title: Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institut de Recherche Expérimentale et Clinique (IREC), Center for Applied Molecular Technologies (CTMA), Université catholique de Louvain, Clos chapelle-aux-champs B1.30.24, 1200 Brussels, Belgium.
authors.forename: Jerome
authors.initials: J
authors.lastname: Ambroise
authors.affiliation: Institut de Recherche Expérimentale et Clinique (IREC), Center for Applied Molecular Technologies (CTMA), Université catholique de Louvain, Clos chapelle-aux-champs B1.30.24, 1200 Brussels, Belgium.
authors.forename: Jamal
authors.initials: J
authors.lastname: Badir
authors.affiliation: Institut de Recherche Expérimentale et Clinique (IREC), Center for Applied Molecular Technologies (CTMA), Université catholique de Louvain, Clos chapelle-aux-champs B1.30.24, 1200 Brussels, Belgium.
authors.forename: Louise
authors.initials: L
authors.lastname: Nienhaus
authors.affiliation: Epidemiology and Biostatistics Department (EPID), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, 1200 Brussels, Belgium.
authors.forename: Annie
authors.initials: A
authors.lastname: Robert
authors.affiliation: Institut de Recherche Expérimentale et Clinique (IREC), Center for Applied Molecular Technologies (CTMA), Université catholique de Louvain, Clos chapelle-aux-champs B1.30.24, 1200 Brussels, Belgium.
authors.forename: Anne-France
authors.initials: AF
authors.lastname: Dekairelle
authors.affiliation: Institut de Recherche Expérimentale et Clinique (IREC), Center for Applied Molecular Technologies (CTMA), Université catholique de Louvain, Clos chapelle-aux-champs B1.30.24, 1200 Brussels, Belgium.
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Gala
keywords: AdvISER-PYRO-SMQ; Pyrosequencing; Sparse representation; somatic