Abstract Trastuzumab Deruxtecan (T-DXd) has improved outcomes for HER2-positive cancers, yet intrinsic and acquired resistance present significant clinical hurdles. The mechanisms of resistance are diverse and incompletely understood, and optimal subsequent therapies for T-DXd-resistant patients remain undefined. To address this, we established a matched pair of patient-derived xenograft (PDX) models that recapitulate these critical clinical scenarios. Through in vivo efficacy screening of our HER2-positive PDX bank, we identified a unique model demonstrating de novo resistance to T-DXd, showing minimal tumor regression upon treatment. Separately, we generated a model of acquired resistance by subjecting an initially sensitive HER2-positive PDX to repeated cycles of T-DXd in vivo. This model recapitulated the clinical progression from initial response to treatment failure. This paired set of models provides a powerful, controlled system for a direct comparative analysis of resistance mechanisms. These well-characterized PDX models represent a critical resource for the oncology community. They enable the direct comparison of molecular mechanisms driving both intrinsic and treatment-induced resistance in a controlled, in vivo setting. This platform is immediately applicable for investigating alterations in HER2 biology, payload delivery, and bypass signaling pathways, and is ideally suited for evaluating novel therapeutic strategies aimed at overcoming T-DXd resistance. Citation Format: Hongyan Sun, Xiaoliu Yang, Shiying Guo, Yujing Zhang, Huixin Yang, Xiang Gao, . Modeling of de novo and experimentally induced acquired resistance to trastuzumab deruxtecan in HER2-positive patient-derived xenografts abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7495.
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Sun et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69d1fd62a79560c99a0a3520 — DOI: https://doi.org/10.1158/1538-7445.am2026-7495
Hongyan Sun
Xiaoliu Yang
Shiying Guo
Cancer Research
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