109 A Small RNA Signature from Extracellular Vesicles in Patient Plasma Correlates With Recurrence or Progression of High-Grade Gliomas. (April 2023) - Han, Jae Ho; Attwood, Kathleen; Roy, Jeremy; Weeks, Adrienne
Abstract : INTRODUCTION: Glial cell-derived tumors of the central nervous system make up the largest group of brain tumors. Most are high-grade gliomas (HGG) and are universally fatal despite multimodal therapy. In managing patients with HGG, predicting recurrence, or differentiating between pseudoprogression (radiation necrosis) and true tumor progression would be invaluable in improving overall prognosis. Characterizing small RNA (sRNA) expression profiles from plasma-derived extracellular vesicles (EVs) over the course of a patient's treatments, may allow for patient-specific treatment modifications and improve outcomes. METHODS: Plasma EVs were isolated using Vn96 capture from HGG patients perioperatively and with routine, follow-up surveillance imaging. Small RNA sequencing was conducted and unsupervised hierarchal clustering of sRNA signatures were completed. Expression profiles were grouped longitudinally with the clinical status of patients. RESULTS: Cluster analysis of nine HGG patients, has revealed a sRNA signature that is able to distinguish between tumors showing evidence of progression and those remaining stable over time. Those samples obtained from patients where a clinical diagnosis of tumor progression or pseudoprogression were uncertain, were found to cluster into progression vs. stable signatures. Annotation of differentially expressed small RNAs identified from stable vs. clinically progressed patient include small nuclear RNA, ribosomal RNA, and micro RNA among others. MiRNA species we have identified correspond to previously identified miRNA, which have been ascribed functional significance to HGG aggressiveness and progression. CONCLUSIONS: These preliminary findings demonstrate the potential utility of sRNA profiling of plasma-derived EVs obtained from patients with high-grade gliomas as non-invasive biomarkers for recurrent/progressive disease or stability/pseudoprogression.