Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data
Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2014-06-01
|
| Series: | Journal of Integrative Bioinformatics |
| Online Access: | https://doi.org/10.1515/jib-2014-236 |
| _version_ | 1818720574123278336 |
|---|---|
| author | List Markus Hauschild Anne-Christin Tan Qihua Kruse Torben A. Baumbach Jan Batra Richa |
| author_facet | List Markus Hauschild Anne-Christin Tan Qihua Kruse Torben A. Baumbach Jan Batra Richa |
| author_sort | List Markus |
| collection | DOAJ |
| description | Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level. |
| first_indexed | 2024-12-17T20:25:00Z |
| format | Article |
| id | doaj.art-53f0c2ffd4914a8b9cfa6ffd0cf68b7f |
| institution | Directory Open Access Journal |
| issn | 1613-4516 |
| language | English |
| last_indexed | 2024-12-17T20:25:00Z |
| publishDate | 2014-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Integrative Bioinformatics |
| spelling | doaj.art-53f0c2ffd4914a8b9cfa6ffd0cf68b7f2022-12-21T21:33:49ZengDe GruyterJournal of Integrative Bioinformatics1613-45162014-06-0111211410.1515/jib-2014-236jib-2014-236Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation DataList Markus0Hauschild Anne-Christin1Tan Qihua2Kruse Torben A.3Baumbach Jan4Batra Richa5Lundbeckfonden Center of Excellence in Nanomedicine (NanoCAN), University of Southern Denmark, 5000Odense, DenmarkComputational Systems Biology Group, Max Planck Institute for Informatics, 66123Saarbrücken, GermanyClinical Institute, University of Southern Denmark, 5000Odense, DenmarkLundbeckfonden Center of Excellence in Nanomedicine (NanoCAN), University of Southern Denmark, 5000Odense, DenmarkDepartment of Mathematics and Computer Science (IMADA), University of Southern Denmark, 5000Odense, DenmarkDepartment of Mathematics and Computer Science (IMADA), University of Southern Denmark, 5000Odense, DenmarkSelecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.https://doi.org/10.1515/jib-2014-236 |
| spellingShingle | List Markus Hauschild Anne-Christin Tan Qihua Kruse Torben A. Baumbach Jan Batra Richa Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data Journal of Integrative Bioinformatics |
| title | Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data |
| title_full | Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data |
| title_fullStr | Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data |
| title_full_unstemmed | Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data |
| title_short | Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data |
| title_sort | classification of breast cancer subtypes by combining gene expression and dna methylation data |
| url | https://doi.org/10.1515/jib-2014-236 |
| work_keys_str_mv | AT listmarkus classificationofbreastcancersubtypesbycombininggeneexpressionanddnamethylationdata AT hauschildannechristin classificationofbreastcancersubtypesbycombininggeneexpressionanddnamethylationdata AT tanqihua classificationofbreastcancersubtypesbycombininggeneexpressionanddnamethylationdata AT krusetorbena classificationofbreastcancersubtypesbycombininggeneexpressionanddnamethylationdata AT baumbachjan classificationofbreastcancersubtypesbycombininggeneexpressionanddnamethylationdata AT batraricha classificationofbreastcancersubtypesbycombininggeneexpressionanddnamethylationdata |