PATH-20. CILIA-ASSOCIATED GENE EXPRESSION PREDICTS SURVIVAL IN LOW-GRADE GLIOMAS

Abstract

Abstract Low-grade gliomas (WHO II and III) encompass a histopathologically and clinically heterogeneous constellation of neoplasms. Molecular biomarkers play an increasingly important role in glioma classification, therapy, and prognostication. Primary cilia are microtubule-based organelles that regulate several canonical signal transduction cascades and they have been implicated in the pathogenesis of many malignancies. We hypothesized that LGGs differentially express ciliary genes resulting in unique molecular and prognostic clusters. Four-hundred-sixty-seven LGG patients within The Cancer Genome Atlas (TCGA) were stratified based on differential expression of cilia-associated genes. Four statistically distinct clusters (P < 0.0001) were identified including a high- (median overall survival [OS] 95.5 months; CI [52.1-NA]), mid- (median OS 63.5 months; CI [43.9-NA]) and a low-surviving (median OS 23.7 months; CI [19-134]) cluster. OS was independent of IDH status, 1p/19q codeletion or MGMT promoter methylation status on multivariate analysis. Orthogonal validation was performed using 442 LGGs from the Chinese Glioma Genome Atlas (CGGA). Again, four discrete clusters were found including a high- (median OS 78.4 months, CI [45.8-NA]) and a low-surviving (median OS 16.5 months, CI [12.2-25.5]) cluster. While LGGs with 1p/19q codeletions were present in all clusters, they were overrepresented in the high-surviving groups. The most robustly differentially expressed genes were compared across clusters to find cluster-defining genes most associated with OS. LGGs in the high-surviving group are defined by the expression profiles of 13 genes while low-surviving LGGs are identified by the differential expression of 15 non-overlapping genes. LGGs can be classified into four distinct clusters that are independently associated with OS based on cilia-associated gene expression alone. Within these clusters, there is a high- and a low-surviving group, each with their own unique genetic profiles. These new molecular markers may inform novel therapeutic targets and predictors of clinical outcomes.