ESTABLISHING PERSONALIZED TREATMENT OPTIONS FOR RECURRENT HIGH-GRADE GLIOMAS

Abstract BACKGROUND High grade glioma (HGG) patients develop resistance to standard treatment leading to disease progression and limited life expectancy. Recent advances in the molecular characterisation of treatment-naïve HGGs based on next generation sequencing and DNA methylation analyses have led to a better delineation of HGG-subtypes and identification of distinct genomic abnormalities opening opportunities for personalized treatment strategies. METHODS We collected 300 fresh glioma specimen with approximately 100 longitudinal samples of initial and recurrent tumors from 43 matched patients. We succeeded in generating a live-biobank of HGG patient-derived orthotopic xenografts (PDOX) and 3D tumor organoids that neatly recapitulates the mutational spectrum including structural DNA variations and methylation-based subtypes of gliomas. A highlight is the generation of 19 PDOXs of paired initial and relapse HGGs from 9 glioma patients, enabling high-throughput drug screens. We performed comprehensive molecular profiling using arrayCGH, DNA-methylation and targeted DNA sequencing on patient specimen and their derivatives, 3D tumor organoids and PDOXs. RESULTS Detailed analysis of the paired longitudinal samples indicated that PDOXs closely recapitulate the evolutionary trajectory of the parental tumors. Furthermore, targeted genomic sequencing of paired HGGs suggests that relapse tumors also accumulate somatic mutations in epigenetic effectors. Based on patient-derived material we carried out drug response screening on 3D tumor organoids using a compound library matching the majority of genes that were assessed with targeted sequencing. Differential drug responses between initial and recurrent tumors were observed and the prevailing primary drug response profiles were essentially recapitulad in the relapse setting. CONCLUSIONS Response assessment of treatment-naïve gliomas and their recurrences provides crucial information on the differential sensitivity between initial and relapsed HGGs and offers novel personalized therapeutic options for the relapse setting. Furthermore, in depth correlation of the profiled somatic molecular landscape with drug response will enable pharmacogenomic predictions of potential inhibitors in the clinical setting.