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DELFOS—drug efficacy leveraging forked and specialized networks—benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity
MOTIVATION: Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627353/ https://www.ncbi.nlm.nih.gov/pubmed/37862234 http://dx.doi.org/10.1093/bioinformatics/btad645 |
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author | Piochi, Luiz Felipe Preto, António J Moreira, Irina S |
author_facet | Piochi, Luiz Felipe Preto, António J Moreira, Irina S |
author_sort | Piochi, Luiz Felipe |
collection | PubMed |
description | MOTIVATION: Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can display severe side effects, compromising patient well-being. Therefore, novel strategies for identifying the optimal set of compounds to treat each tumor have become an important research topic in recent decades. RESULTS: To address this challenge, we developed a novel drug response prediction algorithm called Drug Efficacy Leveraging Forked and Specialized networks (DELFOS). Our model learns from multi-omics data from over 65 cancer cell lines, as well as structural data from over 200 compounds, for the prediction of drug sensitivity. We also evaluated the benefits of incorporating single-cell expression data to predict drug response. DELFOS was validated using datasets with unseen cell lines or drugs and compared with other state-of-the-art algorithms, achieving a high prediction performance on several correlation and error metrics. Overall, DELFOS can effectively leverage multi-omics data for the prediction of drug responses in thousands of drug–cell line pairs. AVAILABILITY AND IMPLEMENTATION: The DELFOS pipeline and associated data are available at github.com/MoreiraLAB/delfos. |
format | Online Article Text |
id | pubmed-10627353 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106273532023-11-07 DELFOS—drug efficacy leveraging forked and specialized networks—benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity Piochi, Luiz Felipe Preto, António J Moreira, Irina S Bioinformatics Original Paper MOTIVATION: Cancer is currently one of the most notorious diseases, with over 1 million deaths in the European Union alone in 2022. As each tumor can be composed of diverse cell types with distinct genotypes, cancer cells can acquire resistance to different compounds. Moreover, anticancer drugs can display severe side effects, compromising patient well-being. Therefore, novel strategies for identifying the optimal set of compounds to treat each tumor have become an important research topic in recent decades. RESULTS: To address this challenge, we developed a novel drug response prediction algorithm called Drug Efficacy Leveraging Forked and Specialized networks (DELFOS). Our model learns from multi-omics data from over 65 cancer cell lines, as well as structural data from over 200 compounds, for the prediction of drug sensitivity. We also evaluated the benefits of incorporating single-cell expression data to predict drug response. DELFOS was validated using datasets with unseen cell lines or drugs and compared with other state-of-the-art algorithms, achieving a high prediction performance on several correlation and error metrics. Overall, DELFOS can effectively leverage multi-omics data for the prediction of drug responses in thousands of drug–cell line pairs. AVAILABILITY AND IMPLEMENTATION: The DELFOS pipeline and associated data are available at github.com/MoreiraLAB/delfos. Oxford University Press 2023-10-20 /pmc/articles/PMC10627353/ /pubmed/37862234 http://dx.doi.org/10.1093/bioinformatics/btad645 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Piochi, Luiz Felipe Preto, António J Moreira, Irina S DELFOS—drug efficacy leveraging forked and specialized networks—benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity |
title | DELFOS—drug efficacy leveraging forked and specialized networks—benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity |
title_full | DELFOS—drug efficacy leveraging forked and specialized networks—benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity |
title_fullStr | DELFOS—drug efficacy leveraging forked and specialized networks—benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity |
title_full_unstemmed | DELFOS—drug efficacy leveraging forked and specialized networks—benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity |
title_short | DELFOS—drug efficacy leveraging forked and specialized networks—benchmarking scRNA-seq data in multi-omics-based prediction of cancer sensitivity |
title_sort | delfos—drug efficacy leveraging forked and specialized networks—benchmarking scrna-seq data in multi-omics-based prediction of cancer sensitivity |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627353/ https://www.ncbi.nlm.nih.gov/pubmed/37862234 http://dx.doi.org/10.1093/bioinformatics/btad645 |
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