A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.
Nat Cancer
; 5(7): 996-1009, 2024 Jul.
Article
in En
| MEDLINE
| ID: mdl-38443662
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Piperazines
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Pyridines
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Drug Resistance, Neoplasm
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Protein Kinase Inhibitors
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Cyclin-Dependent Kinase 4
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Cyclin-Dependent Kinase 6
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Deep Learning
Limits:
Animals
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Female
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Humans
Language:
En
Journal:
Nat Cancer
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: