大多数靶向抗癌疗法因耐药性进化而失败。在这里，我们表明，无论预先存在的遗传异质性的确切集合如何，肿瘤进化都可以可重复地重新定向以设计治疗机会。我们开发了一种选择基因驱动系统，该系统被稳定地引入癌细胞中，并由两个基因或开关组成，将可诱导的适应性优势与共享的适应性成本结合起来。使用进化动力学的随机模型，我们确定了选择基因驱动的设计标准。然后，我们构建了原型，利用多种已批准的酪氨酸激酶抑制剂的选择压力，并采用前药催化和免疫活性诱导等多种治疗机制。我们证明选择基因驱动可以在体外消除多种形式的遗传抗性。最后，我们证明模型知情的开关参与有效地针对实体瘤小鼠模型中预先存在的耐药性。这些结果将选择基因驱动确立为进化引导抗癌治疗的强大框架。© 2024。作者获得 Springer Nature America, Inc. 的独家许可。

Most targeted anticancer therapies fail due to drug resistance evolution. Here we show that tumor evolution can be reproducibly redirected to engineer therapeutic opportunity, regardless of the exact ensemble of pre-existing genetic heterogeneity. We develop a selection gene drive system that is stably introduced into cancer cells and is composed of two genes, or switches, that couple an inducible fitness advantage with a shared fitness cost. Using stochastic models of evolutionary dynamics, we identify the design criteria for selection gene drives. We then build prototypes that harness the selective pressure of multiple approved tyrosine kinase inhibitors and employ therapeutic mechanisms as diverse as prodrug catalysis and immune activity induction. We show that selection gene drives can eradicate diverse forms of genetic resistance in vitro. Finally, we demonstrate that model-informed switch engagement effectively targets pre-existing resistance in mouse models of solid tumors. These results establish selection gene drives as a powerful framework for evolution-guided anticancer therapy.© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.