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Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer
BACKGROUND: It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. METHODS: 46 tumours from node-negative breast cancer patients were studied with ge...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559847/ https://www.ncbi.nlm.nih.gov/pubmed/18778486 http://dx.doi.org/10.1186/1471-2407-8-254 |
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author | Karlsson, Elin Delle, Ulla Danielsson, Anna Olsson, Björn Abel, Frida Karlsson, Per Helou, Khalil |
author_facet | Karlsson, Elin Delle, Ulla Danielsson, Anna Olsson, Björn Abel, Frida Karlsson, Per Helou, Khalil |
author_sort | Karlsson, Elin |
collection | PubMed |
description | BACKGROUND: It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. METHODS: 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. RESULTS: A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). CONCLUSION: The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved. TRIAL REGISTRATION: The research on these tumours was approved by the Medical Faculty Research Ethics Committee (Medicinska fakultetens forskningsetikkommitté, Göteborg, Sweden (S164-02)). |
format | Text |
id | pubmed-2559847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-25598472008-10-03 Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer Karlsson, Elin Delle, Ulla Danielsson, Anna Olsson, Björn Abel, Frida Karlsson, Per Helou, Khalil BMC Cancer Research Article BACKGROUND: It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. METHODS: 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. RESULTS: A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). CONCLUSION: The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved. TRIAL REGISTRATION: The research on these tumours was approved by the Medical Faculty Research Ethics Committee (Medicinska fakultetens forskningsetikkommitté, Göteborg, Sweden (S164-02)). BioMed Central 2008-09-08 /pmc/articles/PMC2559847/ /pubmed/18778486 http://dx.doi.org/10.1186/1471-2407-8-254 Text en Copyright © 2008 Karlsson et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Karlsson, Elin Delle, Ulla Danielsson, Anna Olsson, Björn Abel, Frida Karlsson, Per Helou, Khalil Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer |
title | Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer |
title_full | Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer |
title_fullStr | Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer |
title_full_unstemmed | Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer |
title_short | Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer |
title_sort | gene expression variation to predict 10-year survival in lymph-node-negative breast cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559847/ https://www.ncbi.nlm.nih.gov/pubmed/18778486 http://dx.doi.org/10.1186/1471-2407-8-254 |
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