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

AI Article Synopsis

  • Early detection of pancreatic ductal adenocarcinoma (PDAC) is crucial for improving survival, but current methods like imaging and the CA19-9 blood test lack sensitivity for early-stage tumors.
  • A study was conducted to identify a blood-based protein signature that performs better than CA19-9, analyzing approximately 3000 proteins and using machine learning to create multiple biomarker signatures.
  • The new signatures showed significant improvements, achieving 84% sensitivity at 95% specificity, compared to CA19-9’s 53% sensitivity, and identified 41 promising biomarker candidates for further validation.

Article 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 ( = 75) and various controls including healthy controls ( = 50), individuals at high risk (genetic and familial) for PDAC ( = 47), or those under surveillance for an intraductal papillary mucinous neoplasm ( = 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 ( < 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 ( = 81) and chronic pancreatitis ( = 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.

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Source
http://dx.doi.org/10.1021/acs.jproteome.4c00752DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705213PMC

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