Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability to detect large numbers of modified proteins with a high level of sensitivity and specificity. The low stoichiometry of modified peptides means fractionation and enrichment techniques are often performed prior to MS to improve detection yields. Immuno-based techniques remain popular, with improvements in the quality of commercially available modification-specific antibodies facilitating the detection of modified proteins with high affinity. PTM-focused studies on blood cancers have provided information on altered cellular processes, including cell signaling, apoptosis and transcriptional regulation, that contribute to the malignant phenotype. Furthermore, the mechanism of action of many blood cancer therapies, such as kinase inhibitors, involves inhibiting or modulating protein modifications. Continued optimization of protocols and techniques for PTM analysis in blood cancer will undoubtedly lead to novel insights into mechanisms of malignant transformation, proliferation, and survival, in addition to the identification of novel biomarkers and therapeutic targets. This review discusses techniques used for PTM analysis and their applications in blood cancer research.

Acute myeloid leukemia (AML) is characterized by an increasing number of clonal myeloid blast cells which are incapable of differentiating into mature leukocytes. AML risk stratification is based on genetic background, which also serves as a means to identify the optimal treatment of individual patients. However, constant refinements are needed, and the inclusion of significant measurements, based on the various omics approaches that are currently available to researchers/clinicians, have the potential to increase overall accuracy with respect to patient management. Using both nontargeted (label-free mass spectrometry) and targeted (multiplex immunoassays) proteomics, a range of proteins were found to be significantly changed in AML patients with different genetic backgrounds. The inclusion of validated proteomic biomarker panels could be an important factor in the prognostic classification of AML patients. The ability to measure both cellular and secreted analytes, at diagnosis and during the course of treatment, has advantages in identifying transforming biological mechanisms in patients, assisting important clinical management decisions.

Since the emergence of high-throughput proteomic techniques and advances in clinical technologies, there has been a steady rise in the number of cancer-associated diagnostic, prognostic, and predictive biomarkers being identified and translated into clinical use. The characterisation of biofluids has become a core objective for many proteomic researchers in order to detect disease-associated protein biomarkers in a minimally invasive manner. The proteomes of biofluids, including serum, saliva, cerebrospinal fluid, and urine, are highly dynamic with protein abundance fluctuating depending on the physiological and/or pathophysiological context. Improvements in mass-spectrometric technologies have facilitated the in-depth characterisation of biofluid proteomes which are now considered hosts of a wide array of clinically relevant biomarkers. Promising efforts are being made in the field of biomarker diagnostics for haematologic malignancies. Several serum and urine-based biomarkers such as free light chains, β-microglobulin, and lactate dehydrogenase are quantified as part of the clinical assessment of haematological malignancies. However, novel, minimally invasive proteomic markers are required to aid diagnosis and prognosis and to monitor therapeutic response and minimal residual disease. This review focuses on biofluids as a promising source of proteomic biomarkers in haematologic malignancies and a key component of future diagnostic, prognostic, and disease-monitoring applications.

Multiple myeloma (MM) is an incurable haematological malignancy of plasma cells in the bone marrow. In rare cases, an aggressive form of MM called extramedullary multiple myeloma (EMM) develops, where myeloma cells enter the bloodstream and colonise distal organs or soft tissues. This variant is associated with refractoriness to conventional therapies and a short overall survival. The molecular mechanisms associated with EMM are not yet fully understood. Here, we analysed the proteome of bone marrow mononuclear cells and blood plasma from eight patients (one serial sample) with EMM and eight patients without extramedullary spread. The patients with EMM had a significantly reduced overall survival with a median survival of 19 months. Label-free mass spectrometry revealed 225 proteins with a significant differential abundance between bone marrow mononuclear cells (BMNCs) isolated from patients with MM and EMM. This plasma proteomics analysis identified 22 proteins with a significant differential abundance. Three proteins, namely vascular cell adhesion molecule 1 (VCAM1), pigment epithelium derived factor (PEDF), and hepatocyte growth factor activator (HGFA), were verified as the promising markers of EMM, with the combined protein panel showing excellent accuracy in distinguishing EMM patients from MM patients. Metabolomic analysis revealed a distinct metabolite signature in EMM patient plasma compared to MM patient plasma. The results provide much needed insight into the phenotypic profile of EMM and in identifying promising plasma-derived markers of EMM that may inform novel drug development strategies.

