A Combined DNA/RNA-based Next-Generation Sequencing Platform to Improve the Classification of Pancreatic Cysts and Early Detection of Pancreatic Cancer Arising From Pancreatic Cysts

Marina N. Nikiforova, Abigail I. Wald, Daniel M. Spagnolo, Melissa A. Melan, Maria Grupillo, Yi Tak Lai, Randall E. Brand, Anne Marie O'Broin-Lennon, Kevin McGrath, Walter G. Park, Patrick R. Pfau, Patricio M. Polanco, Nisa Kubiliun, John Dewitt, Jeffrey J. Easler, Aamir Dam, Shaffer R. Mok, Michael B. Wallace, Vivek Kumbhari, Brian A. BooneWallis Marsh, Shyam Thakkar, Kimberly J. Fairley, Elham Afghani, Yasser Bhat, Sanjay Ramrakhiani, John Nasr, Wasseem Skef, Nikhil R. Thiruvengadam, Asif Khalid, Kenneth Fasanella, Jennifer Chennat, Rohit Das, Harkirat Singh, Savreet Sarkaria, Adam Slivka, Charles Gabbert, Tarek Sawas, Thomas Tielleman, Hendrikus Dutch Vanderveldt, Anna Tavakkoli, Lynette M. Smith, Katelyn Smith, Phoenix D. Bell, Ralph H. Hruban, Alessandro Paniccia, Amer Zureikat, Kenneth K. Lee, Melanie Ongchin, Herbert Zeh, Rebecca Minter, Jin He, Yuri E. Nikiforov, Aatur D. Singhi

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Objective: We report the development and validation of a combined DNA/RNA next-generation sequencing (NGS) platform to improve the evaluation of pancreatic cysts. Background and Aims: Despite a multidisciplinary approach, pancreatic cyst classification, such as a cystic precursor neoplasm, and the detection of high-grade dysplasia and early adenocarcinoma (advanced neoplasia) can be challenging. NGS of preoperative pancreatic cyst fluid improves the clinical evaluation of pancreatic cysts, but the recent identification of novel genomic alterations necessitates the creation of a comprehensive panel and the development of a genomic classifier to integrate the complex molecular results. Methods: An updated and unique 74-gene DNA/RNA-targeted NGS panel (PancreaSeq Genomic Classifier) was created to evaluate 5 classes of genomic alterations to include gene mutations (e.g., KRAS, GNAS, etc.), gene fusions and gene expression. Further, CEA mRNA (CEACAM5) was integrated into the assay using RT-qPCR. Separate multi-institutional cohorts for training (n=108) and validation (n=77) were tested, and diagnostic performance was compared to clinical, imaging, cytopathologic, and guideline data. Results: Upon creation of a genomic classifier system, PancreaSeq GC yielded a 95% sensitivity and 100% specificity for a cystic precursor neoplasm, and the sensitivity and specificity for advanced neoplasia were 82% and 100%, respectively. Associated symptoms, cyst size, duct dilatation, a mural nodule, increasing cyst size, and malignant cytopathology had lower sensitivities (41-59%) and lower specificities (56-96%) for advanced neoplasia. This test also increased the sensitivity of current pancreatic cyst guidelines (IAP/Fukuoka and AGA) by >10% and maintained their inherent specificity. Conclusions: PancreaSeq GC was not only accurate in predicting pancreatic cyst type and advanced neoplasia but also improved the sensitivity of current pancreatic cyst guidelines.

Original languageEnglish (US)
Pages (from-to)E789-E797
JournalAnnals of surgery
Volume278
Issue number4
DOIs
StatePublished - Oct 1 2023

Keywords

  • intraductal oncocytic papillary neoplasm
  • intraductal papillary mucinous neoplasm
  • mucinous cystic neoplasm
  • pancreatic ductal adenocarcinoma
  • pancreatic neuroendocrine tumor
  • pseudocyst
  • serous cystadenoma

ASJC Scopus subject areas

  • Surgery

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