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Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome Wide Expression Data: A Validation Study.

Lauss, Martin LU ; Ringnér, Markus LU and Höglund, Mattias LU (2010) In Clinical Cancer Research 16(17). p.4421-4433
Abstract
PURPOSE: To evaluate performances of published gene signatures for the assessment of urothelial carcinoma. EXPERIMENTAL DESIGN: We evaluated 28 published gene signatures designed for diagnostic and prognostic purposes of urothelial cancer. The investigated signatures include eight signatures for stage, five for grade, four for progression, and six for survival. We used two algorithms for classification, nearest centroid classification and support vector machine, and Cox regression to evaluate signature performance in four independent data sets. RESULTS: The overlap of genes among the signatures was low, ranging from 11% among stage signatures to 0.6% among survival signatures. The published signatures predicted muscle-invasive and... (More)
PURPOSE: To evaluate performances of published gene signatures for the assessment of urothelial carcinoma. EXPERIMENTAL DESIGN: We evaluated 28 published gene signatures designed for diagnostic and prognostic purposes of urothelial cancer. The investigated signatures include eight signatures for stage, five for grade, four for progression, and six for survival. We used two algorithms for classification, nearest centroid classification and support vector machine, and Cox regression to evaluate signature performance in four independent data sets. RESULTS: The overlap of genes among the signatures was low, ranging from 11% among stage signatures to 0.6% among survival signatures. The published signatures predicted muscle-invasive and high-grade tumors with accuracies in the range of 70% to 90%. The performance for a given signature varied considerably with the validation data set used, and interestingly, some of the best performing signatures were not designed for the tested classification problem. In addition, several nonbladder-derived gene signatures performed equally well. Large randomly selected gene signatures performed better than the published signatures, and by systematically increasing signature size, we show that signatures with >150 genes are needed to obtain robust performance in independent validation data sets. None of the published survival signatures performed better than random assignments when applied to independent validation data. CONCLUSION: We conclude that gene expression signatures with >150 genes predict muscle-invasive growth and high-grade tumors with robust accuracies. Special considerations have to be taken when designing gene signatures for outcome in bladder cancer. Clin Cancer Res; 16(17); 4421-33. (c)2010 AACR. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Clinical Cancer Research
volume
16
issue
17
pages
4421 - 4433
publisher
American Association for Cancer Research
external identifiers
  • wos:000281444700016
  • pmid:20736328
  • scopus:77956235993
ISSN
1078-0432
DOI
10.1158/1078-0432.CCR-10-0606
project
CREATE Health
language
English
LU publication?
yes
id
aa12a970-c321-4bd9-b0d7-084a33a316d2 (old id 1665040)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/20736328?dopt=Abstract
date added to LUP
2010-09-03 11:28:00
date last changed
2018-07-15 04:17:34
@article{aa12a970-c321-4bd9-b0d7-084a33a316d2,
  abstract     = {PURPOSE: To evaluate performances of published gene signatures for the assessment of urothelial carcinoma. EXPERIMENTAL DESIGN: We evaluated 28 published gene signatures designed for diagnostic and prognostic purposes of urothelial cancer. The investigated signatures include eight signatures for stage, five for grade, four for progression, and six for survival. We used two algorithms for classification, nearest centroid classification and support vector machine, and Cox regression to evaluate signature performance in four independent data sets. RESULTS: The overlap of genes among the signatures was low, ranging from 11% among stage signatures to 0.6% among survival signatures. The published signatures predicted muscle-invasive and high-grade tumors with accuracies in the range of 70% to 90%. The performance for a given signature varied considerably with the validation data set used, and interestingly, some of the best performing signatures were not designed for the tested classification problem. In addition, several nonbladder-derived gene signatures performed equally well. Large randomly selected gene signatures performed better than the published signatures, and by systematically increasing signature size, we show that signatures with >150 genes are needed to obtain robust performance in independent validation data sets. None of the published survival signatures performed better than random assignments when applied to independent validation data. CONCLUSION: We conclude that gene expression signatures with >150 genes predict muscle-invasive growth and high-grade tumors with robust accuracies. Special considerations have to be taken when designing gene signatures for outcome in bladder cancer. Clin Cancer Res; 16(17); 4421-33. (c)2010 AACR.},
  author       = {Lauss, Martin and Ringnér, Markus and Höglund, Mattias},
  issn         = {1078-0432},
  language     = {eng},
  number       = {17},
  pages        = {4421--4433},
  publisher    = {American Association for Cancer Research},
  series       = {Clinical Cancer Research},
  title        = {Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome Wide Expression Data: A Validation Study.},
  url          = {http://dx.doi.org/10.1158/1078-0432.CCR-10-0606},
  volume       = {16},
  year         = {2010},
}