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.

Abstract

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.