E-Poster Presentation 33rd Lorne Cancer Conference 2021

Desmoglein-2 expression by multiple myeloma is an independent predictor of poor prognosis (#210)

Lisa M Ebert 1 , Kate Vandyke 2 3 , Zahied Z Johan 1 , Lih Y Tan 1 , Kay K Myo Min 1 , Ben M Weimann 1 4 , Stuart M Pitson 1 , Andrew Zannettino 2 3 , Craig Wallington-Beddoe 4 5 , Claudine S Bonder 1
  1. Centre for Cancer Biology, University of South Australia & SA Pathology, Adelaide
  2. Adelaide Medical School, Faculty of Health Sciences, University of Adelaide., Adelaide, SA, Australia
  3. Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
  4. College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
  5. Flinders Medical Centre, Adelaide, South Australia, Australia

Multiple myeloma (MM) is the second most common haematological malignancy and is an incurable disease of neoplastic plasma cells (PC). Newly-diagnosed MM patients currently undergo lengthy genetic testing to match chromosomal mutations with the most potent drug/s to decelerate disease progression. With only 17% of MM patients surviving 10-years post diagnosis, faster detection and earlier intervention would unequivocally improve outcomes. Here, we show that the cell surface protein desmoglein-2 (DSG2) is overexpressed (gene and protein) in approximately one third of bone marrow biopsies from newly-diagnosed MM patients. Importantly, DSG2 expression was strongly predictive of poor clinical outcome, with patients expressing DSG2 above the 70th percentile exhibiting an almost 3-fold increased risk of death. As a prognostic factor, DSG2 is independent of (i) genetic subtype, (ii) routinely measured biomarkers of MM activity (e.g. paraprotein) and (iii) therapy received. Functional studies revealed a non-redundant role for DSG2 in adhesion of MM PC to endothelial cells. Together, our studies suggest DSG2 to be a potential cell surface biomarker that can be readily detected by flow cytometry to rapidly predict disease trajectory at the time of diagnosis.