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Classification of mismatch repair gene missense variants with PON-MMR

Ali, Heidi; Olatubosun, Ayodeji and Vihinen, Mauno LU (2012) In Human Mutation 33(4). p.642-650
Abstract
Numerous mismatch repair (MMR) gene variants have been identified in Lynch syndrome and other cancer patients, but knowledge about their pathogenicity is frequently missing. The diagnosis and treatment of patients would benefit from knowing which variants are disease related. Bioinformatic approaches are well suited to the problem and can handle large numbers of cases. Functional effects were revealed based on literature for 168 MMR missense variants. Performance of numerous prediction methods was tested with this dataset. Among the tested tools, only the results of tolerance prediction methods correlated to functional information, however, with poor performance. Therefore, a novel consensus-based predictor was developed. The novel... (More)
Numerous mismatch repair (MMR) gene variants have been identified in Lynch syndrome and other cancer patients, but knowledge about their pathogenicity is frequently missing. The diagnosis and treatment of patients would benefit from knowing which variants are disease related. Bioinformatic approaches are well suited to the problem and can handle large numbers of cases. Functional effects were revealed based on literature for 168 MMR missense variants. Performance of numerous prediction methods was tested with this dataset. Among the tested tools, only the results of tolerance prediction methods correlated to functional information, however, with poor performance. Therefore, a novel consensus-based predictor was developed. The novel prediction method, pathogenic-or-not mismatch repair (PON-MMR), achieved accuracy of 0.87 and Matthews correlation coefficient of 0.77 on the experimentally verified variants. When applied to 616 MMR cases with unknown effects, 81 missense variants were predicted to be pathogenic and 167 neutral. With PON-MMR, the number of MMR missense variants with unknown effect was reduced by classifying a large number of cases as likely pathogenic or benign. The results can be used, for example, to prioritize cases for experimental studies and assist in the classification of cases. Hum Mutat 33:642650, 2012. (c) 2012 Wiley Periodicals, Inc. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
bioinformatic prediction method, Lynch syndrome, colorectal cancer, genetic diagnostics
in
Human Mutation
volume
33
issue
4
pages
642 - 650
publisher
John Wiley & Sons
external identifiers
  • wos:000301338900010
  • scopus:84858205858
ISSN
1059-7794
DOI
10.1002/humu.22038
language
English
LU publication?
yes
id
b4d2190b-1a4b-4254-8df9-5a348ecfc990 (old id 2561837)
date added to LUP
2012-06-01 10:37:00
date last changed
2017-08-27 03:16:05
@article{b4d2190b-1a4b-4254-8df9-5a348ecfc990,
  abstract     = {Numerous mismatch repair (MMR) gene variants have been identified in Lynch syndrome and other cancer patients, but knowledge about their pathogenicity is frequently missing. The diagnosis and treatment of patients would benefit from knowing which variants are disease related. Bioinformatic approaches are well suited to the problem and can handle large numbers of cases. Functional effects were revealed based on literature for 168 MMR missense variants. Performance of numerous prediction methods was tested with this dataset. Among the tested tools, only the results of tolerance prediction methods correlated to functional information, however, with poor performance. Therefore, a novel consensus-based predictor was developed. The novel prediction method, pathogenic-or-not mismatch repair (PON-MMR), achieved accuracy of 0.87 and Matthews correlation coefficient of 0.77 on the experimentally verified variants. When applied to 616 MMR cases with unknown effects, 81 missense variants were predicted to be pathogenic and 167 neutral. With PON-MMR, the number of MMR missense variants with unknown effect was reduced by classifying a large number of cases as likely pathogenic or benign. The results can be used, for example, to prioritize cases for experimental studies and assist in the classification of cases. Hum Mutat 33:642650, 2012. (c) 2012 Wiley Periodicals, Inc.},
  author       = {Ali, Heidi and Olatubosun, Ayodeji and Vihinen, Mauno},
  issn         = {1059-7794},
  keyword      = {bioinformatic prediction method,Lynch syndrome,colorectal cancer,genetic diagnostics},
  language     = {eng},
  number       = {4},
  pages        = {642--650},
  publisher    = {John Wiley & Sons},
  series       = {Human Mutation},
  title        = {Classification of mismatch repair gene missense variants with PON-MMR},
  url          = {http://dx.doi.org/10.1002/humu.22038},
  volume       = {33},
  year         = {2012},
}