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Activity Number:
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313
- Data Analytics and Visualization: Advances and Challenges
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Graphics
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Abstract #313131
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Title:
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Tumor-Agnostic Modeling in a Phase I Oncology Trial
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Author(s):
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Courtney Henry* and Ruby Sung and Helen Zhou
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Companies:
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GlaxoSmithKline (GSK) and GlaxoSmithKline (GSK) and GlaxoSmithKline (GSK)
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Keywords:
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tumor agnostic;
bayesian hierarchial model;
oncology;
phase I trial
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Abstract:
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The tumor-agnostic paradigm is transforming pharmaceutical research and discovery from standard therapies driven by histological characteristics to novel therapies targeting genetic (bio)markers. Tumor-agnostic therapy is a form of personalized (precision) medicine that uses the same drug to treat all cancer types that have the genetic mutation or biomarker that is targeted by the drug. In a large, on-going Phase I trial (approximately 700 patients enrolled to-date) of multiple solid tumor types, we use a tumor-agnostic modeling approach in the form of a Bayesian hierarchical model to yield predictive probabilities of the efficacy signal of a given tumor type by pooling data across other tumor types which share genetic features. This approach allows futility decisions to be made prior to hitting any pre-defined futility benchmark requiring a minimum number of observed responses. Allowing this type of continuous monitoring discourages ongoing treatments that fail to show therapeutic benefit, thus maximizing patient benefit.
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Authors who are presenting talks have a * after their name.
