Loading
Life sciences · Journal article
Cancer Research Communications · October 13, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Abstract Only a subset of non-small cell lung cancer (NSCLC) patients benefits from immune checkpoint inhibitors (ICIs). Tissue PD-L1 has limited accuracy in predicting ICI response and requires invasive biopsies, highlighting the urgent need for reliable, minimally invasive biomarkers. Extracellular vesicle PD-L1 (EV PD-L1) is a promising liquid biopsy marker, but its predictive performance is confounded by PD-L1 derived from non-tumor cells. We developed a tumor-specific EVs–based assay to predict ICI response. Tumor-derived extracellular vesicles (TEXs) were selectively captured using the tumor-specific Thomsen–Friedenreich glycoantigen alpha (TF-Ag-α), and PD-L1 levels in TEXs expressing TF-Ag-α (i.e., TEXTF-Ag-α PD-L1) were measured in sera from 91 NSCLC patients receiving anti-PD-1 therapy (40 responders and 51 non-responders). Predictive performance was evaluated at pre-treatment baseline, at the end of first treatment cycle, and via fold changes. At pre-treatment baseline, TEXTF-Ag-α PD-L1 showed moderate predictive ability (AUC: 0.76; sensitivity: 0.97; specificity: 0.56). By the end of the first treatment cycle, responders exhibited a ~4.18-fold increase in TEXTF-Ag-α PD-L1 expression, whereas non-responders showed an approximate 20% decrease. Dynamic fold changes after the first treatment cycle substantially improved predictive performance, achieving an AUC of 0.93, sensitivity of 0.95, and specificity of 0.80 for distinguishing responders from non-responders, outperforming both total EV PD-L1 and tissue PD-L1. These results demonstrated that selective TEX capture using TF-Ag-α significantly enhanced the predictive value of EV PD-L1. Dynamic changes in TEXTF-Ag-α PD-L1 provided a highly accurate, minimally invasive biomarker for early assessment of anti-PD-1 response, supporting tumor-specific EVs–based liquid biopsy for precision immunotherapy.