Ontology type: schema:ScholarlyArticle Open Access: True
2019-12
AUTHORSHosub Park, Sung-Min Chun, Jooyong Shim, Ji-Hye Oh, Eun Jeong Cho, Hee Sang Hwang, Ji-Young Lee, Deokhoon Kim, Jin Jang, Soo Jeong Nam, Changha Hwang, Insuk Sohn, Chang Ohk Sung
ABSTRACTMolecular testing is increasingly important in cancer diagnosis. Targeted next generation sequencing (NGS) is widely accepted method but structural variation (SV) detection by targeted NGS remains challenging. In the brain tumor, identification of molecular alterations, including 1p/19q co-deletion, is essential for accurate glial tumor classification. Hence, we used targeted NGS to detect 1p/19q co-deletion using a newly developed deep learning (DL) model in 61 tumors, including 19 oligodendroglial tumors. An ensemble 1-dimentional convolution neural network was developed and used to detect the 1p/19q co-deletion. External validation was performed using 427 low-grade glial tumors from The Cancer Genome Atlas (TCGA). Manual review of the copy number plot from the targeted NGS identified the 1p/19q co-deletion in all 19 oligodendroglial tumors. Our DL model also perfectly detected the 1p/19q co-deletion (area under the curve, AUC = 1) in the testing set, and yielded reproducible results (AUC = 0.9652) in the validation set (n = 427), although the validation data were generated on a completely different platform (SNP Array 6.0 platform). In conclusion, targeted NGS using a cancer gene panel is a promising approach for classifying glial tumors, and DL can be successfully integrated for the SV detection in NGS data. More... »
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DOIhttp://dx.doi.org/10.1038/s41598-019-40364-5
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