Proteomic Alterations in Multiple Myeloma: A Comprehensive Study Using Bone Marrow Interstitial Fluid and Serum Samples

Proteomics analysis of serum and urine identifies VCP and CTSA as potential biomarkers associated with multiple myeloma

AIMS

We analyzed the differentially expressed proteins (DEPs) in serum and urine in order to provide new potential biomarkers for MM.

METHODS

Data-Independent Acquisition-based proteomics of serum and urine was performed to identify potential biomarkers for MM patients. Then we performed Western Blotting (WB), ELISA along with their ROC curve analysis to confirm DEPs.

RESULTS

A total of 1653 proteins in serum and 4519 proteins in urine were identified using Data-Dependent Acquisition method. VCP was the only protein that showed significant differences in different comparison groups in both serum and urine. Pathway analysis revealed that protein processing in the endoplasmic reticulum was the most relevant pathway associated with MM. Furthermore, the increased expression of HSP90B1, VCP, CTSA, HYOU1, PDIA4, and RAB7A was detected by WB. The results of ELISA indicated that a combination of VCP and CTSA provided a high area under curve (AUC) value of 0.883 (95 % CI, 0.769-0.997, p < 0.001) to diagnose NDMM. The combination of VCP, CTSA, ALB, and HGB exhibited better performance (AUC = 0.981), with 100 % specificity and 86.7 % sensitivity.

CONCLUSION

These findings suggest VCP and CTSA exhibit potential as biomarkers for MM, which may be helpful in the molecular mechanisms and pathogenesis upon further investigation.

Identification of tumor antigens and immune subtypes of hepatocellular carcinoma for mRNA vaccine development

BACKGROUND

mRNA vaccines have been investigated in multiple tumors, but limited studies have been conducted on their use for hepatocellular carcinoma (HCC).

AIM

To identify candidate mRNA vaccine antigens for HCC and suitable subpopulations for mRNA vaccination.

METHODS

Gene expression profiles and clinical information of HCC datasets were obtained from International Cancer Genome Consortium and The Cancer Genome Atlas. Genes with somatic mutations and copy number variations were identified by cBioPortal analysis. The differentially expressed genes with significant prognostic value were identified by Gene Expression Profiling Interactive Analysis 2 website analysis. The Tumor Immune Estimation Resource database was used to assess the correlation between candidate antigens and the abundance of antigen-presenting cells (APCs). Tumor-associated antigens were overexpressed in tumors and associated with prognosis, genomic alterations, and APC infiltration. A consensus cluster analysis was performed with the Consensus Cluster Plus package to identify the immune subtypes. The weighted gene coexpression network analysis (WGCNA) was used to determine the candidate biomarker molecules for appropriate populations for mRNA vaccines.

RESULTS

AURKA, CCNB1, CDC25C, CDK1, TRIP13, PES1, MCM3, PPM1G, NEK2, KIF2C, PTTG1, KPNA2, and PRC1 were identified as candidate HCC antigens for mRNA vaccine development. Four immune subtypes (IS1-IS4) and five immune gene modules of HCC were identified that were consistent in both patient cohorts. The immune subtypes showed distinct cellular and clinical characteristics. The IS1 and IS3 immune subtypes were immunologically "cold". The IS2 and IS4 immune subtypes were immunologically "hot", and the immune checkpoint genes and immunogenic cell death genes were upregulated in these subtypes. IS1-related modules were identified with the WGCNA algorithm. Ultimately, five hub genes (RBP4, KNG1, METTL7A, F12, and ABAT) were identified, and they might be potential biomarkers for mRNA vaccines.

CONCLUSION

AURKA, CCNB1, CDC25C, CDK1, TRIP13, PES1, MCM3, PPM1G, NEK2, KIF2C, PTTG1, KPNA2, and PRC1 have been identified as candidate HCC antigens for mRNA vaccine development. The IS1 and IS3 immune subtypes are suitable populations for mRNA vaccination. RBP4, KNG1, METTL7A, F12, and ABAT are potential biomarkers for mRNA vaccines.

Targeted single-cell proteomic analysis identifies new liquid biopsy biomarkers associated with multiple myeloma

Multiple myeloma (MM) is accompanied by alterations to the normal plasma cell (PC) proteome, leading to changes to the tumor microenvironment and disease progression. There is a great need for understanding the consequences that lead to MM progression and for the discovery of new biomarkers that can aid clinical diagnostics and serve as targets for therapeutics. This study demonstrates the applicability of utilizing the single-cell high-definition liquid biopsy assay (HDSCA) and imaging mass cytometry to characterize the proteomic profile of myeloma. In our study, we analyzed ~87,000 cells from seven patient samples (bone marrow and peripheral blood) across the myeloma disease spectrum and utilized our multiplexed panel to characterize the expression of clinical markers for PC classification, additional potential therapeutic targets, and the tumor microenvironment cells. Our analysis showed BCMA, ICAM3 (CD50), CD221, and CS1 (SLAMF7) as the most abundantly expressed markers on PCs across all myeloma stages, with BCMA, ICAM3, and CD221 having significantly higher expression levels on disease versus precursor PCs. Additionally, we identify significantly elevated levels of expression for CD74, MUM1, CD229, CD44, IGLL5, Cyclin D1, UBA52, and CD317 on PCs from overt disease conditions compared to those from precursor states.

