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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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