinoma is a cancer with high mortality and low survival, the early detection of which is hampered by the absence of specific symptoms until an advanced stage is reached. No reliable early screening tool for pancreatic ductal adenocarcinoma is currently available. Circulating tumour DNA (ctDNA) methylation has emerged as a promising new type of biomarker for blood-based early detection of multiple types of cancer. As a part of the PanSeerX study, which aims to discover robust ctDNA methylation markers for multicancer early screening, a customised pancreatic ductal adenocarcinoma-specific targeted methylation sequencing panel has been preliminarily developed and validated for its accuracy in classifying pancreatic ductal adenocarcinoma from retrospective clinical plasma samples.We led a single-centre retrospective pilot study at the Changhai Hospital, Navy Medical University, Shanghai, China, where 62 adult patients with pancreatic ductal adenocarcinoma and 393 age-matched and sex-matched healthy controls were enrolled after written consent was obtained from each participant. Individuals with previous cancer diagnosis were excluded. Blood samples were drawn from all participants. A methylation target sequencing panel covering 1601 potential pancreatic ductal adenocarcinoma methylation markers, which were collected from our previous PanSeer study and other related studies, identified by mining the public methylomic datasets The Cancer Genome Atlas and Gene Expression Omnibus, or discovered from the analysis of in-house reduced-representation bisulfite sequencing data, was designed and technically validated. The panel was then applied to test the collected plasma samples at mean a sequencing depth of 1000× per target to quantify the methylation levels and patterns on the targets. A two-layer deep neural network model was built to classify cancer and healthy samples from the sequencing data. The robustness of entire approach was verified with a 3× cross-validation by randomly splitting samples into training set and test set at a 2:1 ratio. This study was approved by the Changhai Hospital, Navy Medical University (reference number CHEC2021-165).All blood samples were used in the development and validation of the pancreatic ductal adenocarcinoma panel. The average area under the curve of the training dataset during the 3× validation was 1·000 (95% CI 0·999-1·000; sensitivity 100·0% [95% CI 100·0-100·0; specificity 99·0%, [98·6-99·5]). The average area under the curve of the testing dataset during the 3× validation was 0·987 (95% CI 0·971-0·996; sensitivity 89·0% [95% CI 75·0-100·0]; specificity 96·0% [92·3-100·0]). Notably, this model is highly sensitive to pancreatic ductal adenocarcinoma of stages I and II, achieving a sensitivity of 81% for stage I and 88% for stage II, showing its ability to detect early stage pancreatic ductal adenocarcinoma.Our preliminary results show that our pancreatic ductal adenocarcinoma-detecting panel is highly accurate in classifying pancreatic ductal adenocarcinoma plasma from healthy controls using ctDNA methylation markers. Its high sensitivity for stages I and II pancreatic cancer is especially promising for further optimisation into diagnostics for blood-based, early pancreatic ductal adenocarcinoma screening. On the basis of these results, a larger, multicentre study is currently underway, which not only enrolled a higher number of patients with pancreatic ductal adenocarcinoma cases and healthy controls, but also included samples from acute and chronic pancreatitis to comprehensive evaluate our pancreatic ductal adenocarcinoma-detecting panel's accuracy in classifying pancreatic ductal adenocarcinoma plasma against non-malignant controls. The results of this study are expected to be reported later in 2022.The National Key Research and Development Project of China (grant 2019YFC1315904), the 234 Discipline Climbing Plan Project of the First Affiliated Hospital of Naval Military Medical University (grant 2019YXK033), and the Shanghai Science and Technology Committee.