Circulating extracellular vesicle isomiR signatures predict therapy response in patients with multiple myeloma

TL;DR

This study shows that analyzing RNA fragments in blood vesicles can predict how well multiple myeloma patients will respond to a specific treatment.

Contribution

A machine learning approach using isomiR signatures from extracellular vesicles predicts therapy response in multiple myeloma patients.

Findings

EV-isomiR signatures accurately predict therapeutic response with an area-under-the-curve of 0.98.

A classifier signature with miR-148-3p predicts durable response and improved survival in MM patients.

Targetome analysis links the isomiR signature to known drug targets BCL2 and MYC in multiple myeloma.

Abstract

Multiple myeloma (MM) is a plasma cell neoplasm characterized by high inter- and intra-patient clonal heterogeneity, leading to high variability in therapeutic responses. Minimally invasive biomarkers that predict response may help personalize treatment decisions. IsoSeek, a single-nucleotide resolution small RNA sequencing method can profile thousands of microRNAs (miRNAs) and their variants (isomiRs) from patient plasma-purified extracellular vesicles (EVs). Machine learning-generated miRNA/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% confidence interval [CI]:0.94–1.00). A classifier signature with the plasma cell-selective miR-148-3p, predicts durable response (≥6 months), progression-free (hazard ratio [HR]: 33.09, 95% CI: 4.2–262, p < 0.001), and overall survival (HR: 3.81, 95% CI: 1.05–13.99, p < 0.05). Targetome analysis connects the prognostic classifier to established MM drug targets BCL2 and MYC suggesting biological relevance. Thus, EV-isomiR sequencing in MM patients offers a tumor-naïve alternative to an invasive bone-marrow biopsy for predicting treatment outcome.

•IsoSeek detects thousands of isomiRs in plasma EVs from MM patients•EVs from MM patients are enriched in plasma B cell-derived transcripts•A machine learning strategy builds biologically driven EV isomiR-gene target networks•EV-IsomiR signature predicts durable response to daratumumab in real-world MM patients

IsoSeek detects thousands of isomiRs in plasma EVs from MM patients

EVs from MM patients are enriched in plasma B cell-derived transcripts

A machine learning strategy builds biologically driven EV isomiR-gene target networks

EV-IsomiR signature predicts durable response to daratumumab in real-world MM patients

Current diagnostic methods fail to predict which patients with multiple myeloma will benefit from daratumumab-containing treatment regimens. Gómez-Martín et al. demonstrate that a machine learning strategy applied to small RNA sequencing data from plasma extracellular vesicle fractions can generate biologically motivated isomiR signatures that predict treatment response.

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