Next-generation sequencing of pancreatic cyst wall specimens obtained using micro-forceps for improving diagnostic accuracy.

Abstract

Background and study aims Pancreatic cysts are common incidental findings, with an estimated prevalence of 13% to 15% in imaging done for other reasons. Diagnosis often relies on collection of cyst fluid, but tissue sampling using micro-forceps may allow for a more reliable diagnosis and higher yield of DNA for next-generation sequencing (NGS). The primary aim was to assess the performance of NGS in identifying mucinous cyst. The secondary aims were to assess DNA yield between the cyst fluid and cyst wall tissue, complication rate and performance of conventional investigations. Patients and methods Twenty-four patients referred for endoscopic ultrasound were recruited. Biopsies were taken using micro-forceps and the AmpliSeq Cancer Hotspot panel was used for NGS, a polymerase chain reaction assay targeting several hotspots within 50 genes, including GNAS , KRAS and VHL . Results The concentration of DNA extracted from 24 cyst wall samples was significantly higher than in the nine of 24 available matched cyst fluid samples. The sensitivity, specificity, and diagnostic accuracy of NGS for diagnosing mucinous cyst were 93%, 50% and 84%; for standard of care, they were -66.6%, 50% and 63.1%; and for standard of care with NGS, they were 100%, 50%, and 89.4% respectively. Cyst wall biopsy was able to diagnose 19 of 24 cysts (4 high risk, 7 intraductal papillary mucinous neoplasms, 4 cysts of mucinous origin, and 4 benign). Conclusions NGS data correlate well with histology and may aid in diagnosis and risk stratification of pancreatic cysts. Cyst wall biopsy performs well in diagnosing cysts but was inadequate in five of 24 patients.

• Authors: Stuart Astbury, Aishwarya Baskar, Jane I Grove, Philip Kaye, Aloysious D Aravinthan, Martin W James, Christopher Clarke, Guruprasad P Aithal, Suresh Vasan Venkatachalapathy
• Journal: Endoscopy international open (Endosc Int Open) Vol. 11 Issue 10 Pg. E983-E991 (Oct 2023) ISSN: 2364-3722 [Print] Germany
• PMID: 37941539 (Publication Type: Journal Article)
• Copyright: The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).