No abstract

No abstract

Introduction: Extramedullary multiple myeloma (EMM) refers to the spread of clonal plasma cells to tissues extending outside of the bone marrow microenvironment. EMM is present at the time of diagnosis in 6-10% of patients, however, this increases to 13-26% in patients with disease progression and relapse. Cancer cells are suspected to spread to new tissues and organs via the circulatory system as a result of molecular changes that allow malignant cells to escape the bone marrow (BM). For example, downregulation of CXCR4 (C-X-C Motif Chemokine Receptor 4), an important factor in cellular homing to the BM, has frequently been reported to be linked to the EMM phenotype. In addition, the majority of patients presenting with EMM have highly complex cytogenetic abnormalities and high-risk cytogenetic markers such as t(14;16). As EMM is an indicator of a more aggressive disease, more intensive treatment, including combination chemotherapy, is often recommended. Many of the underlying molecular mechanisms accompanying EMM are yet to be characterised. Our mass-spectrometry (MS)-based proteomic study provides insight into the unique molecular mechanisms associated with EMM, identifying key proteins linked to the progression of medullary multiple myeloma (MM) to EMM. Methods: Label-free liquid chromatography mass spectrometric analysis of age and gender matched medullary MM (n=8) and EMM (n=9) bone-marrow derived mononuclear cells (MNCs) was carried out using a Thermo Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific). Proteome Discoverer 2.2 using Sequest HT (Thermo Fisher Scientific) and a percolator were employed for the identification of peptides and proteins. For protein identification, the following search parameters were used: (i) 0.02 Da for MS/MS mass tolerance, (ii) 10 ppm for peptide mass tolerance, (iii) variable modification settings for methionine oxidation, (iv) fixed modification settings in relation to carbamido-methylation and (v) tolerance for up to two missed cleavages. Peptide probability was set to high confidence. Datasets were imported into Progenesis QI (version 2.0) software for further analysis. Data was filtered based on an ANOVA p-value of ≤0.05, fold change >1.5 between experimental groups, and proteins with ≥1 unique peptides contributing to the identification. Proteins with less than 70% valid values were removed from the analysis. G:profiler and STRING were utilized for functional enrichment and the characterisation of protein interaction patterns. Results: Our quantitative MS-based proteomic analysis identified a total of 492 proteins with significantly altered abundances between EMM and MM bone marrow MNC. Of these significant proteins, 275 were found to be increased in EMM compared to medullary MM and 217 were found to be decreased in EMM compared to medullary MM. Hierarchical clustering was performed to highlight the proteomic profile associated with extramedullary disease (Figure 1A). KEGG pathway analysis and gene ontology (GO) analysis of proteins found to be increased in EMM indicated an increase in proteins associated with cell adhesion, invasion, and migration pathways (Figure 1B). Interestingly, several proteins involved in leukocyte transendothelial migration were significantly increased in EMM indicating their potential involvement in the dissemination of MM cells from the bone marrow microenvironment to distal tissues (Figure 1C). Among the proteins found to be involved in this biological pathway was junctional adhesion molecule-A (F11R), a protein previously reported to play a role in EMD pathophysiology [1]. Other proteins involved in MM invasion and migration including Rho-associated protein kinase 2 (ROCK2), Ras-related C3 botulinum toxin substrate 1 (Rac1) and platelet endothelial cell adhesion molecule (PECAM-1) were significantly increased in EMM. Conclusion: Using high-resolution mass spectrometry to characterise the tumour proteome of MM patients with extramedullary disease, we have identified a significant increase in the abundance of proteins associated with leukocyte transendothelial invasion in primary EMM samples. Our study provides further insight into the molecular mechanisms within EMM and thus holds potential to enhance current efforts to provide a more personalised therapeutic approach for EMM patients. References: [1] A.G. Solimando et al., Blood 2018; 132 (Supplement 1): 4455. