212P - Extracellular vesicle miRNA as effective biomarkers for predicting antitumor efficacy in lung adenocarcinoma treated with chemotherapy and checkpoint blockade

Background

Immune checkpoint inhibitors (ICIs) have improved the care of patients in metastatic lung adenocarcinoma (LUAD). However, PD-L1 status, high Tumor Mutational Burden (TMB), and mismatch repair deficiency are the only validated biomarkers of efficacy for ICIs. These markers remain imperfect, and new predictive markers represent an unmet medical need. MicroRNAs (miRNAs) encapsulated within plasma-derived extracellular vesicles (EVs) may be suitable for use as noninvasive diagnostic biomarkers for aggressive malignancies, including LUAD. Here, we assessed the possibility of plasma EVs derived miRNAs as potential biomarkers for predicting and identifying the beneficiaries of combined immunochemotherapy.

Methods

A total of 31 patients with metastatic LUAD who received pembrolizumab combined with pemetrexed and carboplatin were enrolled. After efficacy evaluation. 25 patients had durable clinical benefits from combined immunochemotherapy, and the rest patients showed disease progression. MiRNA profiles of plasma-derived EVs from these patients were investigated using unsupervised hierarchical clustering.

Results

46 differentially expressed miRNAs (DEMs) were identified between responders and non-responders. Interestingly, we found that miR-6815-5p, miR-15a-5p, miR-92a-3p, and miR-107 were obviously down regulated in non-responder group. However, miR-769-5p, and miR-589-5p showed a trend of significantly increased expression. In addition, all these findings were further confirmed by clinical imaging assessment.

Conclusions

In conclusion, EVs miRNAs derived from patients with lung cancer showed promising application scenarios for effectively identifying true responders treated with combined immunochemotherapy.

Clinical trial identification

NCT04427475, release date is June 11, 2020.

Editorial acknowledgement

Legal entity responsible for the study

Fudan University Shanghai Cancer Center.

Funding

Shanghai "Science and Technology Innovation Action Plan" Natural Science Foundation (19ZR1410400).

Disclosure

All authors have declared no conflicts of interest.