Tailoring Glioblastoma Treatment for Frail Populations: Insights from Genomic and Clinical Data

Background/ObjectivesGlioblastoma is the most common primary malignant brain tumor in adults, with a particularly high incidence among individuals aged 65 and older. Older patients often experience worse outcomes due to limited treatment options, comorbidities, and frailty. This study investigates the impact of radiation therapy and genomic factors on survival outcomes in older glioblastoma patients, aiming to inform treatment strategies for this vulnerable population. MethodsWe analyzed clinical and genomic data from 109 glioblastoma patients aged 65 and older, obtained from The Cancer Genome Atlas (TCGA). Kaplan-Meier survival analysis was performed to assess the impact of radiation therapy on survival. Correlations between genomic features, including mutation count, tumor mutational burden, and aneuploidy score, and overall survival were examined. Descriptive statistics were used to summarize patient demographics and treatment patterns. ResultsRadiation therapy was associated with a higher mean survival (10.3 months) compared to patients who did not receive radiation (6.2 months). Genomic factors, such as mutation count and tumor mutational burden, showed weak negative correlations with survival. Despite the overall poor prognosis, radiation therapy appeared to modestly improve survival in this cohort. ConclusionsOur findings highlight the potential benefits of radiation therapy for older glioblastoma patients, even in the context of frailty and comorbidities. Further research is needed to explore how genomic markers can inform personalized treatment strategies and improve outcomes in this population. Simple SummaryGlioblastoma is an aggressive brain cancer that disproportionately affects older adults, a group often excluded from clinical trials. This study aims to examine how treatment approaches, such as radiation therapy and tumor characteristics, influence survival outcomes in glioblastoma patients aged 65 and older. By analyzing clinical and genomic data, we hope to identify factors that may improve treatment strategies and outcomes for this vulnerable population. Our findings could help guide healthcare professionals in making more personalized and effective treatment decisions for older patients, potentially improving their quality of life and survival.