ArticleBMC genomics2021
Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines.
Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed, 70 citations in OpenAlex.
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- Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.Scientific reports · 2025Article
- Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates.Brain sciences · 2025Article
- Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition.Biology · 2025Article
- Ancestral Differences in Anticancer Treatment Efficacy and Their Underlying Genomic and Molecular Alterations.Cancer discovery · 2025Article
- Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Comparison of baseline global gene expression profiles of prostate cancer cell lines LNCaP and DU145.BMC research notes · 2024Article
- Design, Synthesis, and Biological Evaluation of Novel Urea-Containing Carnosic Acid Derivatives with Anticancer Activity.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundHuman cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients' care. Tremendous progress has been made.
resultsIn this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC
conclusionsWe demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.
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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.