Proteomics Profiling of CLL Versus Healthy B-cells Identifies Putative Therapeutic Targets and a Subtype-independent Signature of Spliceosome Dysregulation

Abstract

Chronic lymphocytic leukemia (CLL) is a heterogeneous B-cell cancer exhibiting a wide spectrum of disease courses and treatment responses. Molecular characterization of RNA and DNA from CLL cases has led to the identification of important driver mutations and disease subtypes, but the precise mechanisms of disease progression remain elusive. To further our understanding of CLL biology we performed isobaric labeling and mass spectrometry proteomics on 14 CLL samples, comparing them with B-cells from healthy donors (HDB). Of 8694 identified proteins, ∼6000 were relatively quantitated between all samples (q<0.01). A clear CLL signature, independent of subtype, of 544 significantly overexpressed proteins relative to HDB was identified, highlighting established hallmarks of CLL (e.g. CD5, BCL2, ROR1 and CD23 overexpression). Previously unrecognized surface markers demonstrated overexpression (e.g. CKAP4, PIGR, TMCC3 and CD75) and three of these (LAX1, CLEC17A and ATP2B4) were implicated in B-cell receptor signaling, which plays an important role in CLL pathogenesis. Several other proteins (e.g. Wee1, HMOX1/2, HDAC7 and INPP5F) were identified with significant overexpression that also represent potential targets. Western blotting confirmed overexpression of a selection of these proteins in an independent cohort. mRNA processing machinery were broadly upregulated across the CLL samples. Spliceosome components demonstrated consistent overexpression (p = 1.3 × 10^-21) suggesting dysregulation in CLL, independent of SF3B1 mutations. This study highlights the potential of proteomics in the identification of putative CLL therapeutic targets and reveals a subtype-independent protein expression signature in CLL.

Fig. 1.

CLL proteomics workflow. PBMCs from 14 CLL patients and 3 healthy donors were subjected to negative B-cell isolation followed by whole cell lysis, reduction, alkylation and trypsin digestion. 100 μg of peptides from each CLL sample was assigned to one of two tandem mass tag (TMT)-labeled 10-plex experiments. 200 μg of each healthy donor B-cell (HDB) protein lysate was labeled and bifurcated to provide bridging controls across the two 10-plex experiments. Each 10-plex was handled and analyzed separately using 2-dimensional liquid chromatography coupled with data-dependent mass spectrometry; in each case, 60 peak-dependent fractions were analyzed. Peptides were identified from mass spectra using target-decoy searching (false discovery rate of <1%). The identified proteins were quantitated from isobaric labels relative to HDB bridging controls and differential expression analyzed to identify CLL-specific differences in protein expression.

Fig. 2.

Quantitative proteomics profiles and characteristics of CLL samples. A, Summary of the major characteristics of the 14 CLL samples, including; the assigned TMT 10-plex experiment (A or B), patient gender, presence of mutations to either NOTCH1 or SF3B1 genes, IGHV mutation status (U-IGHV, solid fill; unmutated IGHV, open), trisomy 12 status (Tri12), CD38+ (>99%) cases. B, Hierarchical clustering of 5956 protein log₂ (ratios) for the 14 CLL samples relative to HDB, sorted by regulation score. C, Volcano plot demonstrating proteins observed with significant overexpression (Rs>0.3, p < 0.05) or underexpression (Rs<-0.3, p < 0.05) in CLL versus HDB. A selection of the most overexpressed proteins are annotated, identifying the accepted CLL-specific hallmarks BCL2, ROR1 and CD5. D, The top 20 most consistently overexpressed proteins in CLL identified by the proteomics.

Fig. 3.

Validation of proteins deemed overexpressed by CLL proteomics. A, Proteomics-derived quantitations for proteins previously described with overexpression in CLL, relative to HDB controls. B, Western blot validation of differential protein expression observed in an independent cohort of CLL samples versus HDB controls. C, Comparison between the differential expressions observed for key proteins by Western blotting and proteomics for CLL and HDB controls.

Fig. 4.

Proteomics identification of CLL-overexpressed cell surface proteins. Proteomics-derived quantitations for the 20 most consistently upregulated cell surface-expressed proteins in CLL, relative to HDB. The data represent the number of unique peptides and PSMs, the Log₂ (ratios) relative to HDB, average Log₂ (ratios) and proposed function and evidence for prior observations in CLL.

Fig. 5.

Proteomics identification of CLL-overexpressed drug targets. Proteomics-derived quantitations for the 20 most consistently upregulated annotated targets of small molecular inhibitors in CLL, relative to HDB. Proteins were annotated using IPA. The data represent the number of unique peptides and PSMs, the Log₂ (ratios) relative to HDB, average Log₂ (ratios) and IPA-annotated drugs known to target each protein.

Fig. 6.

Bioinformatics analysis of the CLL-overexpressed proteome. A, GO term enrichment for the 544 overexpressed proteins (Rs>0.3, p < 0.05). The benjamini-corrected GO term enrichment p values were plotted against the number of CLL-upregulated proteins annotated with each term, additionally highlighting the observed fold-enrichment relative to the number expected by chance. B, IPA enriched canonical pathway “cleavage and polyadenylation of pre-mRNA” (p = 2.0 × 10⁻¹⁰) and C, “preinitiation complex assembly” (p = 1.7 × 10⁻⁴).

Fig. 7.

Significant enrichment of spliceosome components in CLL. A, Overexpressed proteins demonstrated significant enrichment of the components of the KEGG pathway “spliceosome” (n = 36, p = 1.3 × 10⁻²¹). This pathway is annotated with those proteins identified as significantly overexpressed (p < 0.05), with both substantial (Rs>0.3) and marginal (0.1<Rs<0.3) overexpression annotated using red and yellow stars, respectively. B, The differential expressions observed for all proteins and the individual log₂ (ratios) for each CLL sample, mapping to the KEGG “spliceosome” pathway.