ArticleNature cancer2024
A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.
Article in Nature cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07227233 (Artificial Intelligence and Machine Learning-Enhanced Biomarker-dRiven CDK4/6 Inhibitor Rechallenge in HR+ HER2- Advanced Breast Tumors.), which is not on this map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence and Machine Learning-Enhanced Biomarker-dRiven CDK4/6 Inhibitor Rechallenge in HR+ HER2- Advanced Breast Tumors.
Who cites it
32 citing papers in PubMed, 45 citations in OpenAlex.
- Biologically-informed integration of drug representations for breast cancer treatment using deep learning.Nature communications · 2025Trial
- Identifying multigenic modules under selection in the tumor genome.Bioinformatics (Oxford, England) · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Simulation and empirical evaluation of biologically-informed neural network performance.Machine learning with applications · 2026Article
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- Targeting a super-enhancer induced aldehyde dehydrogenase metabolic loop mitigates CDK4/6 inhibitor resistance in estrogen receptor-positive cancers.Nature communications · 2026Article
- The ADAPT learning cancer treatment system: ARPA-H's initiative to revolutionize cancer therapy.Cancer cell · 2026Article
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.Frontiers in pharmacology · 2026Article
- AI-Guided Discovery of Oncogenic Signaling Crosstalk in Tumor Progression and Drug Resistance.Oncology research · 2026Review
- Using Biological Networks to Guide Biomedical Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Single-Cell Transcriptome Analysis Reveals IRF1-Driven Epithelial States and Glycosaminoglycan-Glycolysis Coupling in Cisplatin-Resistant HGSOC.Cancer informatics · 2026Article
- Drug resistance in cancer: molecular mechanisms and emerging treatment strategies.Molecular biomedicine · 2025Review
- Simulation and empirical evaluation of biologically-informed neural network performance.bioRxiv : the preprint server for biology · 2025Article
- Potential Loss of Imprinting of Tumor Suppressor Gene RB1 in Triple Negative Breast Cancer.Breast cancer research : BCR · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 1 country.
Funding
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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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.