Background: Identifying predictive factors for the clinical outcome of patients with low grade gliomas following radiotherapy could help optimize patient treatments. Here, we investigate the predictive efficacy of both a previously identified “31 gene signature” and programmed death ligand-1 (PD-L1) expression.

Methods: We identified 516 patients with low grade glioma in The Cancer Genome Atlas dataset and divided them into two clusters: radiosensitive (RS) and radioresistant (RR). Patients were also classified as PD-L1-high or PD-L1-low based on CD274 mRNA expression. Five-year survival rates were compared across patient groups, and differentially expressed genes were identified via a gene enrichment analysis.

Results: Patients in the RS group had a higher survival rate than in the RR group, but only when treated with radiotherapy (63% vs. 52%, p = 0.019; univariate analysis). Patients in the RR group who were also PD-L1-high had a lower survival rate than other patients (50% vs. 60%, p = 0.010). Furthermore, a Cox multivariate analysis demonstrated that both belonging to this patient population and expressing wild-type isocitrate dehydrogenase were predictive of lower survival rates (p = 0.048). Differentially expressed genes associated with this group were found to play a role in the immune response, including the T-cell receptor signaling pathway.

Conclusions: We validated the predictive value of a 31 gene signature and PD-L1 expression in a dataset of patients with low-grade glioma. Our results suggest that the patient population classified as both RR and PD-L1 high may benefit most from radiotherapy combined with anti-PD1/PD-L1 treatment.

This work was supported by a grant from the Ministry of Science, ICT & Future Planning (NRF#2017R1A2B4002710 & & 2017M2A2A7A01018438) to In Ah Kim.

Citation Format: Bum Sup Jang, In Ah Kim. A radiosensitivity gene signature and PD-L1 expression are predictive of the clinical outcomes of patients with lower-grade glioma in The Cancer Genome Atlas dataset [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr A75.