LGG-33. NETWORK ANALYSIS INTEGRATING MICRORNAS AND CLINICAL DATA TO DETERMINE PROGNOSTIC OUTCOME IN PEDIATRIC LOW-GRADE GLIOMA PATIENTS

Abstract

Abstract BACKGROUND Low-grade pediatric gliomas (pLGGs) are the most common pediatric brain tumors. Surgical resection, when feasible, represents the standard of care. If complete resection cannot be achieved, disease progression occurs in about 30% of the cases, and these patients receive adjuvant therapy. The identification of predictive biomarkers to predict tumors that will progress after partial resection, requiring systemic treatment (Chemotherapy or target therapy), could support clinicians in the decision-making strategy. MicroRNAs have been related to tumorigenesis and tumor maintenance and can be used as important biomarkers. This study aims to develop a predictive model able to determine prognostic outcomes and to better stratify pLGG patients according to disease progression. METHODS MicroRNAs levels have been assessed in 76 human tumor tissue samples. Clinical data have been collected for statistical and network-based analyses. Using a network-based approach, we integrated clinical data and tissue microRNA expression to identify a microRNA signature able to predict disease progression. RESULTS Our preliminary microRNA expression analysis displayed new promising signatures correlating with prognostic features, such as disease progression and embryological origin of the tumor. CONCLUSION The predictive model we developed integrating biomarkers and clinical data could be crucial to support clinicians in the decision-making strategy and in advancing personalized medicine.