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Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?
A large number of prognostic and predictive signatures have been proposed for breast cancer and a few of these are now available in the clinic as new molecular diagnostic tests. However, several other signatures have not fared well in validation studies. Some investigators continue to be puzzled by...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016623/ https://www.ncbi.nlm.nih.gov/pubmed/21092148 http://dx.doi.org/10.1186/gm202 |
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author | Iwamoto, Takayuki Pusztai, Lajos |
author_facet | Iwamoto, Takayuki Pusztai, Lajos |
author_sort | Iwamoto, Takayuki |
collection | PubMed |
description | A large number of prognostic and predictive signatures have been proposed for breast cancer and a few of these are now available in the clinic as new molecular diagnostic tests. However, several other signatures have not fared well in validation studies. Some investigators continue to be puzzled by the diversity of signatures that are being developed for the same purpose but that share few or no common genes. The history of empirical development of prognostic gene signatures and the unique association between molecular subsets and clinical phenotypes of breast cancer explain many of these apparent contradictions in the literature. Three features of breast cancer gene expression contribute to this: the large number of individually prognostic genes (differentially expressed between good and bad prognosis cases); the unstable rankings of differentially expressed genes between datasets; and the highly correlated expression of informative genes. |
format | Text |
id | pubmed-3016623 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30166232011-11-12 Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? Iwamoto, Takayuki Pusztai, Lajos Genome Med Commentary A large number of prognostic and predictive signatures have been proposed for breast cancer and a few of these are now available in the clinic as new molecular diagnostic tests. However, several other signatures have not fared well in validation studies. Some investigators continue to be puzzled by the diversity of signatures that are being developed for the same purpose but that share few or no common genes. The history of empirical development of prognostic gene signatures and the unique association between molecular subsets and clinical phenotypes of breast cancer explain many of these apparent contradictions in the literature. Three features of breast cancer gene expression contribute to this: the large number of individually prognostic genes (differentially expressed between good and bad prognosis cases); the unstable rankings of differentially expressed genes between datasets; and the highly correlated expression of informative genes. BioMed Central 2010-11-12 /pmc/articles/PMC3016623/ /pubmed/21092148 http://dx.doi.org/10.1186/gm202 Text en Copyright ©2010 BioMed Central Ltd. |
spellingShingle | Commentary Iwamoto, Takayuki Pusztai, Lajos Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? |
title | Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? |
title_full | Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? |
title_fullStr | Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? |
title_full_unstemmed | Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? |
title_short | Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? |
title_sort | predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data? |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016623/ https://www.ncbi.nlm.nih.gov/pubmed/21092148 http://dx.doi.org/10.1186/gm202 |
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