建筑
抗性(生态学)
癌症研究
生物
计算生物学
神经科学
计算机科学
历史
生态学
考古
作者
Sungjoon Park,Erica Silva,Akshat Singhal,Marcus R. Kelly,Kate Licon,Ioannis Panagiotou,Catalina Fogg,Samson Fong,John J. Y. Lee,Xiaoyu Zhao,Robin E. Bachelder,Barbara A. Parker,Kay T. Yeung,Trey Ideker
标识
DOI:10.1038/s43018-024-00740-1
摘要
Abstract 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.
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