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This is a phase II, multi-arm, Bayesian adaptive platform trial designed to efficiently evaluate novel therapies for advanced hepatocellular carcinoma (HCC) after first-line treatment failure. The study aims to rapidly identify the most effective investigational regimens and discover predictive biomarker signatures (from tumor tissue, blood, and imaging) to guide personalized second-line therapy.
Full description
This open-label, adaptive platform trial addresses the lack of effective second-line therapies for advanced HCC. Multiple experimental regimens will be evaluated against a common control arm. The study employs a Bayesian adaptive design. Patient randomization probabilities will favor regimens showing better interim efficacy (primary endpoint: objective response rate). Underperforming regimens will be dropped, and new ones may be added over time. A key objective is to identify predictive biomarker signatures. Baseline and on-treatment biomarker assessments (including tumor molecular profiling, ctDNA, and quantitative imaging features) will be analyzed to define patient subsets most likely to respond to each therapy. Regimens with a high predictive probability of success will graduate for further development. Those with low probability of benefit will be discontinued. This design enables rapid, biomarker-informed therapy screening for second-line HCC.
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350 participants in 9 patient groups
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Peng Wang, MD
Data sourced from clinicaltrials.gov
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