Cargando…

Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data

BACKGROUND: Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data te...

Descripción completa

Detalles Bibliográficos
Autores principales: Park, Sejin, Soh, Jihee, Lee, Hyunju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152321/
https://www.ncbi.nlm.nih.gov/pubmed/34034645
http://dx.doi.org/10.1186/s12859-021-04146-z
_version_ 1783698581549481984
author Park, Sejin
Soh, Jihee
Lee, Hyunju
author_facet Park, Sejin
Soh, Jihee
Lee, Hyunju
author_sort Park, Sejin
collection PubMed
description BACKGROUND: Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response. RESULTS: We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data. CONCLUSION: By separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at https://github.com/DMCB-GIST/Super.FELT. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04146-z.
format Online
Article
Text
id pubmed-8152321
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-81523212021-05-26 Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data Park, Sejin Soh, Jihee Lee, Hyunju BMC Bioinformatics Research BACKGROUND: Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response. RESULTS: We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data. CONCLUSION: By separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at https://github.com/DMCB-GIST/Super.FELT. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04146-z. BioMed Central 2021-05-25 /pmc/articles/PMC8152321/ /pubmed/34034645 http://dx.doi.org/10.1186/s12859-021-04146-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Park, Sejin
Soh, Jihee
Lee, Hyunju
Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data
title Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data
title_full Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data
title_fullStr Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data
title_full_unstemmed Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data
title_short Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data
title_sort super.felt: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152321/
https://www.ncbi.nlm.nih.gov/pubmed/34034645
http://dx.doi.org/10.1186/s12859-021-04146-z
work_keys_str_mv AT parksejin superfeltsupervisedfeatureextractionlearningusingtripletlossfordrugresponsepredictionwithmultiomicsdata
AT sohjihee superfeltsupervisedfeatureextractionlearningusingtripletlossfordrugresponsepredictionwithmultiomicsdata
AT leehyunju superfeltsupervisedfeatureextractionlearningusingtripletlossfordrugresponsepredictionwithmultiomicsdata