Identifying stable breast cancer subgroups using semi-supervised fuzzy c-means on a reduced panel of biomarkers

The aim of this work is to identify clinically-useful and stable breast cancer subgroups using a reduced panel of biomarkers. First, we investigate the stability of subgroups generated using two different reduced panels of biomarkers on clustering of breast cancer data. The stability of the subgroups found are assessed based on comparison of agreement levels using Cohen´s Kappa Index on clustering solutions from ssFCM methodologies, consensus K-means and model-based clustering. The clustering solutions obtained from the feature set which achieve the higher agreement is chosen for further biological and clinical evaluation to establish the subgroups are clinically-useful. Using a ssFCM methodology, we identified seven clinically-useful and stable breast cancer subgroups using a reduced panel by Soria et al. So far, the stability of the subgroups identified using the reduced panel of biomarkers have not yet been investigated.