Clustering of Expression Data in Chronic Lymphocytic Leukemia Reveals New Molecular Subdivisions.

ABSTRACT: Although the identification of inherent structure in chronic lymphocytic leukemia (CLL) gene expression data using class discovery approaches has not been extensively explored, the natural clustering of patient samples can reveal molecular subdivisions that have biological and clinical implications. To explore this, we preprocessed raw gene expression data from two published studies, combined the data to increase the statistical power, and performed unsupervised clustering analysis. The clustering analysis was replicated in 4 independent cohorts. To assess the biological significance of the resultant clusters, we evaluated their prognostic value and identified cluster-specific markers. The clustering analysis revealed two robust and stable subgroups of CLL patients in the pooled dataset. T

SUBMITTER: Yepes S 

PROVIDER: S-EPMC4565688 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature