Clinical trial design for primary brain tumors: a problem-based framework for neuro-oncology practice

Primary brain tumor trials may produce an uninterpretable or non-generalizable answer when biological mismatch, inadequate intratumoral exposure, restrictive eligibility, molecular rarity, unstable controls, and disease-specific endpoint ambiguity are addressed separately. The practical problem is not the absence of modern methods, but failure to select and combine them according to the dominant threat to the development decision.

We performed a practice-facing narrative synthesis of methodological literature, consensus recommendations, regulatory guidance, and published neuro-oncology trials through June 2026. The review focuses primarily on adult trials while identifying areas in which adolescent inclusion or tumor-specific pediatric response frameworks are relevant. For each design problem, we apply four questions: the problem, its failure mode, the design response, and the residual uncertainty.

We propose a problem-based framework that matches each dominant threat to trial validity or feasibility with a prespecified design response. Published trials illustrate why apparently compelling signals can fail at confirmation: ACT IV and INTELLANCE-1 showed no overall-survival benefit despite biomarker selection, and CheckMate 143 showed that a durable minority response signal did not translate into superiority over bevacizumab. Conversely, ROAR and INDIGO demonstrate that strong biological selection, brain-penetrant therapy, and a disease-appropriate population can generate clinically meaningful effects. Quantitative examples show that recurrent-glioblastoma benchmarks vary substantially between historical and contemporary control cohorts. Endpoint hierarchies should combine contemporary Response Assessment in Neuro-Oncology criteria with survival, neurologic function, symptoms, cognition, corticosteroid exposure, seizures, and patient-reported outcomes. We provide a decision tree, disease-specific endpoint map, steroid thresholds, and applied case studies.

A problem-based framework adds value by making the design rationale auditable: each methodological choice is linked to a defined failure mode, quantitative assumptions, and the uncertainty that remains. This approach can improve go/no-go decisions, generalizability, and patient relevance without implying that any single design tool is universally superior.