Protein expression analysis of chronic lymphocytic leukemia defines the effect of genetic aberrations and uncovers a correlation of CDK4, P27 and P53 with hierarchical risk

Abstract

Background: Chronic lymphocytic leukemia has a variable clinical course. Genomic aberrations identify prognostic subgroups, pointing towards distinct underlying biological mechanisms that are poorly understood. In particular it remains unclear whether the prognostic subgroups of chronic lymphocytic leukemia are characterized by different levels of leukemogenic proteins.

Design and methods: Expression of 23 proteins involved in apoptosis, proliferation, DNA damage, and signaling or whose genes map to chromosomal regions known to be critical in chronic lymphocytic leukemia was quantified in 185 cytogenetically well characterized cases of chronic lymphocytic leukemia using immunoblotting. Cases were categorized hierarchically into deletion(17p), deletion(11q), trisomy 12, deletion(13q) as sole abnormality or normal karyotype. Statistical analysis was performed for expression differences between these subgroups. In addition, the expression levels of CDK4, P27 and P53 were quantified over the clinical course and compared to levels in immunopurified B cells from healthy individuals.

Results: In subgroups with a good prognosis, differential expression was mainly seen for proteins that regulate apoptosis. In contrast, in cytogenetic subgroups with a worse prognosis, differential expression was mostly detected for proteins that control DNA damage and proliferation. Expression levels of CDK4, P27 and P53 were higher compared to those in B cells from healthy individuals and significantly correlated with increasing hierarchical risk. In addition, no significant longitudinal changes of expression levels of CDK4, P27 and P53 could be detected in chronic lymphocytic leukemia patients.

Conclusions: Differences in expression levels of apoptosis- and proliferation-controlling proteins define distinct prognostic subgroups of chronic lymphocytic leukemia and uncover a correlation of levels of CDK4, P27 and P53 proteins with higher hierarchical risk.

Figure 1.

Combined box plots/dot plots of differentially expressed proteins in CLL cases with del(13q), del(11q), trisomy 12q, del(17p) and a cytogenetically normal karyotype compared to all other hierarchical subgroups. Each dot represents the level of expression (y-axis) of a single CLL case. Negative values of expression are the result of log-transformation for statistical analysis.

Figure 2.

Overview of protein levels in hierarchical subgroups of CLL suggesting an imbalance of apoptosis and proliferation in CLL in benign versus aggressive subgroups. The figure illustrates an increasing number of differentially expressed proteins involved in proliferation/DNA damage and a decreasing number of differentially expressed proteins that control apoptosis with higher genetic risk category [two arrows: proteins with significant differential expression; single arrows: significant before adjustment for multiple testing (trend)].

Figure 3.

Heat map showing multivariate analysis of protein-protein correlations taking cytogenetics into account (imputed data set) (N=139). The order of the proteins is determined by a seriation method which sorts the pairs of proteins with the strongest correlations close to the diagonal. The strongest correlations were found for ARF3 and STAT6, ARF3 and AKT1 as well as for BCL2 and MCL1 (correlation coefficients: 0.22, −0.21, and 0.20, respectively).

Figure 4.

(A) Diagrams of ordinality risk for CDK4, P27 and P53. Protein expression was correlated to ordinal hierarchical risk as a surrogate marker for survival. Patients were stratified into four categories representing increasing risk [IGHV mutated < IGHV unmutated/V3–21 < del(11q) < del(17p)].^{(3, 26)} Continuation ratio (CR) models for correlation of protein expression with hierarchical risk (univariate analysis) were calculated based on the data detected in this study and the assumption of a hierarchical risk as a surrogate marker for survival. Solid lines connect the observed mean log expression values per risk category, while dashed lines show the estimated lines according to the CR model without interaction effects. Diagrams show an increase of the risk category with increasing expression of CDK4, P27 and P53 [Wald-tests for overall effects at a significance level of 0.05; adjusted P values: CDK4: P<0.001 (n=90); P27 P<0.001 (n=98); P53: P<0.01 (n=117)]. (B) Kaplan-Meier curves for overall survival from diagnosis stratified by protein expression levels (high: log(expression) > median, low: log(expression) ≤ median). The given P values correspond to the log-rank tests between these groups (CDK4 (n=18), P27 (n=20), and P53 (n=25)). As the relation between death hazard and P53 protein expression level is highly non-linear three strata (with cut-offs at the 33%-quantile and 67%-quantile) were used for P53. The figure shows that survival rates are better for the intermediate group. This fits nicely with the observed non-linear effect of P53 on the hierarchical risk (Figure 4a), where the two high-risk groups del(11q) and del(17p) correspond to either very low P53 expression (11q) or very high P53 expression (17p), while the lower-risk groups showed intermediate P53 expression.