The impact of survivorship bias in glioblastoma research

Despite advances in the therapy of Central Nervous System (CNS) malignancies, treatment of glioblastoma (GB) poses significant challenges due to GB resistance and high recurrence rates following post-operative radiochemotherapy. The majority of prognostic and predictive GB biomarkers are currently developed using tumour samples obtained through surgical interventions. However, the selection criteria adopted by different neurosurgeons to determine which cases are suitable for surgery make operated patients not representative of all GB cases. Particularly, geriatric and frail individuals are excluded from surgical consideration in some cancer centers. Such selection generates a survival (or selection) bias that introduces limitations, rendering the patients or data chosen for downstream analyses not representative of the entire community. In this review, we discuss the implication of survivorship bias on current and novel biomarkers for patient selection, stratification, therapy, and outcome analyses.

Automated summary

Despite advances in CNS malignancies therapy, GB treatment remains challenging due to resistance and high recurrence rates. Most predictive GB biomarkers are developed using surgical samples, but selection criteria vary among neurosurgeons, making the patients not representative. Survivorship bias affects biomarkers for patient selection, therapy, and outcome analyses.