1. A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.
- Author
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Park, Sungjoon, Park, Sungjoon, Silva, Erica, Singhal, Akshat, Kelly, Marcus, Licon, Kate, Panagiotou, Isabella, Fogg, Catalina, Fong, Samson, Lee, John, Zhao, Xiaoyu, Bachelder, Robin, Parker, Barbara, Yeung, Kay, Ideker, Trey, Park, Sungjoon, Park, Sungjoon, Silva, Erica, Singhal, Akshat, Kelly, Marcus, Licon, Kate, Panagiotou, Isabella, Fogg, Catalina, Fong, Samson, Lee, John, Zhao, Xiaoyu, Bachelder, Robin, Parker, Barbara, Yeung, Kay, and Ideker, Trey
- 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 tumors genetic profile modulates CDK4/6i resistance.
- Published
- 2024