NIMG-65. PREDICTING PROGNOSIS AND CANCER HOTSPOT MUTATIONS USING QUALITATIVE MR IMAGING ANALYSIS IN GLIOBLASTOMA. (11th November 2019) - Verduin, Maikel; Compter, Inge; Primakov, Sergey; van Kuijk, Sander; te Dorsthorst, Maarten; Revenich, Elles; ter Laan, Mark; Pegge, Sjoert; Meijer, Anton; Beckervordersandforth, Jan; Jan Speel, Ernst; Jochems, Arthur; et al.
Abstract: INTRODUCTION: Tumor heterogeneity poses one of the major limitations in improving the treatment for glioblastoma (GBM), which calls for new clinically relevant predictive models. This study aims to investigate non-invasive diagnostic methods, including patient characteristics and qualitative imaging analysis as a prognostic classifier and predictor for druggable oncogenes. METHODS: We performed a retrospective analysis on 143 GBM patients (discovery cohort). Diagnostic MRIs were re-analyzed for qualitative imaging features (VASARI features). DNA was extracted from formalin-fixed, paraffin-embedded GBM tissue of the discovery cohort for next-generation sequencing (Ion Torrent Cancer Hotspot panel v2Plus), TERT-promoter mutation and MGMT-methylation analysis. Multivariable regression analysis was used to determine the prognostic and predictive value of VASARI features. RESULTS: Of the 143 patients, median age was 61.4 years (range 15.5–84.6) with a median overall survival of 12 months (range 0–142). We observed IDH1 R132H mutation in 8.5%, MGMT-promotor methylation in 26.1%, TERT-promotor mutation (C250T;C228T) in 69.5%, EGFR mutation in 20.3% and EGFR amplification in 37.5% of all patients. A set of eight VASARI features was identified to be associated with overall survival (p< 0.001), which is currently being validated in an external dataset (n= 184). Interestingly, VASARI features appeared to be associated with IDH1-mutation (four features, p=0.004), TERT-promotor mutation (five features, p-value < 0.001), EGFR mutation (five features, p-value < 0.001) and EGFR amplification (seven features, p-value < 0.001) but not with MGMT-methylation (two features, p-value=0.054). Additional cancer hotspots are currently being analyzed and internal validation is ongoing. CONCLUSION AND FUTURE PERSPECTIVES: We propose an integrated prognostic classifier comprising MRI features, also associated with GBM-specific molecular alterations. Additionally, quantitative MRI radiomics features are being extracted from the discovery and validation set and incorporated in the prognostic classifier. Subsequently, radiomics and VASARI features will be correlated to intratumoral heterogeneity, assessed by tissue micro-array analysis of the discovery cohort.