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Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

Cancer has recently become the number one cause of death in The Netherlands. Breast cancer is the most prevalent form of cancer among females, with a lifetime risk of 12.8% (i.e. the 1 in 8 rule). As a result, more and more research is devoted to getting a better insight into cancer, and to further develop strategies for personalized treatment. Cancer is often induced by damage to the DNA, a molecule present in all cells. Usually, damage to the DNA is not that much of a problem, because it gets repaired by one of several repair mechanisms in the cell. However, sometimes the repair is incorrect, or the repair mechanisms themselves might get damaged, resulting in aberrations to the DNA that could eventually lead to abnormal cell behavior. If the DNA is aberrated in a suitable way, a cell can start to replicate uncontrollably, which causes cells to accumulate. This results in the formation of a tumor, and if the cell growth is allowed to continue long enough this can result in a palpable lump in the breast. Eventually, cells from the tumor will spread trough the body and invade other tissues and organs (with metastases as a result), which is often fatal. Upon diagnosis of breast cancer, the tumor is surgically removed, followed by chemotherapy treatment to eradicate any tumorigenic cells that may still be present. This strategy has led to overtreatment of patients in the past, since some patients would have remained metastasis free even without additional therapy. Knowing beforehand whether or not a patient will eventually develop a metastasis (i.e. the outcome of the disease), would be of great value in the clinic. Low-risk patients could then be spared unnecessary treatment, for example. Hence, several models have been developed that predict outcome based on clinical and pathological parameters that are being recorded at the time of diagnosis. More recently, microarray analysis has enabled the measurement of the mRNA levels of cells (a derivative of the genes on the, ICT group, Electrical Engineering, Mathematics and Computer Science

Cancer has recently become the number one cause of death in The Netherlands. Breast cancer is the most prevalent form of cancer among females, with a lifetime risk of 12.8% (i.e. the 1 in 8 rule). As a result, more and more research is devoted to getting a better insight into cancer, and to further develop strategies for personalized treatment. Cancer is often induced by damage to the DNA, a molecule present in all cells. Usually, damage to the DNA is not that much of a problem, because it gets repaired by one of several repair mechanisms in the cell. However, sometimes the repair is incorrect, or the repair mechanisms themselves might get damaged, resulting in aberrations to the DNA that could eventually lead to abnormal cell behavior. If the DNA is aberrated in a suitable way, a cell can start to replicate uncontrollably, which causes cells to accumulate. This results in the formation of a tumor, and if the cell growth is allowed to continue long enough this can result in a palpable lump in the breast. Eventually, cells from the tumor will spread trough the body and invade other tissues and organs (with metastases as a result), which is often fatal. Upon diagnosis of breast cancer, the tumor is surgically removed, followed by chemotherapy treatment to eradicate any tumorigenic cells that may still be present. This strategy has led to overtreatment of patients in the past, since some patients would have remained metastasis free even without additional therapy. Knowing beforehand whether or not a patient will eventually develop a metastasis (i.e. the outcome of the disease), would be of great value in the clinic. Low-risk patients could then be spared unnecessary treatment, for example. Hence, several models have been developed that predict outcome based on clinical and pathological parameters that are being recorded at the time of diagnosis. More recently, microarray analysis has enabled the measurement of the mRNA levels of cells (a derivative of the genes on the, ICT group, Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

OA fund TU Delft, Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

OA fund TU Delft, Electrical Engineering, Mathematics and Computer Science

Background. The availability of large collections of microarray datasets (compendia), or knowledge about grouping of genes into pathways (gene sets), is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while gene sets reduce the size of the feature space. This should be favorable from a machine learning perspective and result in more robust predictors. Methodology. We extracted modules of regulated genes from gene sets, and compendia. Through supervised analysis, we constructed predictors which employ modules predictive of breast cancer outcome. To validate these predictors we applied them to independent data, from the same institution (intra-dataset), and other institutions (inter-dataset). Conclusions. We show that modules derived from single breast cancer datasets achieve better performance on the validation data compared to gene-based predictors. We also show that there is a trend in compendium specificity and predictive performance: modules derived from a single breast cancer dataset, and a breast cancer specific compendium perform better compared to those derived from a human cancer compendium. Additionally, the module-based predictor provides a much richer insight into the underlying biology. Frequently selected gene sets are associated with processes such as cell cycle, E2F regulation, DNA damage response, proteasome and glycolysis. We analyzed two modules related to cell cycle, and the OCT1 transcription factor, respectively. On an individual basis, these modules provide a significant separation in survival subgroups on the training and independent validation data., Mediamatics, Electrical Engineering, Mathematics and Computer Science

Background. The availability of large collections of microarray datasets (compendia), or knowledge about grouping of genes into pathways (gene sets), is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while gene sets reduce the size of the feature space. This should be favorable from a machine learning perspective and result in more robust predictors. Methodology. We extracted modules of regulated genes from gene sets, and compendia. Through supervised analysis, we constructed predictors which employ modules predictive of breast cancer outcome. To validate these predictors we applied them to independent data, from the same institution (intra-dataset), and other institutions (inter-dataset). Conclusions. We show that modules derived from single breast cancer datasets achieve better performance on the validation data compared to gene-based predictors. We also show that there is a trend in compendium specificity and predictive performance: modules derived from a single breast cancer dataset, and a breast cancer specific compendium perform better compared to those derived from a human cancer compendium. Additionally, the module-based predictor provides a much richer insight into the underlying biology. Frequently selected gene sets are associated with processes such as cell cycle, E2F regulation, DNA damage response, proteasome and glycolysis. We analyzed two modules related to cell cycle, and the OCT1 transcription factor, respectively. On an individual basis, these modules provide a significant separation in survival subgroups on the training and independent validation data., Mediamatics, Electrical Engineering, Mathematics and Computer Science