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Life sciences · Journal article
BMC Cancer · November 6, 2026
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Abstract Background Immune checkpoint inhibitors (ICIs), including anti-programmed cell death-1/programmed death ligand-1 (anti-PD-(L)1) therapies, have improved outcomes in patients with advanced non-small cell lung cancer (NSCLC) and melanoma. However, objective response rates remain limited (40 to 60%), and existing biomarkers, including tumor PD-L1 expression and emerging markers such as tumor mutational burden (TMB) and circulating tumor DNA (ctDNA) dynamics, remain insufficient to accurately predict response to ICIs. Extracellular vesicles (EVs), carrying proteins and nucleic acids originating from tumor and immune cells, represent a promising minimally invasive source of biomarkers reflecting both tumor biology and systemic immune status. We previously identified an EV-associated signature linked to response to anti-PD-(L)1 therapy in a small cohort of patients with NSCLC or melanoma, supporting its further validation in larger prospective cohorts. Methods EVpredict is a prospective, multicenter, non-randomized study designed to evaluate the predictive value of EV-associated immune and tumor biomarkers in patients with unresectable locally advanced or metastatic NSCLC or melanoma treated with anti-PD-(L)1 therapies. A total of 378 patients will be enrolled. Blood samples will be collected longitudinally before and during anti-PD-(L)1 treatment. EVs will be isolated from plasma and characterized by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and EV marker profiling, following MISEV2023 guidelines. EV-associated proteins will be quantified by multiplex ELISA, while EV-derived miRNAs will be analyzed by small RNA sequencing and RT-qPCR. The primary objective is to determine the association between baseline EV protein biomarkers and treatment response, assessed by best overall response (BOR) according to RECIST 1.1 criteria within 6 months. Secondary objectives include the identification of an EV-derived miRNA predictive signature, predictive score development, evaluation of longitudinal changes in EV-associated biomarkers during follow-up, assessment of their predictive value for primary and secondary resistance and progression-free survival (PFS), and comparison with current standard biomarkers. Discussion This study aims to validate EV-associated multimolecular signatures as predictive biomarkers of anti-PD-(L)1 response. Strengths include prospective multicenter recruitment, standardized sampling, centralized processing, and minimally invasive longitudinal monitoring. If validated, EV-derived signatures could improve patient stratification and support more personalized immunotherapy strategies. Trial registration ClinicalTrials.gov identifier NCT07376512. Registered 29 January 2026, https://clinicaltrials.gov/study/NCT07376512?term=evpredict&viewType=Card&rank=1