Clinical and Biological Relevance of Genomic Heterogeneity in Chronic Lymphocytic Leukemia

<div><p>Background</p><p>Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients’ leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL.</p> <p>Methods</p><p>To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups.</p> <p>Results</p><p>Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes.</p> <p>Conclusions</p><p>Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways.</p> </div

Representative examples of Consensus Cumulative Distribution Function (CDF) plots for the entire dataset (right) and randomly selected sub-datasets of 100 and 600 CLL samples (left and middle, respectively).

<p>By evaluating area under the curve and slope of the curves, it is appreciated that CDF plots of Consensus Clustering of sub-datasets the include 600 CLL samples are similar to the CDF plot of the entire dataset containing 893 CLL samples. However, CDF plots obtained upon using smaller sub-datasets, for example comprised of 100 CLL samples, is not similar to the CDF plot of the entire dataset.</p

Genomically-defined CLL subgroups with biological annotation.

<p>Unsupervised hierarchical clustering of the combined and normalized gene expression profiling dataset defined seven CLL subgroups. The number of CLL samples per subgroup ranged from 32 to 225. Gene Set Enrichment Analysis (GSEA) was used to evaluate biological pathways that distinguished each subgroup from the others.</p

A heatmap of oncogenic pathway signature predictions, with CLL samples grouped by genomically-defined subgroups on the x-axis, and signatures on the y-axis.

<p>Red denotes high signature prediction, and blue denotes low signature prediction, with prediction scores scaled by row. This demonstrates that subgroups have distinct patterns of oncogenic pathway activity, which confirm results obtained from GSEA analysis.</p

High-risk molecular prognostic markers found in each genomically-defined CLL subgroup.

<p>An evaluation of molecular prognostic markers found in the genomically-defined CLL subgroups identifies significantly different levels of these markers between the subgroups (p<0.0001, Pearson’s Chi-squared test for each prognostic marker). Results are reported as percentage of samples within a group with each high-risk prognostic marker, calculated as number with the prognostic marker divided by the total within the subgroup with data available.</p

Kaplan-Meier analysis of time from diagnosis to treatment in sixty-eight CLL patient samples, grouped by genomically-defined subgroup.

<p>A) A significant difference in overall survival was observed between CLL subgroups (p  = 0.004). B) CLL patients in “Interferon Pathway” subgroups had inferior overall survival compared to CLL patients in the “Receptor Signaling” subgroup (p  = 0.03). Significance was assessed using the log-rank test.</p

Single nucleotide polymorphism deletions and amplifications that are statistically enriched in genomically-defined CLL subgroups.

<p>An evaluation of copy number variations in CLL lymphocytes revealed two regions of amplification and one region of deletion that are significantly associated with certain subgroups. The regions were identified based on Affymetrix annotation, and was verified with the University of California Santa Cruz genomic browser, NCBI135/hg17 genome assembly. A full list of the genes contained in amplification region on chromosome two is found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057356#pone.0057356.s001" target="_blank">Table S1</a>. P values were calculated using the Pearson’s Chi-squared test.</p

CLL gene expression data files from the fifteen individual datasets were evaluated by principal component analysis (PCA).

<p>A) PCA prior to Bayesian Factor Regression Modeling (BFRM) normalization was performed, and the first principal component (PC) is plotted against the second PC. Numbers represent dataset order found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057356#pone-0057356-t001" target="_blank">Table 1</a>. CLL samples from each dataset cluster together. B) PCA following BFRM normalization was performed, and the first PC is plotted against the second PC. Samples retain the same numbering as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057356#pone-0057356-g001" target="_blank">Figure 1A</a>. CLL samples now cluster together in one cloud.</p

Patient treatment history.

<p>Patient treatment history.</p

Enhanced CDC of B cell chronic lymphocytic leukemia cells mediated by rituximab combined with a novel anti-complement factor H antibody

<div><p>Rituximab therapy for B cell chronic lymphocytic leukemia (B-CLL) has met with mixed success. Among several factors to which resistance can be attributed is failure to activate complement dependent cytotoxicity (CDC) due to protective complement regulatory proteins, including the soluble regulator complement factor H (CFH). We hypothesized that rituximab killing of non-responsive B-CLL cells could be augmented by a novel human monoclonal antibody against CFH. The B cells from 11 patients with B-CLL were tested <i>ex vivo</i> in CDC assays with combinations of CFH monoclonal antibody, rituximab, and a negative control antibody. CDC of rituximab non-responsive malignant B cells from CLL patients could in some cases be augmented by the CFH monoclonal antibody. Antibody-mediated cytotoxicity of cells was dependent upon functional complement. In one case where B-CLL cells were refractory to CDC by the combination of rituximab plus CFH monoclonal antibody, additionally neutralizing the membrane complement regulatory protein CD59 allowed CDC to occur. Inhibiting CDC regulatory proteins such as CFH holds promise for overcoming resistance to rituximab therapy in B-CLL.</p></div