ArticleFrontiers in genetics2019
Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- An integrative multi-omics modeling framework for chemotherapy response prediction through microRNA-target regulatory signatures.Briefings in bioinformatics · 2026Article
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Predicting atezolizumab response in metastatic urothelial carcinoma patients using machine learning on integrated tumour gene expression and clinical data.NPJ precision oncology · 2025Article
- Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Comprehensive review and updated analysis of DNA methylation in hepatocellular carcinoma: From basic research to clinical application.Clinical and translational medicine · 2024Review
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.Journal of cheminformatics · 2024Article
- A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening.Cancers · 2024Article
- 2023 Beijing Health Data Science Summit.Health data science · 2024Article
- Effect of cyp1a1, cyp1b1 and cyp2c gene polymorphisms on doxorubicin and paclitaxel.Bioinformation · 2024Article
- Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer.Health data science · 2024Article
- A performance evaluation of drug response prediction models for individual drugs.Scientific reports · 2023Article
- AI-Aided Disease Prediction in Visualized Medicine.Advances in experimental medicine and biology · 2023Article
- A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.Frontiers in molecular biosciences · 2023Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- Genetic variations that influence paclitaxel pharmacokinetics and intracellular effects that may contribute to chemotherapy-induced neuropathy: A narrative review.Frontiers in pain research (Lausanne, Switzerland) · 2023Review
- Promises and challenges in pharmacoepigenetics.Cambridge prisms. Precision medicine · 2023Review
- Article
- 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
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute's Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.
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