Cargando…

Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis

BACKGROUND: Pyrosequencing Allele Quantification (AQ) is a cost-effective DNA sequencing method that can be used for detecting somatic mutations in formalin-fixed paraffin-embedded (FFPE) samples. The method displays a low turnaround time and a high sensitivity. Pyrosequencing suffers however from t...

Descripción completa

Detalles Bibliográficos
Autores principales: Ambroise, Jerome, Badir, Jamal, Nienhaus, Louise, Robert, Annie, Dekairelle, Anne-France, Gala, Jean-Luc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5024468/
https://www.ncbi.nlm.nih.gov/pubmed/27651826
http://dx.doi.org/10.1186/s13015-016-0086-4
_version_ 1782453806234075136
author Ambroise, Jerome
Badir, Jamal
Nienhaus, Louise
Robert, Annie
Dekairelle, Anne-France
Gala, Jean-Luc
author_facet Ambroise, Jerome
Badir, Jamal
Nienhaus, Louise
Robert, Annie
Dekairelle, Anne-France
Gala, Jean-Luc
author_sort Ambroise, Jerome
collection PubMed
description BACKGROUND: Pyrosequencing Allele Quantification (AQ) is a cost-effective DNA sequencing method that can be used for detecting somatic mutations in formalin-fixed paraffin-embedded (FFPE) samples. The method displays a low turnaround time and a high sensitivity. Pyrosequencing suffers however from two main drawbacks including (i) low specificity and (ii) difficult signal interpretation when multiple mutations are reported in a hotspot genomic region. RESULTS: Using a constraint-based regression method, the new AdvISER-PYRO-SMQ algorithm was developed in the current study and implemented into an R package. As a proof-of-concept, AdvISER-PYRO-SMQ was used to identify a set of 9 distinct point mutations affecting codon 61 of the NRAS oncogene. In parallel, a pyrosequencing assay using the Qiagen software and its AQ module was used to assess selectively the presence of a single point mutation (NRAS[Formula: see text] - Q61R-1) among the set of codon 61 mutations, and to analyze related pyrosequencing signals. AdvISER-PYRO-SMQ produced a lower limit of blank (0 %) than the AQ module of Qiagen software (5.1 %) and similar limit of detection were obtained for both software (5.6 vs 4.8 %). AdvISER-PYRO-SMQ was able to screen for the presence of 9 distinct mutations with a single pyrosequencing reaction whereas the AQ module was limited to screen a single mutation per reaction. CONCLUSION: Using a constraint-based regression method enables to analyze pyrosequencing signal and to detect multiple mutations within a hotspot genomic region with an optimal compromise between sensitivity and specificity. The AdvISER-PYRO-SMQ R package provides a generic tool which can be applied on a wide range of somatic mutations. Its implementation in a Shiny web interactive application (available at https://ucl-irec-ctma.shinyapps.io/Pyrosequencing-NRAS-61/) enables its use in research or clinical routine applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13015-016-0086-4) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5024468
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-50244682016-09-20 Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis Ambroise, Jerome Badir, Jamal Nienhaus, Louise Robert, Annie Dekairelle, Anne-France Gala, Jean-Luc Algorithms Mol Biol Research BACKGROUND: Pyrosequencing Allele Quantification (AQ) is a cost-effective DNA sequencing method that can be used for detecting somatic mutations in formalin-fixed paraffin-embedded (FFPE) samples. The method displays a low turnaround time and a high sensitivity. Pyrosequencing suffers however from two main drawbacks including (i) low specificity and (ii) difficult signal interpretation when multiple mutations are reported in a hotspot genomic region. RESULTS: Using a constraint-based regression method, the new AdvISER-PYRO-SMQ algorithm was developed in the current study and implemented into an R package. As a proof-of-concept, AdvISER-PYRO-SMQ was used to identify a set of 9 distinct point mutations affecting codon 61 of the NRAS oncogene. In parallel, a pyrosequencing assay using the Qiagen software and its AQ module was used to assess selectively the presence of a single point mutation (NRAS[Formula: see text] - Q61R-1) among the set of codon 61 mutations, and to analyze related pyrosequencing signals. AdvISER-PYRO-SMQ produced a lower limit of blank (0 %) than the AQ module of Qiagen software (5.1 %) and similar limit of detection were obtained for both software (5.6 vs 4.8 %). AdvISER-PYRO-SMQ was able to screen for the presence of 9 distinct mutations with a single pyrosequencing reaction whereas the AQ module was limited to screen a single mutation per reaction. CONCLUSION: Using a constraint-based regression method enables to analyze pyrosequencing signal and to detect multiple mutations within a hotspot genomic region with an optimal compromise between sensitivity and specificity. The AdvISER-PYRO-SMQ R package provides a generic tool which can be applied on a wide range of somatic mutations. Its implementation in a Shiny web interactive application (available at https://ucl-irec-ctma.shinyapps.io/Pyrosequencing-NRAS-61/) enables its use in research or clinical routine applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13015-016-0086-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-15 /pmc/articles/PMC5024468/ /pubmed/27651826 http://dx.doi.org/10.1186/s13015-016-0086-4 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Ambroise, Jerome
Badir, Jamal
Nienhaus, Louise
Robert, Annie
Dekairelle, Anne-France
Gala, Jean-Luc
Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis
title Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis
title_full Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis
title_fullStr Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis
title_full_unstemmed Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis
title_short Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis
title_sort using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for nras analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5024468/
https://www.ncbi.nlm.nih.gov/pubmed/27651826
http://dx.doi.org/10.1186/s13015-016-0086-4
work_keys_str_mv AT ambroisejerome usingaconstraintbasedregressionmethodforrelativequantificationofsomaticmutationsinpyrosequencingsignalsacasefornrasanalysis
AT badirjamal usingaconstraintbasedregressionmethodforrelativequantificationofsomaticmutationsinpyrosequencingsignalsacasefornrasanalysis
AT nienhauslouise usingaconstraintbasedregressionmethodforrelativequantificationofsomaticmutationsinpyrosequencingsignalsacasefornrasanalysis
AT robertannie usingaconstraintbasedregressionmethodforrelativequantificationofsomaticmutationsinpyrosequencingsignalsacasefornrasanalysis
AT dekairelleannefrance usingaconstraintbasedregressionmethodforrelativequantificationofsomaticmutationsinpyrosequencingsignalsacasefornrasanalysis
AT galajeanluc usingaconstraintbasedregressionmethodforrelativequantificationofsomaticmutationsinpyrosequencingsignalsacasefornrasanalysis