Cargando…
Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows
BACKGROUND: The recent development and enormous application of parallel sequencing technology in oncology has produced immense amounts of cell-specific genetic information. However, publicly available cell-specific genetic variants are not explained by well-established guidelines. Additionally, cell...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036759/ https://www.ncbi.nlm.nih.gov/pubmed/35468810 http://dx.doi.org/10.1186/s12920-022-01235-7 |
_version_ | 1784693586110971904 |
---|---|
author | Sobahy, Turki M. Tashkandi, Ghassan Bahussain, Donya Al-Harbi, Raneem |
author_facet | Sobahy, Turki M. Tashkandi, Ghassan Bahussain, Donya Al-Harbi, Raneem |
author_sort | Sobahy, Turki M. |
collection | PubMed |
description | BACKGROUND: The recent development and enormous application of parallel sequencing technology in oncology has produced immense amounts of cell-specific genetic information. However, publicly available cell-specific genetic variants are not explained by well-established guidelines. Additionally, cell-specific variants interpretation and classification has remained a challenging task and lacks standardization. The Association for Molecular Pathology (AMP), the American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP) published the first consensus guidelines for cell-specific variants cataloging and clinical annotations. METHODS: AMP–ASCO–CAP recommended sources and information were downloaded and used as follows: relative knowledge in oncology clinical practice guidelines; approved, investigative or preclinical drugs; supporting literature and each gene-tumor site correlation. All information was homogenized into a single knowledgebase. Finally, we incorporated the consensus recommendations into a new computational method. RESULTS: A subset of cancer genetic variants was manually curated to benchmark our method and well-known computational algorithms. We applied the new method on freely available tumor-specific databases to produce a clinically actionable cancer somatic variants (CACSV) dataset in an easy-to-integrate format for most clinical analytical workflows. The research also showed the current challenges and limitations of using different classification systems or computational methods. CONCLUSION: CACSV is a step toward cell-specific genetic variants standardized interpretation as it is readily adaptable by most clinical laboratory pipelines for somatic variants clinical annotations. CACSV is freely accessible at (https://github.com/tsobahytm/CACSV/tree/main/dataset). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01235-7. |
format | Online Article Text |
id | pubmed-9036759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-90367592022-04-26 Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows Sobahy, Turki M. Tashkandi, Ghassan Bahussain, Donya Al-Harbi, Raneem BMC Med Genomics Technical Advance BACKGROUND: The recent development and enormous application of parallel sequencing technology in oncology has produced immense amounts of cell-specific genetic information. However, publicly available cell-specific genetic variants are not explained by well-established guidelines. Additionally, cell-specific variants interpretation and classification has remained a challenging task and lacks standardization. The Association for Molecular Pathology (AMP), the American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP) published the first consensus guidelines for cell-specific variants cataloging and clinical annotations. METHODS: AMP–ASCO–CAP recommended sources and information were downloaded and used as follows: relative knowledge in oncology clinical practice guidelines; approved, investigative or preclinical drugs; supporting literature and each gene-tumor site correlation. All information was homogenized into a single knowledgebase. Finally, we incorporated the consensus recommendations into a new computational method. RESULTS: A subset of cancer genetic variants was manually curated to benchmark our method and well-known computational algorithms. We applied the new method on freely available tumor-specific databases to produce a clinically actionable cancer somatic variants (CACSV) dataset in an easy-to-integrate format for most clinical analytical workflows. The research also showed the current challenges and limitations of using different classification systems or computational methods. CONCLUSION: CACSV is a step toward cell-specific genetic variants standardized interpretation as it is readily adaptable by most clinical laboratory pipelines for somatic variants clinical annotations. CACSV is freely accessible at (https://github.com/tsobahytm/CACSV/tree/main/dataset). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01235-7. BioMed Central 2022-04-25 /pmc/articles/PMC9036759/ /pubmed/35468810 http://dx.doi.org/10.1186/s12920-022-01235-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Technical Advance Sobahy, Turki M. Tashkandi, Ghassan Bahussain, Donya Al-Harbi, Raneem Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows |
title | Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows |
title_full | Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows |
title_fullStr | Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows |
title_full_unstemmed | Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows |
title_short | Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows |
title_sort | clinically actionable cancer somatic variants (cacsv): a tumor interpreted dataset for analytical workflows |
topic | Technical Advance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036759/ https://www.ncbi.nlm.nih.gov/pubmed/35468810 http://dx.doi.org/10.1186/s12920-022-01235-7 |
work_keys_str_mv | AT sobahyturkim clinicallyactionablecancersomaticvariantscacsvatumorinterpreteddatasetforanalyticalworkflows AT tashkandighassan clinicallyactionablecancersomaticvariantscacsvatumorinterpreteddatasetforanalyticalworkflows AT bahussaindonya clinicallyactionablecancersomaticvariantscacsvatumorinterpreteddatasetforanalyticalworkflows AT alharbiraneem clinicallyactionablecancersomaticvariantscacsvatumorinterpreteddatasetforanalyticalworkflows |