Serum glycoprotein biomarkers for oesophageal adenocarcinoma — ASN Events
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Serum glycoprotein biomarkers for oesophageal adenocarcinoma (#14)

Alok Shah 1 2 , Kim-Anh Le Cao 1 2 , Eunju Choi 1 2 , David Chen 3 , Benoit Gautier 1 2 , Derek Nancarrow 4 , David Whiteman 4 , Nicholas Saunders 1 2 , Andrew Barbour 2 5 , Michelle Hill 1 2
  1. UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
  2. Translational Research Institute, Brisbane, QLD, Australia
  3. School of Information and Communication Technology, Griffith University, Brisbane, QLD, Australia
  4. QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
  5. School of Medicine, The University of Queensland, Brisbane, QLD, Australia

While the incidence of most cancers are now steadying or declining, oesophageal adenocarcinoma (EAC) continues an upward trend. The rapid increase in EAC is not due to improved diagnosis, but likely attributed to the increased prevalence of risk factors of obesity and gastro-esophageal reflux. Despite aggressive treatment, the survival rate for EAC is low at 9-24% five years post diagnosis. The precursor condition, Barrett’s oesophagus (BE), affects 0.2-2% of the adult population and increases EAC risk 30-100 fold. However, due to the low conversion rate of BE to EAC, studies indicate that current endoscopic screening programs may not be beneficial. Furthermore, a significant proportion of EAC patients do not have prior BE diagnosis, hence there is an urgent need for better detection of EAC. Our goal is to develop blood biomarker panels that can be used to screen at-risk patients, with positive or suspicious results triggering follow-on endoscopic screening.

We focused on alterations in circulatory protein glycosylation, using a panel of 20 lectins to enrich serum glycoproteins based on their glycan structures1, 2. Serum samples from healthy, BE and EAC patients (n=29) were analyzed by lectin magnetic bead array (LeMBA)1, 2-coupled discovery proteomics using QTOF, followed by targeted proteomics using MRM-MS (n=61). Data analysis was performed using a customized database and analysis package "GlycoSelector" incorporating sparse Partial Least Squares-Discriminant Analysis (sPLS-DA)3.

We have identified a ranked list of glycoprotein biomarkers that distinguish a) EAC from BE and b) EAC from healthy phenotypes. Selected biomarkers were further validated using an orthogonal technique, immunoblotting. A multivariate panel achieved area under the receiver operating curve (AUROC) over 0.9, indicative of high diagnostic value. Continuing work will evaluate clinical performance of the EAC biomarker panel in a large independent patient cohort.

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

[2] Choi et al., Electrophoresis 2011

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