scholarly journals MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning

2021 ◽  
Author(s):  
Vladimir Nosi ◽  
Alessandrì Luca ◽  
Melissa Milan ◽  
Maddalena Arigoni ◽  
Silvia Benvenuti ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Detection Rate ◽  
Exon Skipping ◽  
Small Cell ◽  
Small Cell Lung ◽  
Driver Genes ◽  
Cancer Driver ◽  
Rnaseq Data

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.

scholarly journals MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning

2021 ◽  
Vol 22 (8) ◽  
pp. 4217
Author(s):  
Vladimir Nosi ◽  
Alessandrì Luca ◽  
Melissa Milan ◽  
Maddalena Arigoni ◽  
Silvia Benvenuti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Exon Skipping ◽  
Driver Genes ◽  
Cancer Driver ◽  
Rnaseq Data ◽  
The Neural Networks ◽  
Long Interspersed Nuclear Element ◽  
Mesenchymal Epithelial Transition

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.

scholarly journals The Fusion Gene Landscape in Taiwanese Patients with Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1343
Author(s):  
Ya-Sian Chang ◽  
Siang-Jyun Tu ◽  
Ju-Chen Yen ◽  
Ya-Ting Lee ◽  
Hsin-Yuan Fang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Fusion Gene ◽  
Small Cell ◽  
Gene Fusions ◽  
Fusion Genes ◽  
Rna Seq ◽  
Small Cell Lung ◽  
Driver Genes ◽  
Cancer Driver

Background: Analyzing fusion gene transcripts may yield an effective approach for selecting cancer treatments. However, few comprehensive analyses of fusions in non-small cell lung cancer (NSCLC) patients have been performed. Methods: We enrolled 54 patients with NSCLC, and performed RNA-sequencing (RNA-Seq). STAR (Spliced Transcripts Alignment to a Reference)-Fusion was used to identify fusions. Results: Of the 218 fusions discovered, 24 had been reported and the rest were novel. Three fusions had the highest occurrence rates. After integrating our gene expression and fusion data, we found that samples harboring fusions containing ASXL1, CACNA1A, EEF1A1, and RET also exhibited increased expression of these genes. We then searched for mutations and fusions in cancer driver genes in each sample and found that nine patients carried both mutations and fusions in cancer driver genes. Furthermore, we found a trend for mutual exclusivity between gene fusions and mutations in the same gene, with the exception of DMD, and we found that EGFR mutations are associated with the number of fusion genes. Finally, we identified kinase gene fusions, and potentially druggable fusions, which may play roles in lung cancer therapy. Conclusion: The clinical use of RNA-Seq for detecting driver fusion genes may play an important role in the treatment of lung cancer.

scholarly journals Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gabriel A. Colozza-Gama ◽  
Fabiano Callegari ◽  
Nikola Bešič ◽  
Ana C. de J. Paviza ◽  
Janete M. Cerutti
Keyword(s):  
Machine Learning ◽  
Sanger Sequencing ◽  
Learning Algorithm ◽  
Absolute Quantification ◽  
Braf V600e Mutation ◽  
Braf V600e ◽  
Driver Genes ◽  
Quantitative Classification ◽  
Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

scholarly journals Classification of Non-Small Cell Lung Cancer Based on Copy Number Alterations

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88300 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Tao Huang ◽  
Yu-Dong Cai
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Copy Number ◽  
Small Cell ◽  
Copy Number Alterations ◽  
Small Cell Lung

Molecular profiling of key driver genes improves staging accuracy in multifocal non–small cell lung cancer

2020 ◽  
Vol 160 (2) ◽  
pp. e71-e79 ◽  
Author(s):  
Richard Zheng ◽  
Qian Shen ◽  
Stacey Mardekian ◽  
Charalambos Solomides ◽  
Zi-Xuan Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Molecular Profiling ◽  
Small Cell ◽  
Small Cell Lung ◽  
Driver Genes ◽  
Staging Accuracy ◽  
Key Driver

Prognostic factors for survival in advanced non-small-cell lung cancer: univariate and multivariate analyses including recursive partitioning and amalgamation algorithms in 1,052 patients. The European Lung Cancer Working Party.

1995 ◽  
Vol 13 (5) ◽  
pp. 1221-1230 ◽  
Author(s):  
M Paesmans ◽  
J P Sculier ◽  
P Libert ◽  
G Bureau ◽  
G Dabouis ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prognostic Factors ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Cox Regression ◽  
Recursive Partitioning ◽  
Working Party ◽  
Small Cell ◽  
Small Cell Lung

PURPOSE This study attempted to determine the prognostic value for survival of various pretreatment characteristics in patients with nonresectable non-small-cell lung cancer in the context of more than 10 years of experience of a European Cooperative Group. PATIENTS AND METHODS We included in the analysis all eligible patients (N = 1,052) with advanced non-small-cell lung cancer registered onto one of seven trials conducted by the European Lung Cancer Working Party (ELCWP) during one decade. The patients were treated by chemotherapy regimens based on platinum derivatives. We prospectively collected 23 variables and analyzed them by univariate and multivariate methods. RESULTS The global estimated median survival time was 29 weeks, with a 95% confidence interval of 27 to 30 weeks. After univariate analysis, we applied two multivariate statistical techniques. In a Cox regression model, the selected explanatory variables were disease extent, Karnofsky performance status, WBC and neutrophil counts, metastatic involvement of skin, serum calcium level, age, and sex. These results were confirmed by application of recursive partitioning and amalgamation algorithms (RECPAM), which led to classification of the patients into four homogeneous subgroups. CONCLUSION We confirmed by our analysis the role of well-known independent prognostic factors for survival, but also identified the effect of the neutrophil count, rarely studied, with the use of two methods: a classical Cox regression model and a RECPAM analysis. The classification of patients into the four subgroups we obtained needs to be validated in other series.

scholarly journals The immune response‐related mutational signatures and driver genes in non‐small‐cell lung cancer

2019 ◽  
Vol 110 (8) ◽  
pp. 2348-2356 ◽  
Author(s):  
Hao Chen ◽  
Wei Chong ◽  
Changcai Teng ◽  
Yueliang Yao ◽  
Xin Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Response ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutational Signatures ◽  
Driver Genes
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