Abstract 472: Transcriptome profiling-based identification of prognostic subtypes in glioblastoma: Novel therapeutic strategy targeting invasiveness

Abstract

Abstract Glioblastoma (GBM) is a lethal tumor, but few biomarkers and molecular subtypes predicting prognosis are available. This study was aimed to identify prognostic subtypes and multi-omics signatures for GBM. We identified 80 genes most associated with GBM prognosis using correlations between gene expression levels and overall survival of patients. The prognostic score of each sample was calculated using these genes, followed by assigning three prognostic subtypes according to prognostic scores. This novel classification was validated in several independent datasets including our patient dataset. Functional annotation revealed that invasion- and cell cycle-related gene sets were enriched in the poor and favorable group, respectively. Therefore, the three subtypes were named as invasive (poor), mitotic (favorable), and intermediate. Interestingly, invasive subtype showed increased invasiveness, and MGMT methylation was enriched in mitotic subtype, indicating the need for different therapeutic strategies according to prognostic subtypes. For clinical convenience, we also identified the genes that best distinguished the invasive and mitotic subtypes. Immunohistochemical staining showed that markedly higher expression of PDPN in the invasive subtype and of TMEM100 in the mitotic subtype. We expect that this comprehensive transcriptome-based classification, with several multi-omics signatures and biomarkers, can improve molecular understanding of GBM. Because invasive subtype showed worse prognosis, we next evaluated invasion-modulating transcriptional regulatory networks for novel therapeutic interventions. After classification of 23 GBM patient-derived tumorspheres into low and high invasion groups, we applied single sample gene set enrichment analysis using transcription factor (TF) target gene sets. According to scores, TFs responsible for low (PCBP1) and high (STAT3 and SRF) invasiveness were identified. Consistently with computational prediction, knockdown of PCBP1 significantly increased invasion area, whereas knockdown of STAT3 or SRF significantly suppressed invasive properties in all tested TSs and mice. Notably, MR images showed coherent patterns with invasion of originated TS, and high invasiveness was associated with poor prognosis in both mice and GBM patients, interrelating these transcriptional regulatory networks, invasive phenotype, and prognosis. We suggest that these transcription factors are deterministic molecules for invasion, which can be utilized as novel drug targets for GBM. Citation Format: Junseong Park, Jin-Kyoung Shim, Seon-Jin Yoon, Se Hoon Kim, Jong Hee Chang, Seok-Gu Kang. Transcriptome profiling-based identification of prognostic subtypes in glioblastoma: Novel therapeutic strategy targeting invasiveness [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 472.