Abstract

Plasma Extracellular Vesicle-Associated microRNAs for On-Treatment Response Monitoring and Prediction

D. Michiel Pegtel, Associate Professor, Amsterdam University Medical Center

Response monitoring and outcome prediction is essential in the clinical management of hematological malignancies. Extracellular Vesicle associated microRNAs (EV-miRNAs) are considered promising liquid biopsy-based biomarkers for hematological malignancies. We performed small RNA sequencing of plasma samples collected during therapy and applied machine learning to build signatures for response prediction in Multiple Myeloma (MM) and patients with high grade B-cell lymphoma (HGBL). We collected plasma samples from multiple clinical trials and obtained 'real-world' samples. In HGBL, response was assessed by an end-of-treatment (EOT) PET/CT while in MM we defined response based in part on M protein levels. We isolated plasma EVs with size exclusion chromatography as confirmed with transmission electron microscopy (TEM), tunable resistive pulse sensing (TRPS), and western blotting. Library preparation was done according to our IsoSeek method. We applied machine learning to build models with EV-miRNAs for early response prediction (HGBL) and monitoring (MM). We could generate robust signatures for HGBL that can predict EOT response after one cycle of R-CHOP. If validated in independent cohorts, this novel approach could potentially, in combination with other modalities, guide early risk-adapted treatment strategies. In addition, we show that EV-miRNAs distinguish MM patients with active disease from those in remission. Together our data suggests plasma EV-miRNA sequencing is a versatile platform technology for minimally invasive response evaluation and prediction.