ABSTRACT
Chronic lymphocytic leukemia (CLL) is a clonal disease of B lymphocytes manifesting as an absolute lymphocytosis in the blood. However, not all lymphocytoses are leukemic. In addition, first-degree relatives of CLL patients have an ~15 % chance of developing a precursor condition to CLL termed monoclonal B cell lymphocytosis (MBL), and distinguishing CLL and MBL B lymphocytes from normal B cell expansions can be a challenge. Therefore, we selected FMOD, CKAP4, PIK3C2B, LEF1, PFTK1, BCL-2, and GPM6a from a set of genes significantly differentially expressed in microarray analyses that compared CLL cells with normal B lymphocytes and used these to determine whether we could discriminate CLL and MBL cells from B cells of healthy controls. Analysis with receiver operating characteristics and Bayesian relevance determination demonstrated good concordance with all panel genes. Using a random forest classifier, the seven-gene panel reliably distinguished normal polyclonal B cell populations from expression patterns occurring in pre-CLL and CLL B cell populations with an error rate of 2 %. Using Bayesian learning, the expression levels of only two genes, FMOD and PIK3C2B, correctly distinguished 100 % of CLL and MBL cases from normal polyclonal and mono/oligoclonal B lymphocytes. Thus, this study sets forth effective computational approaches that distinguish MBL/CLL from normal B lymphocytes. The findings also support the concept that MBL is a CLL precursor.