Dataset Information

Proteomic profiling of pancreatic cyst fluid in patients with pancreatic cystic lesions identifies novel biomarkers of pancreatic cancer risk

ABSTRACT: Pancreatic Cancer (PC) has the worst 5-year survival rate of any cancer as of 2024, at just 13%. The late-stage diagnosis of these patients limits their treatment options, further compounding the problem. Early detection of PC, therefore, is the primary concern of most PC research, as it has the potential to make a substantial difference to the treatment and survival of these patients. Pancreatic cystic lesions (PCLs) are fluid-filled sacs, on or inside the pancreas, that have the potential to become premalignant. While some PCLs are completely benign, others have been shown to have malignant potential and could therefore play a role in the progression to PC. Using the 2018 European evidence-based guidelines for pancreatic cystic neoplasms, patients were classified as being either at a low- or high-risk of PC development. In this study, we profile the proteome of pancreatic cyst fluid from low-risk (n=15) and high-risk (n=17) patients with PCLs and identify differentially expressed proteins between these two risk classifications. We show that these PCF-based differentially expressed proteins have potential utility as biomarkers of risk stratification in this setting.

INSTRUMENT(S): Dionex instrument model, Q Exactive

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Laura Kane

LAB HEAD: Stephen Maher

PROVIDER: PXD057299 | Pride | 2025-01-06

REPOSITORIES: pride

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Dataset's files

Source:

			Action	DRS
	2021_06_22_LK_D02_P77_HR.raw	Raw
	2021_06_22_LK_D04_P13_LR.raw	Raw
	2021_06_22_LK_D05_P44_HR.raw	Raw
	2021_06_22_LK_D06_P46_LR.raw	Raw
	2021_06_22_LK_D07_P90_LR.raw	Raw

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Publications

Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development.

Kane Laura E LE Mellotte Gregory S GS Mylod Eimear E Dowling Paul P Marcone Simone S Scaife Caitriona C Kenny Elaine M EM Henry Michael M Meleady Paula P Ridgway Paul F PF MacCarthy Finbar F Conlon Kevin C KC Ryan Barbara M BM Maher Stephen G SG

Scientific reports 20250102 1

Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential ...[more]

PMID: 39747972

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Project description:Pancreatic cancer (PC) is the fourth leading cause of cancer death with an overall 5-year survival rate of < 5%, a statistic that has changed little in almost 50 years. A deeper understanding of the underlying molecular pathophysiology is expected to advance the urgent need to develop novel therapeutic and early detection strategies for this disease. Genomic characterisation of PC has previously relied on targeted PCR based exome sequencing of small cohorts of mixed primary and metastatic lesions propagated as xenografts or cell lines (Jones et al, Science 321:1801-1806), leaving the true mutational spectrum of the clinical disease largely unresolved. Here we use exome sequencing (https://www.ebi.ac.uk/ega/studies/EGAS00001000154) and copy number analysis (not submitted) to define genomic aberrations in a prospectively accrued clinical cohort (n = 142) of early (Stage I and II) pancreatic adenocarcinoma. Detailed analysis of 99 informative tumours identified 1982 non-silent mutations and 1628 significant CNV events, and defined 439 significantly mutated genes based on stringent Significant Mutated Gene or GISTIC analysis. Integration with functional data from in vitro shRNA and in vivo Sleeping Beauty-mediated somatic mutagenesis screens provided supportive evidence for 184 of these as candidate driver mutations. Pathway based analysis recapitulated clustering of mutations in core signalling pathways in PC, and identified multiple new components in each, particularly in DNA damage repair mechanisms (ATM, TOP2A, TLM, RPA1). We also identified frequent somatic aberrations in genes involved in novel mechanisms including chromatin modification (SWI/SNF complex members, SETD2, EPC1), and axon guidance (Semaphorin, Slit, Netrin and Ephrin signalling), extending the number of core perturbed pathways in PC. Aberrant expression of axon guidance genes co- segregated with poor patient survival, and in animal models was associated with disease development and progression, further implicating perturbation of the axon guidance pathway as a novel mechanism important in PC. This dataset includes gene expression data from 90 primary tumour samples, 88 of which were used in this manuscript for survival analysis. Much of this data is also available through the International Cancer Genome Consortium (ICGC) Data Portal (http://dcc/icgc.org), under the project code: "Pancreatic Cancer (QCMG, AU)". Access to the strictly restricted clinical data must be made through the ICGC Data Access Compliance Office (http://www.icgc.org/daco). This dataset contains expression array data from 90 primary pancreatic ductal adenocarcinoma samples. One sample is present with two biological replicates, all others have 1 biological replicate.