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MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning
Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was show...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072630/ https://www.ncbi.nlm.nih.gov/pubmed/33921709 http://dx.doi.org/10.3390/ijms22084217 |
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author | Nosi, Vladimir Luca, Alessandrì Milan, Melissa Arigoni, Maddalena Benvenuti, Silvia Cacchiarelli, Davide Cesana, Marcella Riccardo, Sara Di Filippo, Lucio Cordero, Francesca Beccuti, Marco Comoglio, Paolo M. Calogero, Raffaele A. |
author_facet | Nosi, Vladimir Luca, Alessandrì Milan, Melissa Arigoni, Maddalena Benvenuti, Silvia Cacchiarelli, Davide Cesana, Marcella Riccardo, Sara Di Filippo, Lucio Cordero, Francesca Beccuti, Marco Comoglio, Paolo M. Calogero, Raffaele A. |
author_sort | Nosi, Vladimir |
collection | PubMed |
description | Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. Conclusions: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool. |
format | Online Article Text |
id | pubmed-8072630 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80726302021-04-27 MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning Nosi, Vladimir Luca, Alessandrì Milan, Melissa Arigoni, Maddalena Benvenuti, Silvia Cacchiarelli, Davide Cesana, Marcella Riccardo, Sara Di Filippo, Lucio Cordero, Francesca Beccuti, Marco Comoglio, Paolo M. Calogero, Raffaele A. Int J Mol Sci Article Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. Conclusions: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool. MDPI 2021-04-19 /pmc/articles/PMC8072630/ /pubmed/33921709 http://dx.doi.org/10.3390/ijms22084217 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nosi, Vladimir Luca, Alessandrì Milan, Melissa Arigoni, Maddalena Benvenuti, Silvia Cacchiarelli, Davide Cesana, Marcella Riccardo, Sara Di Filippo, Lucio Cordero, Francesca Beccuti, Marco Comoglio, Paolo M. Calogero, Raffaele A. MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning |
title | MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning |
title_full | MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning |
title_fullStr | MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning |
title_full_unstemmed | MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning |
title_short | MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning |
title_sort | met exon 14 skipping: a case study for the detection of genetic variants in cancer driver genes by deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072630/ https://www.ncbi.nlm.nih.gov/pubmed/33921709 http://dx.doi.org/10.3390/ijms22084217 |
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