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Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer
Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia I...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6365517/ https://www.ncbi.nlm.nih.gov/pubmed/30728399 http://dx.doi.org/10.1038/s41598-018-37574-8 |
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author | Kaur, Pushpinder Porras, Tania B. Ring, Alexander Carpten, John D. Lang, Julie E. |
author_facet | Kaur, Pushpinder Porras, Tania B. Ring, Alexander Carpten, John D. Lang, Julie E. |
author_sort | Kaur, Pushpinder |
collection | PubMed |
description | Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) breast cancer genomic datasets (array and next generation sequencing (NGS) data) in detecting genomic alterations in clinically relevant genes. We performed an in silico analysis to determine the concordance in the frequencies of actionable mutations and copy number alterations/aberrations (CNAs) in the two most common breast cancer histologies, invasive lobular and invasive ductal carcinoma. We found that targeted sequencing identified a larger number of mutational hotspots and clinically significant amplifications that would have been missed by WES and SNP arrays in many actionable genes such as PIK3CA, EGFR, AKT3, FGFR1, ERBB2, ERBB3 and ESR1. The striking differences between the number of mutational hotspots and CNAs generated from these platforms highlight a number of factors that should be considered in the interpretation of array and NGS-based genomic data for precision medicine. Targeted panel sequencing was preferable to WES to define the full spectrum of somatic mutations present in a tumor. |
format | Online Article Text |
id | pubmed-6365517 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63655172019-02-08 Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer Kaur, Pushpinder Porras, Tania B. Ring, Alexander Carpten, John D. Lang, Julie E. Sci Rep Article Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) breast cancer genomic datasets (array and next generation sequencing (NGS) data) in detecting genomic alterations in clinically relevant genes. We performed an in silico analysis to determine the concordance in the frequencies of actionable mutations and copy number alterations/aberrations (CNAs) in the two most common breast cancer histologies, invasive lobular and invasive ductal carcinoma. We found that targeted sequencing identified a larger number of mutational hotspots and clinically significant amplifications that would have been missed by WES and SNP arrays in many actionable genes such as PIK3CA, EGFR, AKT3, FGFR1, ERBB2, ERBB3 and ESR1. The striking differences between the number of mutational hotspots and CNAs generated from these platforms highlight a number of factors that should be considered in the interpretation of array and NGS-based genomic data for precision medicine. Targeted panel sequencing was preferable to WES to define the full spectrum of somatic mutations present in a tumor. Nature Publishing Group UK 2019-02-06 /pmc/articles/PMC6365517/ /pubmed/30728399 http://dx.doi.org/10.1038/s41598-018-37574-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kaur, Pushpinder Porras, Tania B. Ring, Alexander Carpten, John D. Lang, Julie E. Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer |
title | Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer |
title_full | Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer |
title_fullStr | Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer |
title_full_unstemmed | Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer |
title_short | Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer |
title_sort | comparison of tcga and genie genomic datasets for the detection of clinically actionable alterations in breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6365517/ https://www.ncbi.nlm.nih.gov/pubmed/30728399 http://dx.doi.org/10.1038/s41598-018-37574-8 |
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