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 radio-chemotherapy. 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. [Display omitted] •The progress in biomarker identification for the prognosis and treatment of glioblastoma (GB) is currently unsatisfactory.•The collection of tumour samples for the discovery of novel biomarkers presents numerous limitations.•Exclusion of patients from surgery limits correct representation of the entire GB population, introduces a selection bias and reduces the relevance of translational studies.•The implementation of liquid biopsy has the potential to address the issue of selection bias in GB translational studies.