081. The impact of clinical, radiographical, and molecular subgrouping on pediatric ependymoma outcomes

Authors: Nicholas S Chapman

Ependymomas are the third most common pediatric neoplasm, accounting for roughly 10% of pediatric brain tumors. This novel study analyzed clinical, radiographical, biological and molecular subgroup data to understand prognosis of pediatric ependymomas.Methods: An IRB-approved retrospective review of biopsy-proven, primary intracranial ependymoma in which molecular and genetic analysis was performed. Records were searched for age, gender, extent of resection, tumor grade (II/III), tumor location, recurrence, survival, and adjuvant therapy, tumor molecular subgroups by methylation (PF-A, PF-B. RELA), and radiographical findings. Mann-Whitney, Chi-squared and Fisher’s T tests were conducted to understand the impact of each variable on patient outcomes.
Thirty-two patients with primary intracranial ependymoma were identified who had complete molecular analysis over the study period (21 male, 11 female). Mean age at resection was 5.5 years (±0.8). Mean of last follow-up was 63 months (±10). Infratentorial tumors were 68.7% and supratentorial were 31.3%. Tumor grade WHO II and WHO III (48.3% vs. 51.6%). 25/32 (78.1%) underwent GTR and 7/32 (21.9%) underwent STR. 73.3% of patients had necrotic or cystic features. ADC for WHO II had a mean of 927.9 (±54.1) and WHO III had a mean of 888.3 (±45.8). The molecular subgroup was found to be 20/32(61.3%) PF-A, 3/32(9.6%) PF-B, and 9/32(29.1%) RELA. Demographics were not associated with patient outcomes. ADC to tumor grade was not significant (p=0.096). WHO II had a minimum of 719.0 and maximum 1532.8 ADC. WHO III was had a minimum of 557.7 and maximum 1216.9 ADC. Tumor grade and molecular subgroup was not associated with patient outcomes. There was no significance between PF-A and PF-B with association to tumor grade.
The study examined a variety of clinical, radiographical, and molecular factors and outcomes for ependymomas. It is the largest series to date analyzing a multifactorial model using advanced radiographic features and molecular subgroups impacting prognosis.