Abstract
Predictors of patient outcome derived from gene methylation, mutation, or expression are severely limited in IDH1 wild-type glioblastoma (GBM). Radiomics offers an alternative insight into tumor characteristics which can provide complementary information for predictive models. The study aimed to evaluate whether predictive models which integrate radiomic, gene, and clinical (multi-omic) features together offer an increased capacity to predict patient outcome. A dataset comprising 200 IDH1 wild-type GBM patients, derived from The Cancer Imaging Archive (TCIA) (n = 71) and the McGill University Health Centre (n = 129), was used in this study. Radiomic features (n = 45) were extracted from tumor volumes then correlated to biological variables and clinical outcomes. By performing 10-fold cross-validation (n = 200) and utilizing independent training/testing datasets (n = 100/100), an integrative model was derived from multi-omic features and evaluated for predictive strength. Integrative models using a limited panel of radiomic (sum of squares variance, large zone/low gray emphasis, autocorrelation), clinical (therapy type, age), genetic (CIC, PIK3R1, FUBP1) and protein expression (p53, vimentin) yielded a maximal AUC of 78.24% (p = 2.9 × 10−5). We posit that multi-omic models using the limited set of ‘omic’ features outlined above can improve capacity to predict the outcome for IDH1 wild-type GBM patients.
Highlights
Glioblastoma (GBM, grade IV astrocytoma) is the most common and deadly brain tumor [1,2].Median survival of 15 months has remained essentially unchanged since the introduction of trimodal therapy [3], which combines maximum safe resection, radiation therapy (RT), and systemic temozolomide (TMZ)
Imaging Archive (TCIA, n = 71), and an internal cohort treated at McGill University Health Centre (MUHC, n = 129) between 2005 and 2012 and previously reported by our group [22] (Table 1)
Using The Cancer Genome Atlas (TCGA) patients with available transcriptomic analysis (n = 346), when separating patients based upon median expression of each gene performing log-rank analysis testing, we found 498 genes which were differentially expressed between patients with short or long survival in univariate analysis (Table S3)
Summary
Glioblastoma (GBM, grade IV astrocytoma) is the most common and deadly brain tumor [1,2].Median survival of 15 months has remained essentially unchanged since the introduction of trimodal therapy [3], which combines maximum safe resection, radiation therapy (RT), and systemic temozolomide (TMZ). Glioblastoma (GBM, grade IV astrocytoma) is the most common and deadly brain tumor [1,2]. GBM can be divided into two types: Primary GBM, which arises de novo, and secondary GBM, which is an evolutionary progression from low-grade glioma (LGG) [4]. 70% to 80% of secondary GBMs have mutations in the isocitrate dehydrogenase 1 (IDH1) gene that are absent in primary GBM [5,6]. The emergence of next-generation sequencing (NGS) technologies has allowed unprecedented characterization of the molecular landscape of glioma and stimulated the search for means of disease stratification and prediction of patient survival. The use of ‘omic’ analysis for identification of survival features has had the most success in grade II/III glioma where distinct subtypes of gliomas can be Cancers 2019, 11, 1148; doi:10.3390/cancers11081148 www.mdpi.com/journal/cancers
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