Abstract PO-009: Multi-omic profiling of patient pancreatic cyst fluid for the identification of a novel biomarker panel of patient cancer risk

Abstract

Abstract Background: Pancreatic cancer was responsible for almost 500,000 deaths globally in 2020 according to GLOBOCAN 2020. Pancreatic cystic lesions (PCLs) are fluid-filled protrusions either on or inside the pancreas. PCLs can either be benign or pre-malignant, however, current guidelines based on clinical features are limited in their ability to accurately stratify patients based on cancer risk. Multi-omic profiling of the pancreatic cyst fluid (PCF) could aid in the identification of a novel biomarker panel of patient cancer risk. Methods: PCF was collected from 40 patients by EUS-FNA. Patients were stratified using the 2018 European evidence-based guidelines into low-risk (n=15), high risk (n=15) and no-risk/pseudocyst (n=10). PCF was sonicated and subsequently processed using a single-pot solid-phase-enhanced sample preparation (SP3) protocol with Sera-Mag SpeedBead carboxylate-modified beads prior to LC-MS. Samples were run on a Thermo Scientific Q Exactive mass spectrometer coupled to a Dionex Ultimate 3000 (RSLCnano) chromatography system. MS-generated proteomic data were analysed in Perseus (v1.6.13). HTG microRNA whole transcriptome sequencing was run on whole PCF. Transcriptomic data were analysed using HTG EdgeSeq Reveal (v3.1). Results: MS-analysis revealed 1,266 proteins present across all PCF samples. Proteins were filtered based on potential contaminants and valid values. Only proteins expressed in a minimum of six PCF samples were included in the analysis. After data clean-up, 465 proteins were examined for differential expression. A total of eight proteins were upregulated in high-risk PCF compared to low-risk (p<0.05, FDR=0.05, s0=0.1). Among them, seven have been shown to be upregulated in pancreatic cancer. Conversely, one protein which is reported to be downregulated in pancreatic cancer, was significantly upregulated in the high-risk patient cohort. A total of 2,096 miRNAs were identified across all PCF samples. MiRNAs were filtered based on fold-change > ±2 between the groups, with 202 miRNAs meeting this criteria. Forty-six miRNAs were significantly upregulated in high-risk PCF compared to low-risk PCF (adj-p<0.05, FDR=0.05, s0=0.1). Five of these miRNAs are known to be upregulated in pancreatic ductal adenocarcinoma (PDAC) tissues. Furthermore, three of the miRNAs are upregulated in the circulation of PDAC patients. Importantly, seven miRNAs identified as being upregulated in high-risk PCF have been shown to be downregulated in PDAC tissues. Differentially expressed proteins and miRNAs are currently being utilised to create an integrated, multi-omic predictive algorithm for patient risk. Conclusion: Multi-omic profiling of pancreatic cyst fluid provides an abundance of potential biomarkers that could be utilised for the stratification of patients into high-and low-risk groups for malignancy. Integration of multi-omic data has the potential to provide more robust biomarker panels of patient risk. Validation of biomarkers in independent patient cohorts will be key to the development of novel clinical biomarkers. Citation Format: Laura E. Kane, Gregory S. Mellotte, Simone Marcone, Barbara M. Ryan, Stephen G. Maher. Multi-omic profiling of patient pancreatic cyst fluid for the identification of a novel biomarker panel of patient cancer risk [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-009.