ArticleOncotarget2017
Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
18 citing papers in PubMed.
- Innovative strategies to overcome cancer drug resistance: from nanodelivery systems to targeted therapeutics.Discover oncology · 2026Review
- Identifying the Best Predictive Biomarker in Pharmacogenomics Through Multiple Comparisons With the Best.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Predicting atezolizumab response in metastatic urothelial carcinoma patients using machine learning on integrated tumour gene expression and clinical data.NPJ precision oncology · 2025Article
- Predicting the formation of NADES using a transformer-based model.Scientific reports · 2024Article
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer.Health data science · 2024Article
- Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Predicting Anticancer Drug Resistance Mediated by Mutations.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice.BMC bioinformatics · 2022Article
- Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.Biomedicines · 2021Article
- Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data.BMC bioinformatics · 2021Article
- Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.Biomolecules · 2020Article
- The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.BMC genomics · 2020Article
- KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images.Journal of cheminformatics · 2019Article
- Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data.Frontiers in genetics · 2019Article
- Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data.Frontiers in chemistry · 2019Article
- Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties.PloS one · 2019Article
- PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.Genome medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer drug therapies are only effective in a small proportion of patients. To make things worse, our ability to identify these responsive patients before administering a treatment is generally very limited. The recent arrival of large-scale pharmacogenomic data sets, which measure the sensitivity of molecularly profiled cancer cell lines to a panel of drugs, has boosted research on the discovery of drug sensitivity markers. However, no systematic comparison of widely-used single-gene markers with multi-gene machine-learning markers exploiting genomic data has been so far conducted. We therefore assessed the performance offered by these two types of models in discriminating between sensitive and resistant cell lines to a given drug. This was carried out for each of 127 considered drugs using genomic data characterising the cell lines. We found that the proportion of cell lines predicted to be sensitive that are actually sensitive (precision) varies strongly with the drug and type of model used. Furthermore, the proportion of sensitive cell lines that are correctly predicted as sensitive (recall) of the best single-gene marker was lower than that of the multi-gene marker in 118 of the 127 tested drugs. We conclude that single-gene markers are only able to identify those drug-sensitive cell lines with the considered actionable mutation, unlike multi-gene markers that can in principle combine multiple gene mutations to identify additional sensitive cell lines. We also found that cell line sensitivities to some drugs (e.g. Temsirolimus, 17-AAG or Methotrexate) are better predicted by these machine-learning models.
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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.