Decision-tree approach to the immunophenotype-based prognosis of the B-cell chronic lymphocytic leukemia

Use of a nonlinear prediction method, such as machine learning, is a valuable choice in predicting progression rate of disease when applied to the highly variable and correlated biological data such as those in patients with chronic lymphocytic leukemia (CLL). In this work, decision-tree approach to cell phenotype-based prognosis of CLL was adopted. The panel of 33 (32 different phenotypic features and serum concentration of sCD23) parameters was simultaneously presented to the C4.5 decision tree which extracted the most informative of them and subsequently performed classification of CLL patients against the modified Rai staging system. It has been shown that substantial correlation between the percentage of expression of the CD23 molecule on CD19+ B-cells, the level of sCD23, the percentage of CD45RA+, and the absolute number of CD4CD45RA+RO+ T-cells and the clinical stages, exists. The prediction vector, composed of their concatenated values, was able to correctly associate 83% of the cases in the low-risk group (Rai stage 0), 100% of the cases in the intermediate-risk group (Rai stage I and II), and 89% of the cases in the high-risk group (Rai stage III and IV) of CLL patients. Predictivity of this vector was 100%, 95%, and 89%, respectively. In conclusion, from the described analysis, it may be inferred that two processes play important roles in the progression rate of CLL: 1.deregulated function of the CD23 gene in B-cells accompanied by the appearance of its cleaved product sCD23 in the sera; and 2. functionally impaired and imbalanced CD4 T-cell subpopulations found in the peripheral blood of CLL patients.