Accurate Prognosis Prediction of Pancreatic Ductal Adenocarcinoma Using Integrated Clinico-Genomic Data of Endoscopic Ultrasound-Guided Fine Needle Biopsy

Abstract

The aim of this study was to investigate the clinical utility of minimal specimens acquired from endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) and perform targeted deep sequencing as a prognosis prediction tool for pancreatic ductal adenocarcinoma (PDAC). A total of 116 specimens with pathologically confirmed PDAC via EUS-FNB were tested using CancerSCAN® panel for a customized targeted deep sequencing. Clinical prognostic factors significantly associated with survival in PDACs were as follows: stage, tumor mass size, tumor location, metastasis, chemotherapy, and initial CA19-9 level. A total of 114 patients (98.3%) had at least a single genetic alteration, and no mutations were detected in two patients, although they were qualified for the targeted deep sequencing. The frequencies of major gene mutations responsible for PDACs were KRAS 90%, CDKN2A 31%, TP53 77%, and SMAD4 29%. A somatic point mutation of NF1, copy number alteration of SMAD4, and loss-of-function of CDKN2A were significantly associated genetic factors for overall survival. Moreover, BRCA2 point mutation was related to liver metastasis. Finally, a clinico-genomic model was developed to estimate the prognosis of patients with PDAC based on clinical parameters and genetic alterations affecting survival in patients; 20 single nucleotide variants and three copy number variations were selected. Targeted deep sequencing on minimal specimens of PDACs was performed, and it was applied to establish a clinico-genomic model for prognosis prediction.

Prognostic features and comprehensive genomic analysis of NF1 mutations in EGFR mutant lung cancer patients

“…NF1 mutations were reported as potential biomarkers for poor prognosis in pancreatic ductal adenocarcinoma, 23 colorectal cancer. 24 In our study, 118 NF1 mutant patients and 236 NF1 wild‐type patients had enrolled for survival analysis.…”

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

“…EUS-FNB data have been used to predict PDAC prognosis more accurately. Park et al (2021) obtained frequencies of gene mutations through targeted sequencing of EUS-FNB specimens from PDAC patients [107]. Their Cox regression model successfully classified patients into a high-risk group (median overall survival 5.95 months) and a low-risk group (median survival 15.27 months) based on genetic mutations detected from cyst fluid and clinical factors such as age, stage, and mass size.…”

Targeted transcriptomic analysis of pancreatic adenocarcinoma in EUS-FNA samples by NanoString technology

“…EUS-guided fine-needle aspiration (EUS-FNA) is the technique of choice for safely sampling the pancreas, with a pooled sensitivity and specificity for the diagnosis of PDAC of 85% and 98% respectively ( Hewitt et al, 2012 ). Beyond cytopathological evaluation, several studies have shown excellent performance of EUS-FNA samples from PDAC for DNA genomic analysis ( Larghi et al, 2020 ; Habib et al, 2021 ; Park et al, 2021 ). RNA analysis of EUS-acquired pancreatic samples is less extended than DNA based approaches, mainly because of the low yield and quality of RNA due to its easy degradation by pancreatic RNAases.…”