Immune checkpoint inhibitors (ICIs) show promise, but most patients do not respond. We identify and validate biomarkers from extracellular vesicles (EVs), allowing non-invasive monitoring of tumor- intrinsic and host immune status, as well as a prediction of ICI response. We undertook transcriptomic profiling of plasma-derived EVs and tumors from 50 patients with metastatic melanoma receiving ICI, and validated with an independent EV-only cohort of 30 patients. Plasma-derived EV and tumor transcriptomes correlate. EV profiles reveal drivers of ICI resistance and melanoma progression, exhibit differentially expressed genes/pathways, and correlate with clinical response to ICI. We created a Bayesian probabilistic deconvolution model to estimate contributions from tumor and non-tumor sources, enabling interpretation of differentially expressed genes/pathways. EV RNA-seq mutations also segregated ICI response. EVs serve as a non-invasive biomarker to jointly probe tumor-intrinsic and immune changes to ICI, function as predictive markers of ICI responsiveness, and monitor tumor persistence and immune activation.

免疫检查点抑制剂 (ICI) 显示出前景，但大多数患者没有反应。我们从细胞外囊泡 (EV) 中识别和验证生物标志物，允许对肿瘤内在和宿主免疫状态进行无创监测，并预测 ICI 反应。我们对 50 名接受 ICI 的转移性黑色素瘤患者的血浆衍生的 EV 和肿瘤进行了转录组分析，并通过 30 名患者的独立 EV 队列进行了验证。血浆衍生的 EV 和肿瘤转录组相关。EV 谱揭示了 ICI 耐药性和黑色素瘤进展的驱动因素，表现出差异表达的基因/途径，并与对 ICI 的临床反应相关。我们创建了一个贝叶斯概率反卷积模型来估计来自肿瘤和非肿瘤来源的贡献，能够解释差异表达的基因/途径。EV RNA-seq 突变也分离了 ICI 反应。EVs 作为一种非侵入性生物标志物，共同探测 ICI 的肿瘤内在和免疫变化，作为 ICI 反应性的预测标志物，并监测肿瘤持续存在和免疫激活。