Abstract INTRODUCTION Depressive symptoms are prevalent among glioma patients while the impact on survival is not fully clear and underlying mechanisms remain poorly understood. Here, we investigated the impact of glioblastoma-associated depression as the disease-presenting symptom and the influence of antidepressant pharmacotherapy on glioblastoma progression. METHODS We performed a retrospective cohort study on glioblastoma patients treated at our institution. Patients with a new depression as presenting symptom were compared to non-depressive glioblastoma patients employing propensity-matched survival analysis. Additionally, tumor tissues were subjected to DNA methylation analysis (EPIC 850k array). In vitro proliferation assays were performed to investigate the effect of antidepressants on glioma cell growth. RESULTS Of 298 glioblastoma patients, 23 patients suffered from a tumor-associated depression and received antidepressants at time of diagnosis (14 SSRI, 3 NaSSA, 4 TCA and 2 unknown). Propensity-matched survival analysis revealed a shorter OS in the depression group (median OS: 8 vs. 14; HR = 2.247; 95% CI: 1 - 5.051; p < 0.05). Methylation subclass analysis indicated a predominance of RTK2 glioblastoma within the depression group (n = 14; 63.6%). Tumors of the depression group originated more likely in the frontal lobe (43.5% vs. 29.2%) and in the midbrain (13% vs. 2.43%). No significant differences in epigenetic neural signatures were found. Treatment with either citalopram or sertraline – the most frequently used antidepressants - significantly increased the proliferation index in RTK2 glioblastoma cultures but not RTK1 glioblastoma cultures. CONCLUSION Our study reveals a strong correlation between glioblastoma-associated depression with antidepressant treatment and worse outcome in glioblastoma of the DNA methylation subclass RTK2. These findings may reveal a potential causal link, raising awareness of the increased likelihood of depression and glioblastoma progression in a methylation-dependent manner, which could guide antidepressant treatment for improving patients’ quality of life.
Read full abstract