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

IF 10.6 1区医学 Q1 CELL BIOLOGY

Cell Reports Medicine Pub Date : 2025-09-16 DOI:10.1016/j.xcrm.2025.102358

Cristina Gómez-Martín, Esther E E Drees, Monique A J van Eijndhoven, Nils J Groenewegen, Steven Wang, Sandra A W M Verkuijlen, Jan R T van Weering, Ernesto Aparicio-Puerta, Leontien Bosch, Kris A Frerichs, Christie P M Verkleij, Marie J Kersten, Josée M Zijlstra, Daphne de Jong, Catharina G M Groothuis-Oudshoorn, Michael Hackenberg, Johan R de Rooij, Niels W C J van de Donk, D Michiel Pegtel

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引用次数: 0

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.

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循环细胞外囊泡isomiR特征预测多发性骨髓瘤患者的治疗反应。

多发性骨髓瘤（MM）是一种浆细胞肿瘤，其特点是患者间和患者内克隆异质性高，导致治疗反应的高度可变性。预测反应的微创生物标志物可能有助于个性化治疗决策。IsoSeek是一种单核苷酸分辨率的小RNA测序方法，可以从患者血浆纯化的细胞外囊泡（ev）中分析数千种microrna （mirna）及其变体（isomir）。机器学习生成的miRNA/isomiR分类器准确预测接受含daratumumab方案的复发/难治性MM （RRMM）患者的治疗反应，曲线下面积为0.98（95%置信区间[CI]:0.94-1.00）。具有浆细胞选择性miR-148-3p标记的分类器可预测持续反应（≥6个月）、无进展（风险比[HR]: 33.09, 95% CI: 4.2-262, p < 0.001）和总生存（风险比：3.81,95% CI: 1.05-13.99, p < 0.05）。目标组分析将预后分类器与已建立的MM药物靶标BCL2和MYC联系起来，提示生物学相关性。因此，MM患者的EV-isomiR测序为预测治疗结果提供了tumor-naïve替代侵入性骨髓活检的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cell Reports Medicine Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)

CiteScore

15.00

自引率

1.40%

发文量

231

审稿时长

40 days

期刊介绍： Cell Reports Medicine is an esteemed open-access journal by Cell Press that publishes groundbreaking research in translational and clinical biomedical sciences, influencing human health and medicine. Our journal ensures wide visibility and accessibility, reaching scientists and clinicians across various medical disciplines. We publish original research that spans from intriguing human biology concepts to all aspects of clinical work. We encourage submissions that introduce innovative ideas, forging new paths in clinical research and practice. We also welcome studies that provide vital information, enhancing our understanding of current standards of care in diagnosis, treatment, and prognosis. This encompasses translational studies, clinical trials (including long-term follow-ups), genomics, biomarker discovery, and technological advancements that contribute to diagnostics, treatment, and healthcare. Additionally, studies based on vertebrate model organisms are within the scope of the journal, as long as they directly relate to human health and disease.