A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.

Autor: Park S; Department of Medicine, University of California, San Diego, La Jolla, CA, USA., Silva E; Program in Biomedical Sciences, University of California, San Diego, La Jolla, CA, USA., Singhal A; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA., Kelly MR; Department of Medicine, University of California, San Diego, La Jolla, CA, USA.; Moores Cancer Center, University of California, San Diego, San Diego, CA, USA., Licon K; Department of Medicine, University of California, San Diego, La Jolla, CA, USA., Panagiotou I; Department of Medicine, University of California, San Diego, La Jolla, CA, USA., Fogg C; Department of Medicine, University of California, San Diego, La Jolla, CA, USA., Fong S; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA., Lee JJY; Department of Medicine, University of California, San Diego, La Jolla, CA, USA., Zhao X; Department of Medicine, University of California, San Diego, La Jolla, CA, USA., Bachelder R; Department of Medicine, University of California, San Diego, La Jolla, CA, USA., Parker BA; Department of Medicine, University of California, San Diego, La Jolla, CA, USA.; Moores Cancer Center, University of California, San Diego, San Diego, CA, USA., Yeung KT; Department of Medicine, University of California, San Diego, La Jolla, CA, USA.; Moores Cancer Center, University of California, San Diego, San Diego, CA, USA., Ideker T; Department of Medicine, University of California, San Diego, La Jolla, CA, USA. tideker@health.ucsd.edu.; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA. tideker@health.ucsd.edu.; Moores Cancer Center, University of California, San Diego, San Diego, CA, USA. tideker@health.ucsd.edu.; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. tideker@health.ucsd.edu.
Jazyk: angličtina
Zdroj: Nature cancer [Nat Cancer] 2024 Jul; Vol. 5 (7), pp. 996-1009. Date of Electronic Publication: 2024 Mar 05.
DOI: 10.1038/s43018-024-00740-1
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: