ArticleF1000Research2016
Revisiting inconsistency in large pharmacogenomic studies.
Article in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
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Who cites it
66 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
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Harmonization and integration of pharmacogenomics screens.Bioinformatics (Oxford, England) · 2026Article
- Towards convergence of AI and blockchain for personalized medicine in pharmacogenomics.Scientific reports · 2026Article
- Phenotypic AI-based design of cell-specific small molecule cytotoxics.Communications chemistry · 2026Article
- Monotherapy cancer drug-blind response prediction is limited to intraclass generalization.PLoS computational biology · 2026Article
- Decoding FGFR inhibitor sensitivity in cholangiocarcinoma with interpretable machine learning and cross-platform pharmacogenomic validation.Frontiers in pharmacology · 2026Article
- BiDRA: Bayesian inference as a robust alternative to non-linear regression for dose-response efficiency metrics assessment.Bioinformatics advances · 2026Article
- Global Proteomic Determination of the Poly-Pharmacological Effects of PARP Inhibitors Following Treatment of High-Grade Serous Ovarian Cancer Cells.International journal of molecular sciences · 2025Article
- Organoid-based two-step drug screening for rapid identification of chemotherapy-resistant oesophageal squamous cell carcinoma and alternative therapies.Clinical and translational medicine · 2025Article
- Understanding the sources of performance in deep drug response models reveals insights and improvements.Bioinformatics (Oxford, England) · 2025Article
- Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization.Briefings in bioinformatics · 2025Article
- Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma.PloS one · 2024Article
- Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling.Cell reports methods · 2023Article
- Reassessing pharmacogenomic cell sensitivity with multilevel statistical models.Biostatistics (Oxford, England) · 2023Article
- Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.ACS pharmacology & translational science · 2023Article
- CREAMMIST: an integrative probabilistic database for cancer drug response prediction.Nucleic acids research · 2023Article
- Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.Frontiers in molecular biosciences · 2023Article
- Increased copy number of imprinted genes in the chromosomal region 20q11-q13.32 is associated with resistance to antitumor agents in cancer cell lines.Clinical epigenetics · 2022Article
6 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
Abstract
In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.
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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.