Circulating extracellular vesicle isomiR signatures predict therapy response in patients with Multiple Myeloma

Multiple myeloma (MM) is a plasma cell neoplasm characterized by high inter- and intra-patient clonal heterogeneity, leading to a high variability in therapeutic responses. Minimally invasive biomarkers that predict response may help personalize treatment decisions.Using IsoSeek, a single-nucleotide resolution small RNA sequencing method, we profiled thousands of microRNAs and its variants (isomiRs) from patient plasma-purified extracellular vesicles (EVs). Machine learning-generated microRNA/isomiR classifiers accurately predict therapeutic response in relapsed/refractory MM (RRMM) patients receiving daratumumab-containing regimens, achieving an area-under-the-curve of 0.98 (95% CI:0.94-1.00). A classifier signature including plasma cell-selective miR-148-3p, predicted durable response (more than 6 months), progression free survival (Hazard Ratio: 33.09, 95% CI:4.2-262), and overall survival (Hazard Ratio: 3.81, 95% CI:1.05-13.99). Targetome analysis linked the isomiR classifier to MM drug targets including BCL2 and MYC, underscoring the biological relevance of the prognostic classifier. EV-isomiR sequencing offers a tumor-naive alternative to invasive bone-marrow biopsies for predicting treatment outcomes in RRMM patients.