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Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study
  • Timothy Qi,
  • Yanguang Cao
Timothy Qi
The University of North Carolina at Chapel Hill

Corresponding Author:[email protected]

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Yanguang Cao
The University of North Carolina at Chapel Hill
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Abstract

Background and Purpose: Despite their use to treat cancers with specific genetic aberrations, targeted therapies elicit heterogeneous responses. Sources of variability are critical to targeted therapy drug development, yet there exists no method to discern their relative contribution to response heterogeneity. Experimental Approach: We use HER2-amplified breast cancer and two agents, neratinib and lapatinib, to develop a platform for dissecting sources of variability in patient response. The platform comprises four components: pharmacokinetics, tumor burden and growth kinetics, clonal composition, and sensitivity to treatment. Pharmacokinetics are simulated using population models to capture variable systemic exposure. Tumor burden and growth kinetics are derived from clinical data comprising over 800,000 women. The fraction of sensitive and resistant tumor cells is informed by HER2 immunohistochemistry. Growth rate-corrected drug potency is used to predict response. We integrate these factors and simulate clinical outcomes for virtual patients. The relative contribution of these factors to response heterogeneity are compared. Key Results: The platform was verified with clinical data, including response rate and progression-free survival (PFS). For both neratinib and lapatinib, the growth rate of resistant clones influenced PFS to a higher degree than systemic drug exposure. Variability in exposure at labeled doses did not significantly influence response. Drug potency strongly influenced responses to neratinib. Variability in patient HER2 immunohistochemistry scores influenced responses to lapatinib. Exploratory twice daily dosing improved PFS for neratinib but not lapatinib. Conclusion and Implications: The platform can dissect sources of variability in response to target therapy, which may facilitate decision-making during drug development.