Abstract 660: Integration of radiomics with "omic" analyses to predict survival in newly diagnosed IDH-1 wild-type glioblastoma

Abstract

Abstract Glioblastoma (GBM) is the most commonly diagnosed glioma and has the poorest median survival of all brain tumours at only 14 months. Despite intensive characterisation of this disease through ‘omic' investigations which has led to the identification of critical molecular drivers of malignancy, only a limited selection of biomarkers have proven to be clinically relevant in predicting patient survival. One reason for this lack of success is the existence of intratumoural heterogeneity, where individual tumours can harbour spatially separated molecular and phenotypic diversity, thereby limiting the insight which ‘omic' analysis of small biopsy specimens can impart. Radiomics involves high-throughput mining of quantitative image features via image acquisition, region of interest segmentation and feature extraction from standard MRI scans and relating extracted feature values to molecular, phenotypic and clinical properties. Notably, radiomics derives features from the whole tumour mass and therefore overcomes limitations associated with intratumoural heterogeneity. As such, we sought to investigate the ability for radiomics to describe tumour characteristics and integrate radiomics alongside genomics and transcriptomics to derive a prognostic model for IDH1 wild-type GBM. We observed 2 radiomic clusters in IDH1-wild-type GBM which each had an equivalent distribution of each of the four molecular subtypes. Whilst radiomic clusters themselves were not predictive of survival, three radiomic features were able to separate patients into long and short survival groups. Integration of genomic and transcriptomic features alongside radiomic analysis increased the predictive strength of our model. Finally, we found an association between longer survival in patients with IDH1 wild-type GBM and increased expression of the complement system. Our observations suggest that integration of radiomics alongside ‘omic' investigations offer a greater ability to predict survival in IDH1 wild-type GBM and suggests a role for the innate immune system in driving patient outcome. Citation Format: Paul M. Daniel. Integration of radiomics with "omic" analyses to predict survival in newly diagnosed IDH-1 wild-type glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 660.