A Proof-of-Concept Clinical Trial Design for Evolutionary Guided Precision Medicine for Cancer
Abstract Number:
2604
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Deepak Parashar (1), Wei He (2), Peter Mon (3), Matthew McCoy (2), Chen-Hsiang Yeang (4), Robert Beckman (2)
Institutions:
(1) University of Warwick, N/A, (2) Georgetown University, N/A, (3) Purdue University, N/A, (4) Academia Sinica, Teipei, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Current Precision Medicine (CPM) matches cancer therapies to consensus molecular characteristics at one or more timepoints. However, it is well-known that cancers contain extensive subclonal heterogeneity and that their subclonal compositions evolve dynamically in response to therapy. Mathematical modeling of this subclonal evolution has the potential to optimize the timing and sequencing of therapies in an even more effective and personalized manner than CPM. Clinical trial designs that test Evolutionary Guided Precision Medicine (EGPM) strategies that may prevent or delay relapse thereby improving outcomes, are needed. We evaluated Dynamic Precision Medicine (DPM), an EGPM, vs CPM in a stratified randomized design with two strata based on whether the patient was predicted to benefit from DPM, using an evolutionary classifier. We present this new proof-of-concept clinical trial design for this purpose and perform computer simulations which show high power, control of false positive rates, and robust performance in the face of anticipated challenges to clinical translation. The design is distinct from biomarker-driven designs of CPM, and can provide a robust evaluation of EGPM.
Keywords:
Precision Medicine|Clinical Trial Design|Tumour Evolution|Oncology| |
Sponsors:
Biopharmaceutical Section
Tracks:
Personalized/Precision Medicine
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