Abstract
The mutational status of the immunoglobulin heavy chain variable region (IGHV) defines two clinically distinct forms of chronic lymphocytic leukemia (CLL) known as mutated (M-CLL) and un-mutated (UM-CLL). Patients with M-CLL usually have a favourable outcome whereas those with UM-CLL develop progressive disease and have shorter survival. However, the molecular mechanisms responsible for the more aggressive clinical behaviour associated with UM-CLL are not well understood. Here we describe the application of isobaric tags for relative and absolute quantification (iTRAQ) based mass spectrometry (MS) to analyse the total proteome of M-CLL and UM-CLL samples. This has enabled us to generate the largest quantity of proteomic information for CLL to date and, in particular, to directly compare the functions of differentially expressed proteins between UM-CLL and M-CLL cells through a systems biology approach.
We isolated CLL cells from the peripheral blood from 18 CLL patients (9 UM-CLL, 9 M-CLL) and prepared cellular protein extracts which were digested and subjected to labelling with iTRAQ reagents, as previously described (Kitteringham et al, J Proteomics, 2010;73(8):1612-1631). Principal component analysis was used to assess variance across the data set generated by iTRAQ-MS. Statistical significance of the difference in the levels of expression of proteins between UM-CLL and M-CLL samples was determined using student T-test (2-tailed). Several differentially expressed proteins identified by iTRAQ-MS were also validated by immunoblotting. Computational analysis was performed to examine the functions of the differentially expressed proteins and their associated signalling pathways using the GeneGo pathway maps in the Metacore database (Thomson Reuters, NY, USA).
Unsupervised clustering, based on the expression of 3521 identified proteins, separated CLL samples into two groups corresponding to IGHV mutational status. We identified 274 proteins that were differentially expressed between UM-CLL and M-CLL subgroups (p<0.05, Figure 1A). Hierarchical clustering based on the relative expression of differentially expressed proteins also separated individual CLL cases into two distinct clusters according to their IGHV status (Figure 1B). Computational analysis showed that 43 cell migration/adhesion pathways were significantly enriched (p<0.05) by 39 differentially expressed proteins, 35 of which were expressed at significantly lower levels in UM-CLL samples. Furthermore, UM-CLL cells under-expressed proteins associated with cytoskeletal remodelling and over-expressed proteins associated with transcriptional and translational activity. Taken together, these findings indicated that UM-CLL cells are less migratory and more adhesive than M-CLL cells, resulting in their retention in lymph nodes where they are exposed to proliferative stimuli. In agreement with this hypothesis, analysis of an extended cohort of 120 CLL patients revealed that twice as many patients with UM-CLL than M-CLL had documented lymphadenopathy (50% v 24%; P<0.01). The association between UM-CLL and lymphadenopathy was not simply a reflection of increased tumour burden as there was no significant difference in the leukocyte count between the two groups (medians of 37 x 109/L and 28 x 109/L, respectively; P>0.05).
In addition, other pathways that promote cell survival and proliferation in UM-CLL cells were also enriched by the differentially expressed proteins. These include the immune response pathway involving B-cell receptor (BCR) signalling (P=0.006), the endoplasmic reticulum (ER) stress response pathway (P=0.035) and the Wnt signalling pathway (P=0.006).
Our study has shown that quantitative analysis of the total proteome by iTRAQ-MS was able to separate individual CLL cases according to IGHV status and explained the more aggressive clinical behaviour of UM-CLL and its particular sensitivity to novel therapeutic agents that induce anatomical displacement from the lymph node microenvironment, such as ibrutinib and idelalisib. Moreover, in keeping with the ability of proteomics to detect alterations in gene expression resulting from both transcriptional and post-transcriptional mechanisms, the study illustrates the considerable potential of iTRAQ-MS coupled with computational analysis to elucidate pathogenetic mechanisms and indicate therapeutic strategies in cancer.
Figure 1 Figure 1.
Disclosures
No relevant conflicts of interest to declare.
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