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Introduction: Multiple myeloma (MM) is characterized by the clonal expansion of plasma cells in the bone marrow resulting in end-organ damage. Despite an extensive increase in the five-year survival rate in recent years, MM is still considered an incurable disease as patients will repeatedly relapse and develop resistance to current chemotherapies. A key focus for the personalization of myeloma therapy is understanding the biological mechanisms of drug resistance and identifying clinically useful biomarkers of therapeutic response. Highly efficient techniques for the enrichment of phosphorylated peptides followed by high resolution mass spectrometry facilitates the quantitation of thousands of site-specific phosphorylation events. Here, we have performed a phosphoproteomic analysis on MM cell lysates stratified based on their ex vivo drug response profiles to advance our understanding of drug resistance mechanisms. Materials and Methods: CD138 + plasma cells were isolated from 20 adult MM patient bone marrow aspirates at diagnosis (n=7) or relapse (n=13). Samples were grouped based on ex vivo drug sensitivity and resistance testing (DSRT) as follows: highly sensitive (Group I), sensitive (Group II), resistant (Group III), highly resistant (Group IV) [1]. For the phosphoproteomic analysis, peptides were generated and purified using the filter aided sample preparation (FASP) protocol. Peptide tandem mass tag (TMT) labelling, Fe 3+ immobilized metal ion affinity chromatography (IMAC), synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) was performed. Nonenriched peptides were used for proteomic analysis. Resulting data was analysed using MaxQuant, followed by normalization of phosphosite intensities using the internal reference scale (IRS) method, and statistical analysis using Perseus. Functional enrichment and kinase enrichment analyses were performed on significant phosphoproteins using g:profiler and KEA2, respectively. Results: Our quantitative MS-based phosphoproteomic analysis identified 2,945 phosphorylation sites on 2,232 phosphopeptides from 690 phosphoproteins. Of these phosphorylation sites, 176 were significantly changed between all four DSRT groups and 267 were significantly changed between Group I and Group IV (False Discovery Rate (FDR) < 0.05). Hierarchical clustering was performed to highlight the distinct phosphoproteomic profiles associated with each DSRT group, of which the very sensitive (Group I) and very resistant (Group IV) subgroups demonstrated a well-defined separation (Fig. 1A, 1B). KEGG pathway analysis and gene ontology (GO) analysis of significantly increased phosphorylated proteins in Group IV compared to Group I MM patients demonstrated an increased phosphorylation of proteins associated with tight junctions, the Rap1 signalling pathway and the phosphatidylinositol signalling system; indicating an upregulation of cell adhesion associated processes in drug resistant MM. Phosphoproteins increased in abundance in Group I compared to Group IV MM patients revealed an increased phosphorylation of proteins involved in translation and RNA processing including the spliceosome, RNA transport and RNA binding pathways (Fig. 1C). We identified filamin A serine 2152, RAS guanyl-releasing protein 2 serine 576 and proto-oncogene tyrosine-protein kinase Src serine 17 as increased in Group IV MM, and nuclease-sensitive element-binding protein 1 (YBX1) serine 165, CD44 serine 697 and Bcl2-associated agonist of cell death (BAD) serine 99 as increased in Group I MM. KEA of the upregulated phosphoproteome in Group IV revealed an enrichment of cyclin dependent kinase 1 (CDK1) and ribosomal s6 kinases (RPS6K) whereas casein kinase 2 (CK2) and the glycolysis-associated kinases were enriched in Group I (Fig. 1D). Conclusion: Our study has generated a phosphoproteomic dataset demonstrating distinct phosphorylation signatures associated with drug sensitivity in clinical MM plasma cells. The identification of phosphorylation events associated with drug resistance provides a basis for further exploration of these events and associated signalling pathways to further understand drug resistance mechanisms in MM and identify potential biomarkers of therapeutic response and targets for drug re-sensitization in MM. References: [1] M. M. Majumder et al., Oncotarget 8(34), 56338 (2017) Figure 1 Figure 1. Disclosures Heckman: Novartis: Research Funding; Orion Pharma: Research Funding; Celgene/BMS: Research Funding; Oncopeptides: Consultancy, Research Funding; Kronos Bio, Inc.: Research Funding.