Neoadjuvant immunotherapy has brought new hope for patients with non-small cell lung cancer (NSCLC). However, limited by the lack of clinically feasible markers, it is still difficult to select NSCLC patients who respond well and to predict patients' clinical outcomes before the treatment. Before the treatment, we isolated plasma extracellular vesicles (EVs) from three cohorts (discovery, training and validation) of 78 NSCLC patients treated with neoadjuvant immunotherapy. To identify differentially-expressed EV long RNAs (exLRs), we employed RNA-seq in the discovery cohort. And we subsequently used qRT-PCR to establish and validate the predictive signature in the other two cohorts. We have identified 8 candidate exLRs from 27 top-ranked exLRs differentially expressed between responders and non-responders, and tested their expression with qRT-PCR in the training cohort. We finally identified H3C2 (P = 0.029), MALAT1 (P = 0.043) and RPS3 (P = 0.0086) significantly expressed in responders for establishing the predictive signature. Integrated with PD-L1 expression, our signature performed well in predicting immunotherapeutic responses in the training (AUC=0.892) and validation cohorts (AUC=0.747). Furthermore, our signature was proven to be a predictor for favorable prognosis of patients treated with neoadjuvant immunotherapy, which demonstrates the feasibility of our signature in clinical practices (P = 0.048). Our results demonstrate that the exLR-based signature could accurately predict responses to neoadjuvant immunotherapy and prognosis in NSCLC patients.
Keywords: Extracellular vesicles; Long RNAs; Neoadjuvant immunotherapy; Non-small cell lung cancer.
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