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UNMASC: tumor-only variant calling with unmatched normal controls

Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations wi...

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Autores principales: Little, Paul, Jo, Heejoon, Hoyle, Alan, Mazul, Angela, Zhao, Xiaobei, Salazar, Ashley H, Farquhar, Douglas, Sheth, Siddharth, Masood, Maheer, Hayward, Michele C, Parker, Joel S, Hoadley, Katherine A, Zevallos, Jose, Hayes, D Neil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494212/
https://www.ncbi.nlm.nih.gov/pubmed/34632388
http://dx.doi.org/10.1093/narcan/zcab040
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author Little, Paul
Jo, Heejoon
Hoyle, Alan
Mazul, Angela
Zhao, Xiaobei
Salazar, Ashley H
Farquhar, Douglas
Sheth, Siddharth
Masood, Maheer
Hayward, Michele C
Parker, Joel S
Hoadley, Katherine A
Zevallos, Jose
Hayes, D Neil
author_facet Little, Paul
Jo, Heejoon
Hoyle, Alan
Mazul, Angela
Zhao, Xiaobei
Salazar, Ashley H
Farquhar, Douglas
Sheth, Siddharth
Masood, Maheer
Hayward, Michele C
Parker, Joel S
Hoadley, Katherine A
Zevallos, Jose
Hayes, D Neil
author_sort Little, Paul
collection PubMed
description Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data.
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spelling pubmed-84942122021-10-07 UNMASC: tumor-only variant calling with unmatched normal controls Little, Paul Jo, Heejoon Hoyle, Alan Mazul, Angela Zhao, Xiaobei Salazar, Ashley H Farquhar, Douglas Sheth, Siddharth Masood, Maheer Hayward, Michele C Parker, Joel S Hoadley, Katherine A Zevallos, Jose Hayes, D Neil NAR Cancer Cancer Computational Biology Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data. Oxford University Press 2021-10-06 /pmc/articles/PMC8494212/ /pubmed/34632388 http://dx.doi.org/10.1093/narcan/zcab040 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of NAR Cancer. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Computational Biology
Little, Paul
Jo, Heejoon
Hoyle, Alan
Mazul, Angela
Zhao, Xiaobei
Salazar, Ashley H
Farquhar, Douglas
Sheth, Siddharth
Masood, Maheer
Hayward, Michele C
Parker, Joel S
Hoadley, Katherine A
Zevallos, Jose
Hayes, D Neil
UNMASC: tumor-only variant calling with unmatched normal controls
title UNMASC: tumor-only variant calling with unmatched normal controls
title_full UNMASC: tumor-only variant calling with unmatched normal controls
title_fullStr UNMASC: tumor-only variant calling with unmatched normal controls
title_full_unstemmed UNMASC: tumor-only variant calling with unmatched normal controls
title_short UNMASC: tumor-only variant calling with unmatched normal controls
title_sort unmasc: tumor-only variant calling with unmatched normal controls
topic Cancer Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494212/
https://www.ncbi.nlm.nih.gov/pubmed/34632388
http://dx.doi.org/10.1093/narcan/zcab040
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