Poster Presentation 26th Lorne Cancer Conference 2014

Novel serum glycoprotein biomarkers for Barrett’s esophagus and esophageal adenocarcinoma - Discovery and validation (#245)

Alok Shah 1 , David Chen 2 , Kim-Anh Lê Cao 3 , Eunju (April) Choi 1 , Derek Nancarrow 4 , David Whiteman 4 , Nicholas Saunders 1 , Andrew Barbour 5 , Michelle Hill 1
  1. The University of Queensland Diamantina Institute, Brisbane, QLD, Australia
  2. School of Information and Communication Technology, Griffith University, Brisbane, QLD, Australia
  3. Queensland Facility for Advanced Bioinformatics, The University of Queensland, Brisbane, QLD, Australia
  4. QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
  5. School of Medicine, The University of Queensland, Brisbane, QLD, Australia

BACKGROUND: Esophageal adenocarcinoma (EAC) is one of the most rapidly increasing cancers globally. The majority of EAC cases are diagnosed at very late stages during pathogenesis hence <15% of the patients survive 5 year post-diagnosis. There is an urgent need to improve diagnosis of EAC and its pre-cancer metaplastic condition, Barrett’s esophagus (BE). BE patients are monitored using upper gastro-esophageal endoscopy with biopsy for early neoplastic changes. However, being an asymptomatic condition, it is very difficult to identify BE patients for screening. Moreover, endoscopy is unsuitable for population screening due to high cost, requirement of technical expertise and patient non-compliance. This project aims to identify serum biomarkers for diagnosis of BE and EAC, with the goal of translating to blood tests.

APPROACH: We focused on alterations in circulatory protein glycosylation, using a panel of 20 lectins to isolate different glycan structures on serum glycoproteins[1,2]. Serum samples from healthy (n=9), BE (n=10) and EAC (n=10) patient groups were analyzed by lectin magnetic bead array-coupled mass spectrometry (LeMBA-MS/MS)[1,2]. Data analysis was performed using a customized database and analysis package "GlycoSelect" which incorporates outlier detection and sparse Partial Least Squares regression discriminant analysis (sPLS-DA)[3].

RESULTS: We identified a ranked list of candidate glycobiomarkers that distinguish a) EAC from BE b) BE from healthy and c) EAC from healthy group. Top two candidate biomarkers were validated with AUROC of 0.74 to discriminate EAC from BE and 0.71 to discriminate BE from healthy patient group using orthogonal validation technique LeMBA-immunoblotting in an independent patient cohort (n=80). Future work will validate all candidate protein-lectin pairs using lectin-affinity array coupled with triple quadrupole quantitative mass spectrometry measurements for the independent patient cohort. The specificity and sensitivity of panels of glycoprotein biomarkers will be determined for formulating a serum screening test for BE and EAC.

[1] Loo et al., J Proteome Res 2010

[2] Choi et al., Electrophoresis 2011

[3] Lê Cao et al., BMC Bioinformatics 2011