Biomarker Discovery for Early Detection of Pancreatic Ductal Adenocarcinoma (PDAC) Using Multiplex Proteomics Technology

Early detection of pancreatic ductal adenocarcinoma (PDAC) can improve survival but is hampered by the absence of early disease symptoms. Imaging remains key for surveillance but is cumbersome and may lack sensitivity to detect small tumors. CA19-9, the only FDA-approved blood biomarker for PDAC, is insufficiently sensitive and specific to be recommended for surveillance. We aimed to discover a blood-based protein signature to improve PDAC detection in our main target population consisting of stage I or II PDAC patients (<i>n</i> = 75) and various controls including healthy controls (<i>n</i> = 50), individuals at high risk (genetic and familial) for PDAC (<i>n</i> = 47), or those under surveillance for an intraductal papillary mucinous neoplasm (<i>n</i> = 36). Roughly 3000 proteins were measured using Olink multiplex technology and conventional immunoassays. Machine learning combined biomarker candidates into 4- to 6-plex signatures. These signatures significantly (<i>p</i> < 0.001) outperformed CA19-9 with 84% sensitivity at 95% specificity, compared to CA19-9's sensitivity of 53% in the target population. Exploratory analysis was performed in new-onset diabetes (<i>n</i> = 81) and chronic pancreatitis (<i>n</i> = 50) patients. In conclusion, 41 promising biomarker candidates across multiple signatures were identified using proteomics technology and will be further tested in an independent cohort.

Authors

Institutions

Publication Details

Journal
Journal of Proteome Research
Published
2024-12-19
DOI
https://doi.org/10.1021/acs.jproteome.4c00752
Citations
10
Primary Topic
Pancreatic and Hepatic Oncology Research
Type
article
Field-Weighted Citation Impact
2.83
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Biomarker Discovery for Early Detection of Pancreatic Ductal Adenocarcinoma (PDAC) Using Multiplex Proteomics Technology

Thomas Charles King, Maria Sterner, Ramy Huber, Ralph Schiess et al.
10 citations
Journal of Proteome Research
Pancreatic and Hepatic Oncology Research
2.83
article

Biomarker Discovery for Early Detection of Pancreatic Ductal Adenocarcinoma (PDAC) Using Multiplex Proteomics Technology

Thomas Charles King, Maria Sterner, Ramy Huber, Ralph Schiess, Natasha Kureshi, Alcibiade Athanasiou, A. Wittig, Norma Alonzo Palma
article en
10 citations

Abstract

Early detection of pancreatic ductal adenocarcinoma (PDAC) can improve survival but is hampered by the absence of early disease symptoms. Imaging remains key for surveillance but is cumbersome and may lack sensitivity to detect small tumors. CA19-9, the only FDA-approved blood biomarker for PDAC, is insufficiently sensitive and specific to be recommended for surveillance. We aimed to discover a blood-based protein signature to improve PDAC detection in our main target population consisting of stage I or II PDAC patients (<i>n</i> = 75) and various controls including healthy controls (<i>n</i> = 50), individuals at high risk (genetic and familial) for PDAC (<i>n</i> = 47), or those under surveillance for an intraductal papillary mucinous neoplasm (<i>n</i> = 36). Roughly 3000 proteins were measured using Olink multiplex technology and conventional immunoassays. Machine learning combined biomarker candidates into 4- to 6-plex signatures. These signatures significantly (<i>p</i> < 0.001) outperformed CA19-9 with 84% sensitivity at 95% specificity, compared to CA19-9's sensitivity of 53% in the target population. Exploratory analysis was performed in new-onset diabetes (<i>n</i> = 81) and chronic pancreatitis (<i>n</i> = 50) patients. In conclusion, 41 promising biomarker candidates across multiple signatures were identified using proteomics technology and will be further tested in an independent cohort.

Journal of Proteome ResearchVol. 24(1)
Medicon Village (SE), Immunovia (Sweden) (SE)
Openalex Percentile: Top 7%
Pancreatic and Hepatic Oncology Research
2.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.