Identification of a biomarker panel using a multiplex proximity ligation assay improves accuracy of pancreatic cancer diagnosis

J Transl Med. 2009 Dec 11:7:105. doi: 10.1186/1479-5876-7-105.

Abstract

Background: Pancreatic cancer continues to prove difficult to clinically diagnose. Multiple simultaneous measurements of plasma biomarkers can increase sensitivity and selectivity of diagnosis. Proximity ligation assay (PLA) is a highly sensitive technique for multiplex detection of biomarkers in plasma with little or no interfering background signal.

Methods: We examined the plasma levels of 21 biomarkers in a clinically defined cohort of 52 locally advanced (Stage II/III) pancreatic ductal adenocarcinoma cases and 43 age-matched controls using a multiplex proximity ligation assay. The optimal biomarker panel for diagnosis was computed using a combination of the PAM algorithm and logistic regression modeling. Biomarkers that were significantly prognostic for survival in combination were determined using univariate and multivariate Cox survival models.

Results: Three markers, CA19-9, OPN and CHI3L1, measured in multiplex were found to have superior sensitivity for pancreatic cancer vs. CA19-9 alone (93% vs. 80%). In addition, we identified two markers, CEA and CA125, that when measured simultaneously have prognostic significance for survival for this clinical stage of pancreatic cancer (p < 0.003).

Conclusions: A multiplex panel assaying CA19-9, OPN and CHI3L1 in plasma improves accuracy of pancreatic cancer diagnosis. A panel assaying CEA and CA125 in plasma can predict survival for this clinical cohort of pancreatic cancer patients.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biological Assay / methods*
  • Biological Assay / standards
  • Biomarkers, Tumor / blood*
  • Humans
  • Pancreatic Neoplasms / blood*
  • Pancreatic Neoplasms / diagnosis*
  • Pancreatic Neoplasms / pathology
  • Proportional Hazards Models
  • Regression Analysis
  • Reproducibility of Results
  • Sensitivity and Specificity

Substances

  • Biomarkers, Tumor