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Abstract
PURPOSE:
Identifying predictive factors for the clinical outcome of patients with lower 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.

MATERIAL AND METHODS:
We identified 511 patients with lower grade glioma (Grade 2 and 3) 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:
Among 511 patients with lower grade glioma in The Cancer Genome Atlas dataset, we identified a group that was characterized by radioresistant and high PD-L1 (the PD-L1-high-RR group). Multivariate Cox models demonstrated that the membership in the PD-L1-high-RR can predict overall survival regarding to RT. Differentially expressed genes associated with the PD-L1-high-RR group were found to play a role in the immune response, including the T-cell receptor signaling pathway.

CONCLUSION:
We tested the predictive value of a "31-gene signature" and PD-L1 expression status in a dataset of patients with lower grade glioma. Our results suggest that the patient population classified as the PD-L1-high-RR may benefit most from radiotherapy combined with anti-PD-1/PD-L1 treatment. Prospective clinical trial is necessary to validate the findings in a homogenous treated patient cohort.

Author information

Jang BS1, Kim IA2.
1
Department of Radiation Oncology, Seoul National University Hospital, Republic of Korea.
2
Department of Radiation Oncology and Cancer Research Institute, Seoul National University, College of Medicine, Republic of Korea; Department of Radiation Oncology, Seoul National University Bundang Hospital, Seongnamsi, Republic of Korea. Electronic address: inah228@snu.ac.kr.