Diagnosis of suspected cases of gliomas such as intracranial space-occupying lesions (ICSOLs) is based on histopathologic analysis of tumor tissue obtained by invasive biopsies, which are, however, associated with risk of serious complication, high rates of mortality, procedural expenses, sequelae, and other risks. Liquid biopsy (LB)-based noninvasive approaches for diagnosis of gliomas based on circulating tumor biomarkers in peripheral blood have hence gained considerable interest. We describe the discovery of a unique 20 exosomal mRNA signature that can discern gliomas from healthy individuals. We obtained 15 mL of peripheral blood by venous draw from 222 individuals, among whom 47 had confirmed diagnosis of gliomas and 175 were asymptomatic. Exosomes were enriched from peripheral blood and were used for isolation of mRNAs. Exosomal mRNAs were used for expression analysis of 20,802 gene transcripts, including 18,574 coding genes and 2,228 noncoding genes based on University of California Santa Cruz (UCSC) hg19 annotation. Differentially expressed mRNAs were analyzed using a machine learning algorithm to identify transcripts that were unique to or uniquely absent in known cases of gliomas and in healthy individuals. The machine learning algorithm identified a 20-mRNA signature that discerned gliomas from healthy individuals with high sensitivity (87%) and specificity (95%) with an overall accuracy of 94%. The differentially expressed transcripts included MST4, RARRES3, TARP, IL32, PPP3CC, RSL1D1, RSL24D1, EIF4B, ARHGEF3, DENND2D, EVL, SEC11A, RPL22L1, HIST1H1C, IRF8, SOS2, GLTSCR2, GIMAP7, HIST1H4D, and CDK11. These differentially expressed transcripts are potentially involved in various functions such as tumor suppression, protein phosphorylation-dephosphorylation, signal transduction (ligand binding), and cell cycle regulation. Our data indicate that exosomal mRNA profiling is a viable approach for discerning suspected cases of gliomas from healthy individuals. The noninvasive nature of this approach coupled with high sensitivity and specificity favor potential clinical application.

Citation Format: Sachin Apurwa, Pooja Fulmali, Dadasaheb Akolkar, Darshana Patil, Sushant Pawar, Sneha Puranik, Vipul Todarwal, Akshay Ainwale, Pradip Devhare, Vineet Datta, Cynthe Sims, Ajay Srinivasan, Rajan Datar. An mRNA signature that accurately discerns gliomas from healthy individuals [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr B17.