ACTR-17. DIAGNOSTIC UTILITY OF SERUM microRNA CLASSIFICATION IN DIFFUSE GLIOMA. (11th November 2019) - Ohno, Makoto; Matsuzaki, Juntaro; Kawauchi, Junpei; Aoki, Yoshiaki; Miura, Junichiro; Takizawa, Satoko; Kato, Ken; Sakamoto, Hiromi; Matsushita, Yuko; Takahashi, Masamichi; Miyakita, Yasuji; Ichimura, Koichi; et al.
Abstract: To improve clinical outcomes, a less invasive screening test for detection of diffuse glioma is needed. The purpose of this study is to establish models using serum microRNAs (miRNAs) to distinguish between diffuse glioma patients from non-cancer controls (Glioma Index) and between glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic brain tumors (Meta) (3-Tumor Index). A total of 580 patients, including 266 with brain or spinal tumors and 314 non-cancer controls, were analyzed. Non-cancer control samples were collected from the National Cancer Center Biobank and members of the general population undergoing routine health examinations. Expression of serum 2565 miRNAs was assessed in each sample, and the expression profiles were compared between diffuse glioma patients and non-cancer controls, and between GBM, PCNSL, and Meta. Diagnostic models were established using a training set, and diagnostic performance was confirmed in validation and exploratory sets and area under the receiver operating curve (AUC), sensitivity, specificity, and accuracy were used to evaluate their diagnostic performances. The Glioma Index was constructed using three miRNAs. The AUC was 99%, with sensitivity of 95% and specificity of 97% in the validation set. The Glioma Index classified 93% of PCNSL, 89% of Meta, 91% of benign brain tumors, and 0% of spinal tumors as positive in the exploratory set. The 3-Tumor Index was constructed using 48 miRNAs and had an accuracy of 0.80 in the validation set, positively detecting 94.1% of GBM, 80% of Meta, and 50% of PCNSL. In conclusion, we developed novel serum miRNA discriminant models to detect diffuse glioma from non-cancer control and to discriminate tumor histology. These models could serve as less invasive screening tools or complementary diagnostic tools, thereby decreasing neurosurgical risks.