ArticleNature communications2025
Learning and actioning general principles of cancer cell drug sensitivity.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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Who cites it
19 citing papers in PubMed.
- DiM2-DRP: Dual-Input Multi-View Dynamic Contrastive Learning Framework for Drug Response Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Short-Chain Oleanolic Acid Esters and Furoyl Hybrids: Pharmacological Prediction, ADMETox Profiling, In Vitro Cytotoxicity Evaluation, Antioxidant Testing and EGFR Docking.Pharmaceutics · 2026Article
- Review
- Pan-cancer prioritization of CCDC69 reveals an immune-enriched and therapeutically sensitive breast cancer phenotype.Discover oncology · 2026Article
- DISCERN: inferring drug sensitivity from single-cell transcriptomes using cell-type-specific genetic interaction networks.Genome medicine · 2026Article
- Development and validation of a cuproptosis-related lncRNA signature for predicting prognosis and immunotherapy response in breast cancer: a retrospective analysis of TCGA data.Translational cancer research · 2026Article
- Comprehensive review of publicly accessible databases for gastrointestinal cancer and future challenges in database construction.Discover oncology · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Targeting TRIM25 as a therapeutic strategy to enhance ferroptosis in glioblastoma cells.Journal of nanobiotechnology · 2026Article
- High-Resolution Laser Microdissection to Investigate Transcriptional States in Formalin-Fixed Paraffin-Embedded Samples.Methods in molecular biology (Clifton, N.J.) · 2026Article
- PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.Frontiers in pharmacology · 2026Article
- Challenges and opportunities for oncology drug repurposing informed by synthetic lethality.NPJ systems biology and applications · 2025Article
- Advances and Challenges in Drug Screening for Cancer Therapy: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2025Review
- Synthesis, characterization, docking, MD simulation, and evaluation of antiproliferative effectiveness of new 4-aminobenzophenone derivatives.Journal of computer-aided molecular design · 2025Article
- Multi-omics based and AI-driven drug repositioning for epigenetic therapy in female malignancies.Journal of translational medicine · 2025Review
- CellHit: a web server to predict and analyze cancer patients' drug responsiveness.Nucleic acids research · 2025Article
- A Transfer Learning Framework for Predicting and Interpreting Drug Responses via Single-Cell RNA-Seq Data.International journal of molecular sciences · 2025Article
- Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach.Discover oncology · 2025Article
- Learning and actioning general principles of cancer cell drug sensitivity.Nature communications · 2025Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
High-throughput screening of drug sensitivity of cancer cell lines (CCLs) holds the potential to unlock anti-tumor therapies. In this study, we leverage such datasets to predict drug response using cell line transcriptomics, focusing on models' interpretability and deployment on patients' data. We use large language models (LLMs) to match drug to mechanisms of action (MOA)-related pathways. Genes crucial for prediction are enriched in drug-MOAs, suggesting that our models learn the molecular determinants of response. Furthermore, by using only LLM-curated, MOA-genes, we enhance the predictive accuracy of our models. To enhance translatability, we align RNAseq data from CCLs, used for training, to those from patient samples, used for inference. We validated our approach on TCGA samples, where patients' best scoring drugs match those prescribed for their cancer type. We further predict and experimentally validate effective drugs for the patients of two highly lethal solid tumors, i.e., pancreatic cancer and glioblastoma.
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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.