Abstract

Abstract BACKGROUND MRI is the current gold standard imaging technique for diagnostic evaluation and monitoring of pediatric CNS tumors; however, MRI measures can only provide inference into tumor biology and molecular stratification. miRNAs are an abundant and stable nucleic acid found circulating in blood and cerebrospinal fluid (CSF) and can be utilized as a tumor biomarker. METHODS We hypothesized that correlating miRNA biomarkers with radiological tumor measurements may provide improved diagnostic and monitoring for patients with pediatric brain tumors. Using a cohort of 54 pediatric brain tumors including low grade glioma, ependymoma, germ cell tumor, medulloblastoma, atypical teratoid rhabdoid tumor and high-grade glioma we attempted to combine MRI findings and circulating miRNA data. The miRNA expression was profiled in 33 CSF and 52 plasma samples using the HTG EdgeSeq platform. Clinically acquired, multi-parametric MRI scans at time-points close to liquid biopsy collection were collected retrospectively and used to generate volumetric tumor segmentations. Machine learning methods were implemented to evaluate combinatory features. RESULTS We identified unique miRNA targets that significantly correlated with MRI features, clinical findings, and patient outcomes. In both CSF and plasma, miRNA expression was found to correlate with diagnosis and clinical features including tumor grade and disease burden (p < 0.05). In CSF, miRNA expression was correlated with MRI features including cystic core presence, edema, leptomeningeal disease and tumor location (p <0.05). In plasma, differences in miRNA expression were related to MRI measurements including cystic core volume, location, and leptomeningeal disease (p < 0.05). Combination of miRNA targets for plasma or CSF with imaging-based features significantly improved histology specific multimodal diagnosis predictions. CONCLUSIONS These results demonstrate the potential utility of miRNAs as a pediatric brain tumor biomarker which when combined with imaging features can improve minimally and non-invasive diagnostics and the subsequent management of pediatric brain tumor patients.

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