ArticlePloS one2015
Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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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.
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Who cites it
22 citing papers in PubMed.
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
- AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.Current drug discovery technologies · 2025Review
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.Communications biology · 2023Article
- Clinical trial design in the era of precision medicine.Genome medicine · 2022Review
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- A novel HIF1α-STIL-FOXM1 axis regulates tumor metastasis.Journal of biomedical science · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review.The AAPS journal · 2022Review
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
- Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.The AAPS journal · 2021Article
- Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model.BMC bioinformatics · 2021Article
- Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors.Cancer informatics · 2021Article
- Semi-supervised Hierarchical Drug Embedding in Hyperbolic Space.Journal of chemical information and modeling · 2020Article
- DSPLMF: A Method for Cancer Drug Sensitivity Prediction Using a Novel Regularization Approach in Logistic Matrix Factorization.Frontiers in genetics · 2020Article
- An expanded landscape of human long noncoding RNA.Nucleic acids research · 2019Article
- Applications of machine learning in drug discovery and development.Nature reviews. Drug discovery · 2019Review
- Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response.Frontiers in genetics · 2019Article
- Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission.Nature communications · 2018Article
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Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug's known mechanism of action. Also, the models predict each drug's potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.
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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.