Glioblastoma is the most common and most malignant type of intrinsic brain tumor in adults. The standard of care for glioblastoma consists of surgical debulking followed by combined radiochemotherapy. The clinical efficacy of standard therapies for newly diagnosed glioblastomas is rather modest with the highest survival rate at 5-years being less than 10%. Inevitable recurrence after cytotoxic therapies poses the major challenge in the clinical management of high grade gliomas. For recurrent glioblastomas, there is no standard therapy with lack of level one evidence for treatment efficacy. Recent evidence indicates that post-therapy recurrence in gliomas is a consequence of a plethora of molecular and cellular factors including intratumoural heterogeneity, functional hierarchy of distinct types of glioma cells, dynamic changes in the molecular landscapes and cellular composition of the tumour during therapy and the impact of particular treatment modalities. There is an emerging consensus that molecular distinctions within and between individual tumours is an important factor determining clinical outcomes. Consequently, integrated approaches based on the combination of molecular profiling with traditional methods such as immunohistochemical phenotyping, karyotyping and/or non-quantitative methylation-specific PCR have emerged as a promising venue towards increasing the predictive value of diagnostics for malignant brain tumors. The high level of inter-and intra-tumoural molecular diversity in gliomas underscores the need of integrating high throughput molecular profiling and pharmacogenomics into a diagnostic paradigm for gliomas and raises the possibility that molecular-instructed personalized treatments may provide clinical benefit to patients with glioblastoma, particularly in the setting of post-treatment recurrence. Here we discuss potential prospects and challenges of patient-tailored diagnostics and personalized treatment strategies for recurrent glioblastomas.

e13533 Background: This study developed molecular guided tools for individualized selection of chemotherapeutics for recurrent glioblastoma (rGB). A consortium involving clinical neurooncologists, molecular biologists and bioinformaticians identified gene expression patterns in rGB and quantitatively analyzed pathways involved in response to FDA approved oncodrugs. Methods: From2016 to 2018 biopsies from GB were collected using a multisampling approach. Biopsy material was used to isolate glioma stem-like cells and examined by RNA-sequencing. RNA-seq results were subjected to differential expression (DE) analysis and Oncobox analysis – a bioinformatic tool for quantitative pathway activation analysis. Results for newly diagnosed (nGB) and rGB (tissue samples and cell cultures) were compared. Oncobox analysis was further used to examine differential activation of pathways involved in response to existing chemotherapeutics. Results: 128 tissue samples and 28 cell cultures from a total of 44 GBs including 23 nGB, 19 rGB and 2 second-recurrent GBs were analyzed. 14 patient-matched pairs of nGB and rGB were obtained. DE analysis of nGB and rGB, showed a distinct “signature” associated with rGB. Oncobox analysis found down regulation of pathways related to cell cycle and DNA repair and upregulation of immune response pathways in rGB vs corresponding nGB. Specifically, pathways targeted by temozolomide, which is the first line chemotherapy for GB, were found down regulated in rGB. Among the top pathways upregulated in rGB were the pathways targeted by durvalumab and pomalidomide currently under investigation in phase II or III trials for GB. Conclusions: Specific pathway analysis revealed regional and clinical stage-associated differences in the transcriptional landscapes of nGB and rGB. Our results support a concept of treatment-induced resistance to cytotoxic therapeutics and indicate that temozolomide and radiation treatment have important impacts on gene expression changes associated with GB recurrence. Systematic molecular profiling of rGB is a promising avenue towards predicting sensitivity to targeted therapeutics in rGBs on an individual basis.