Using a pharmacokinetic ctDNA shedding model to develop a biomarker of tumor response to targeted therapy

AaronLi

University of Minnesota

"Using a pharmacokinetic ctDNA shedding model to develop a biomarker of tumor response to targeted therapy"

Early prediction of response to therapy or lack thereof is essential for efficient treatment planning. Next-gen sequencing (NGS) has made it possible to non-invasively collect and sequence circulating tumor DNA (ctDNA) from longitudinal plasma samples. While ctDNA data continues to be collected on a wide variety of cancers and treatment types, it is still unclear what ctDNA biomarkers are most indicative of treatment success or failure. We present a pharmacokinetic model of ctDNA shedding under targeted therapy. Using this model to simulate a cohort of virtual patients, we demonstrate the predictive potential of a biomarker based on early ctDNA sampling. We compare the performance of the biomarker to that of a neural network classifier as well as existing ctDNA biomarkers. We show that our biomarker is able to match and exceed the performance of alternatives, both in terms of accuracy and earliness of prediction.
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