Breast cancer gene expression profiling: clinical trial and practice implications

Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns

Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a “core cluster” of samples for each category, and from these we determine “patterns” of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not.

Neural networks and Fuzzy clustering methods for assessing the efficacy of microarray based intrinsic gene signatures in breast cancer classification and the character and relations of identified subtypes

In the classification of breast cancer subtypes using microarray data, hierarchical clustering is commonly used. Although this form of clustering shows basic cluster patterns, more needs to be done to investigate the accuracy of clusters as well as to extract meaningful cluster characteristics and their relations to increase our confidence in their use in a clinical setting. In this study, an in-depth investigation of the efficacy of three reported gene subsets in distinguishing breast cancer subtypes was performed using four advanced computational intelligence methods-Self-Organizing Maps (SOM), Emergent Self-Organizing Maps (ESOM), Fuzzy Clustering by Local Approximation of Memberships (FLAME), and Fuzzy C-means (FCM)-each differing in the way they view data in terms of distance measures and fuzzy or crisp clustering. The gene subsets consisted of 71, 93, and 71 genes reported in the literature from three comprehensive experimental studies for distinguishing Luminal (A and B), Basal, Normal breast-like, and HER2 subtypes. Given the costly procedures involved in clinical studies, the proposed 93-gene set can be used for preliminary classification of breast cancer. Then, as a decision aid, SOM can be used to map the gene signature of a new patient to locate them with respect to all subtypes to get a comprehensive view of the classification. These can be followed by a deeper investigation in the light of the observations made in this study regarding overlapping subtypes. Results from the study could be used as the base for further refining the gene signatures from later experiments and from new experiments designed to separate overlapping clusters as well as to maximally separate all clusters.

A Normalized Tree Index for identification of correlated clinical parameters in microarray experiments

Background

Measurements on gene level are widely used to gain new insights in complex diseases e.g. cancer. A promising approach to understand basic biological mechanisms is to combine gene expression profiles and classical clinical parameters. However, the computation of a correlation coefficient between high-dimensional data and such parameters is not covered by traditional statistical methods.

Methods

We propose a novel index, the Normalized Tree Index (NTI), to compute a correlation coefficient between the clustering result of high-dimensional microarray data and nominal clinical parameters. The NTI detects correlations between hierarchically clustered microarray data and nominal clinical parameters (labels) and gives a measurement of significance in terms of an empiric p-value of the identified correlations. Therefore, the microarray data is clustered by hierarchical agglomerative clustering using standard settings. In a second step, the computed cluster tree is evaluated. For each label, a NTI is computed measuring the correlation between that label and the clustered microarray data.

Results

The NTI successfully identifies correlated clinical parameters at different levels of significance when applied on two real-world microarray breast cancer data sets. Some of the identified highly correlated labels confirm the actual state of knowledge whereas others help to identify new risk factors and provide a good basis to formulate new hypothesis.

Conclusions

The NTI is a valuable tool in the domain of biomedical data analysis. It allows the identification of correlations between high-dimensional data and nominal labels, while at the same time a p-value measures the level of significance of the detected correlations.

Gene expression predictors in breast cancer: current status, limitations and perspectives

Breast cancer is characterised by a wide heterogeneity regarding outcome and drug sensitivity. A better prediction of these two parameters at the individual level should improve patient management and therefore also improve both the quality of life and the overall survival of the patient. Several molecular predictors for prognosis (MammaPrint or Oncotype DX) and drug prediction (DLD30, SET index) have been generated using DNA-based arrays or RT-PCR, some of these being tested in phase III trials. Although they exhibit good metric performance and should improve the quality of care in the next decade, these predictors are considered suboptimal regarding the potential of the technology. New study design and arrays should generate more powerful second generation gene signatures.

Genomic signatures of breast cancer metastasis

Despite significant advances in the treatment of primary cancer, the ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and they predict individual outcomes poorly. However, the recent introduction of high-throughput microarray technology has opened new avenues in genomic investigation of cancer, and through application in tissue-based studies and appropriate animal models, has facilitated the identification of gene expression signatures that are associated with the lethal progression of breast cancer. The use of these approaches has the potential to greatly impact our knowledge of tumor biology, to provide efficient biomarkers, and enable development towards customized prognostication and therapies for the individual.

Microchips for the diagnosis of cervical cancer

Cancer affects more people than any other disease. About one-third of the world's population is likely to get this diagnosis during their lifetime. Currently, the diagnostic methods for cancer detection are based on visual inspection. The lack of high analytical and clinical specificity and sensitivity makes these methods in many cases inferior to recently developed molecular methods. The increased clinical specificity and sensitivity of these new molecular approaches have great benefits, such as the possibility of implementing the molecular methods in miniaturized systems and enabling easier and faster point-of-care or bedside diagnostics. This chapter provides an introduction to performing clinical trials, screening, and molecular diagnostics against cancer-related markers. In addition, an example of molecular diagnosis of cervical cancer within a microsystem concept will be presented.