Extracellular vesicles (EVs) represent promising non-invasive biomarkers that may aid in the diagnosis and risk-stratification of multiple myeloma (MM), an incurable malignancy characterized by monoclonal expansion of plasma cells in the bone marrow. EVs are membrane enclosed nanoparticles containing protein, RNA and DNA that are secreted by most cells and are a potential source of easily accessible cancer biomarkers as they are found in body fluids including blood, saliva and urine. Daratumumab (DARA) is a CD38 antibody approved for treatment of MM, and despite the anti-tumour effects of DARA, the majority of patients eventually relapse. One mechanism of DARA resistance is the upregulation of complement inhibitory proteins CD55 and CD59, and programmed dead ligand-1 (PD-L1) on plasma cells. Our recent published data reveals that EVs isolated from MM patient peripheral blood have elevated levels of CD55, CD59 and CD147 relative to healthy control EVs, with MM patient bone marrow EVs having even higher expression CD59 and CD147 (Brennan et al. 2022). In addition, EV PD-L1 levels are associated with patient response to DARA. Collectively these markers represent the first predictive biomarker signature for myeloma. In this proposal we will advance and refine the biomarker panel in longitudinal disease monitoring studies in MM patients undergoing anti-CD38 therapy. We will also undertake retrospective longitudinal multi-omic protein and miRNA profiling of EVs to identify additional biomarker candidates that will strengthen our biomarker panel, improving sensitivity and specificity for disease monitoring. Overall, this proposal will develop an EV protein signature through longitudinal studies over the course of disease and treatment that will be correlated with biological and clinical data. This work will provide a clinically relevant non-invasive liquid biopsy with potential to aid clinical decisions, to complement or replace invasive bone marrow sampling for improved monitoring of patient response to anti-CD38 therapy.
