Nosi, V.; Luca, A.; Milan, M.; Arigoni, M.; Benvenuti, S.; Cacchiarelli, D.; Cesana, M.; Riccardo, S.; Di Filippo, L.; Cordero, F.; Beccuti, M.; Comoglio, P.M.; Calogero, R.A. MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning. Int. J. Mol. Sci.2021, 22, 4217.
Nosi, V.; Luca, A.; Milan, M.; Arigoni, M.; Benvenuti, S.; Cacchiarelli, D.; Cesana, M.; Riccardo, S.; Di Filippo, L.; Cordero, F.; Beccuti, M.; Comoglio, P.M.; Calogero, R.A. MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning. Int. J. Mol. Sci. 2021, 22, 4217.
Nosi, V.; Luca, A.; Milan, M.; Arigoni, M.; Benvenuti, S.; Cacchiarelli, D.; Cesana, M.; Riccardo, S.; Di Filippo, L.; Cordero, F.; Beccuti, M.; Comoglio, P.M.; Calogero, R.A. MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning. Int. J. Mol. Sci.2021, 22, 4217.
Nosi, V.; Luca, A.; Milan, M.; Arigoni, M.; Benvenuti, S.; Cacchiarelli, D.; Cesana, M.; Riccardo, S.; Di Filippo, L.; Cordero, F.; Beccuti, M.; Comoglio, P.M.; Calogero, R.A. MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning. Int. J. Mol. Sci. 2021, 22, 4217.
Abstract
Background: Disruption of alternative splicing (AS) is frequently observed in cancer and it 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 a neural network (NN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purpose we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The NN had 100% Met exon 14 skipping detection rate, 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 interesting they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1-MET 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.
Keywords
Neural network; MET; exon skipping
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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