Unravelling the Impact of Tumor Location on Patient Survival in Glioblastoma: A Genomics and Radiomics Approach

Glioblastoma (GBM), the most aggressive form of brain tumor, has a median survival rate of 12-15 months. Understanding the relationship between genetics and tumor location, as well as identifying non-invasive biomarkers, is crucial for improving treatment strategies and survival outcomes in GBM. In this study, we investigated the impact of tumor location on survival outcome of GBM patients along with genetic factors that influence tumor behaviour in different brain regions. Interestingly, we found that patients with parietal lobe tumors had significantly poor survival outcome compared to those with tumors in other brain regions, particularly the frontal lobe. In a comprehensive genomic analysis, we identified genetic factors, seemingly contributing to the poor survival outcomes in parietal lobe patients. We found the enrichment of PTEN loss-of-function mutations in parietal lobe tumors and interestingly these mutations are known to be associated with chemoresistance and poor patient survival. We also found two fusion genes i.e., FGFR3-TACC3 and EGFR-SEPT14, exclusively in parietal lobe tumors, which are known to play crucial roles in tumorigenesis. Differential gene expression analysis revealed the upregulation of genes like PITX2, HOXB13, and DTHD1, which could be responsible for tumor progression in parietal lobe tumors. Conversely, the downregulation of ALOX15 increased relapse risk. Copy number alterations, such as deletions in tumor suppressor gene (LINC00290), were linked to the aggressive nature of parietal lobe tumors. Radiomic analysis revealed two key features, lower LLL_GLDM_DependanceEntropy and higher HLL_firstorder_Mean, both of which show a significant correlation with increased risk and poorer survival outcome. These findings suggest the potential for targeted therapies and personalized treatments based on tumor location, genetic profile, and radiomic markers. We anticipate that as the size of the datasets will increase for radiogenomics based studies, it will further strengthen these findings and our understanding of molecular drivers for GBM progression, treatment resistance and survival outcome.