ArticleBiomedicines2021
Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.
Article in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Ferroptosis-related ceRNA axis regulates the apoptosis and proliferation of uveal melanoma cells through MAPRE2.Functional & integrative genomics · 2026Article
- A pan-cancer, pan-treatment model for predicting drug responses from patient-derived xenografts.NAR genomics and bioinformatics · 2025Article
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Review
- Predicting atezolizumab response in metastatic urothelial carcinoma patients using machine learning on integrated tumour gene expression and clinical data.NPJ precision oncology · 2025Article
- Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research.Current issues in molecular biology · 2025Review
- Ligand-Based Approach for Multi-Target Drug Discovery: PTML Modeling of Triple-Target Inhibitors.Current topics in medicinal chemistry · 2025Article
- An innovative prognostic auxiliary for colon adenocarcinoma based on zinc finger protein genes.Translational cancer research · 2024Article
- Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer.Health data science · 2024Article
- New Therapeutic Perspectives in Prostate Cancer: Patient-Derived Organoids and Patient-Derived Xenograft Models in Precision Medicine.Biomedicines · 2023Review
- Radiation Sensitivity: The Rise of Predictive Patient-Derived Cancer Models.Seminars in radiation oncology · 2023Review
- Ten-year distant-recurrence risk prediction in breast cancer by CanAssist Breast (CAB) in Dutch sub-cohort of the randomized TEAM trial.Breast cancer research : BCR · 2023Article
- A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.Frontiers in molecular biosciences · 2023Article
- Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images.Frontiers in medicine · 2023Article
- Tissue-specific identification of multi-omics features for pan-cancer drug response prediction.iScience · 2022Article
- 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
- A novel epithelial-mesenchymal transition (EMT)-related gene signature of predictive value for the survival outcomes in lung adenocarcinoma.Frontiers in oncology · 2022Article
- Patient-derived cancer models: Valuable platforms for anticancer drug testing.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Inter-tumour heterogeneity is one of cancer's most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.
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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.