Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR–Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumor’s genetic profile modulates CDK4/6i resistance.

细胞周期蛋白依赖性激酶 4 和 6 抑制剂 (CDK4/6is) 彻底改变了乳腺癌治疗。然而，<50% 的患者有客观反应，并且几乎所有患者在治疗期间都会产生耐药性。为了阐明潜在机制，我们基于癌症中多蛋白组装的参考图构建了一个可解释的深度学习模型，用于描述对 palbociclib（一种 CDK4/6i）的反应。该模型确定了 8 个核心组件，这些组件整合了 90 个基因中罕见和常见的改变，以对 palbociclib 敏感和 palbociclib 耐药细胞系进行分层。预测可以转化为患者和患者来源的异种移植物，而单基因生物标志物则不能。大多数预测组装可以通过 CRISPR-Cas9 基因破坏来调节 CDK4/6i 反应来显示。经过验证的组件与细胞周期控制、生长因子信号传导和组蛋白调节复合物有关，我们证明该复合物通过激活组蛋白修饰剂 KAT6A 和 TBL1XR1 以及转录因子 RUNX1 来促进进入 S 期。这项研究能够对肿瘤的基因谱如何调节 CDK4/6i 耐药性进行综合评估。