Predicting drug sensitivity of cancer cells based on DNA methylation levels
| العنوان: | Predicting drug sensitivity of cancer cells based on DNA methylation levels |
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| المؤلفون: | Julia L. Fleck, Sofia Pontes de Miranda, Stephen R. Piccolo, Fernanda Araujo Baião |
| المصدر: | PLoS ONE, Vol 16, Iss 9, p e0238757 (2021) PLoS ONE |
| بيانات النشر: | Public Library of Science (PLoS), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Cancer Treatment, Biochemistry, Infographics, Machine Learning, Mathematical and Statistical Techniques, Feature (machine learning), Medicine and Health Sciences, Data Management, Multidisciplinary, DNA methylation, Applied Mathematics, Simulation and Modeling, Statistics, Genomics, Chromatin, Random forest, Nucleic acids, Drug development, Oncology, Kernel (statistics), Physical Sciences, Regression Analysis, Medicine, Epigenetics, DNA modification, Graphs, Algorithms, Chromatin modification, Research Article, Chromosome biology, Computer and Information Sciences, Cell biology, Science, Antineoplastic Agents, Computational biology, Biology, Research and Analysis Methods, Human Genomics, Artificial Intelligence, medicine, Genetics, Humans, Statistical Methods, Biology and life sciences, Data Visualization, Cancer, Cancers and Neoplasms, DNA, medicine.disease, Statistical classification, Gene expression, Mathematics |
| الوصف: | Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas. |
| اللغة: | English |
| تدمد: | 1932-6203 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20d5a34a6b3517edc536b0cab2f6a458 https://doaj.org/article/cda66912ff464280b536bf53c158d2e1 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....20d5a34a6b3517edc536b0cab2f6a458 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19326203 |
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