ArticleScientific reports2016
Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Tissue specificity of in vitro drug sensitivity.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
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- Advances in point-of-care genetic testing for personalized medicine applications.Biomicrofluidics · 2023Review
- Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.Frontiers in molecular biosciences · 2023Article
- Drug response prediction using graph representation learning and Laplacian feature selection.BMC bioinformatics · 2022Article
- Deep reinforcement learning for personalized treatment recommendation.Statistics in medicine · 2022Article
- Matching anticancer compounds and tumor cell lines by neural networks with ranking loss.NAR genomics and bioinformatics · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Liposome-Encapsulated Bioactive Guttiferone E Exhibits Anti-Inflammatory Effect in Lipopolysaccharide-Stimulated MH-S Macrophages and Cytotoxicity against Human Cancer Cells.Mediators of inflammation · 2022Article
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
- Artificial Intelligence for Precision Oncology.Advances in experimental medicine and biology · 2022Review
- Vincristine-Induced Peripheral Neuropathy in Childhood Acute Lymphoblastic Leukemia: Genetic Variation as a Potential Risk Factor.Frontiers in pharmacology · 2021Review
- Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- Network-based drug sensitivity prediction.BMC medical genomics · 2020Article
- A New Bioassay Platform Design for the Discovery of Small Molecules with Anticancer Immunotherapeutic Activity.Marine drugs · 2020Article
- Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.BMC bioinformatics · 2020Article
- Preparation of parenteral nanocrystal suspensions of etoposide from the excipient free dry state of the drug to enhance in vivo antitumoral properties.Scientific reports · 2020Article
- Machine learning approaches to drug response prediction: challenges and recent progress.NPJ precision oncology · 2020Review
- PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we investigated drug profile of 24 anticancer drugs tested against a large number of cell lines in order to understand the relation between drug resistance and altered genomic features of a cancer cell line. We detected frequent mutations, high expression and high copy number variations of certain genes in both drug resistant cell lines and sensitive cell lines. It was observed that a few drugs, like Panobinostat, are effective against almost all types of cell lines, whereas certain drugs are effective against only a limited type of cell lines. Tissue-specific preference of drugs was also seen where a drug is more effective against cell lines belonging to a specific tissue. Genomic features based models have been developed for each anticancer drug and achieved average correlation between predicted and actual growth inhibition of cell lines in the range of 0.43 to 0.78. We hope, our study will throw light in the field of personalized medicine, particularly in designing patient-specific anticancer drugs. In order to serve the scientific community, a webserver, CancerDP, has been developed for predicting priority/potency of an anticancer drug against a cancer cell line using its genomic features (http://crdd.osdd.net/raghava/cancerdp/).
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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.