Proteomic Alteration in the Progression of Multiple Myeloma: A Comprehensive Review

Multiple myeloma (MM) is an incurable hematologic malignancy. Most MM patients are diagnosed at a late stage because the early symptoms of the disease can be uncertain and nonspecific, often resembling other, more common conditions. Additionally, MM patients are commonly associated with rapid relapse and an inevitable refractory phase. MM is characterized by the abnormal proliferation of monoclonal plasma cells in the bone marrow. During the progression of MM, massive genomic alterations occur that target multiple signaling pathways and are accompanied by a multistep process involving differentiation, proliferation, and invasion. Moreover, the transformation of healthy plasma cell biology into genetically heterogeneous MM clones is driven by a variety of post-translational protein modifications (PTMs), which has complicated the discovery of effective treatments. PTMs have been identified as the most promising candidates for biomarker detection, and further research has been recommended to develop promising surrogate markers. Proteomics research has begun in MM, and a comprehensive literature review is available. However, proteomics applications in MM have yet to make significant progress. Exploration of proteomic alterations in MM is worthwhile to improve understanding of the pathophysiology of MM and to search for new treatment targets. Proteomics studies using mass spectrometry (MS) in conjunction with robust bioinformatics tools are an excellent way to learn more about protein changes and modifications during disease progression MM. This article addresses in depth the proteomic changes associated with MM disease transformation.

Current Main Topics in Multiple Myeloma

Multiple Myeloma (MM) remains a difficult to treat disease mainly due to its biological heterogeneity, of which we are more and more knowledgeable thanks to the development of increasingly sensitive molecular methods that allow us to build better prognostication models. The biological diversity translates into a wide range of clinical outcomes from long-lasting remission in some patients to very early relapse in others. In NDMM transplant eligible (TE) patients, the incorporation of mAb as daratumumab in the induction regimens, followed by autologous stem cell transplantation (ASCT) and consolidation/maintenance therapy, has led to a significant improvement of PFS and OS.; however, this outcome remains poor in ultra-high risk MM or in those who did not achieve a minimal residual disease (MRD) negativity. Several trials are exploring cytogenetic risk-adapted and MRD-driven therapies in these patients. Similarly, quadruplets-containing daratumumab, particularly when administered as continuous therapies, have improved outcome of patients not eligible for autologous transplant (NTE). Patients who become refractory to conventional therapies have noticeably poor outcomes, making their treatment a difficult challenge in need of novel strategies. In this review, we will focus on the main points regarding risk stratification, treatment and monitoring of MM, highlighting the most recent evidence that could modify the management of this still incurable disease.

Functional Amyloids in Pseudomonas aeruginosa Are Essential for the Proteome Modulation That Leads to Pathoadaptation in Pulmonary Niches

Persistence and survival of Pseudomonas aeruginosa in chronic lung infections is closely linked to the biofilm lifestyle. One biofilm component, functional amyloid of P. aeruginosa (Fap), imparts structural adaptations for biofilms; however, the role of Fap in pathogenesis is still unclear. Conservation of the fap operon encoding Fap and P. aeruginosa being an opportunistic pathogen of lung infections prompted us to explore its role in lung infection. We found that Fap is essential for establishment of lung infection in rats, as its genetic exclusion led to mild focal infection with quick resolution. Moreover, without an underlying cystic fibrosis (CF) genetic disorder, overexpression of Fap reproduced the CF pathotype. The molecular basis of Fap-mediated pulmonary adaptation was explored through surface-associated proteomics in vitro. Differential proteomics positively associated Fap expression with activation of known proteins related to pulmonary pathoadaptation, attachment, and biofilm fitness. The aggregative bacterial phenotype in the pulmonary niche correlated with Fap-influenced activation of biofilm sustainability regulators and stress response regulators that favored persistence-mediated establishment of pulmonary infection. Fap overexpression upregulated proteins that are abundant in the proteome of P. aeruginosa in colonizing CF lungs. Planktonic lifestyle, defects in anaerobic pathway, and neutrophilic evasion were key factors in the absence of Fap that impaired establishment of infection. We concluded that Fap is essential for cellular equilibration to establish pulmonary infection. Amyloid-induced bacterial aggregation subverted the immune response, leading to chronic infection by collaterally damaging tissue and reinforcing bacterial persistence. IMPORTANCE Pseudomonas aeruginosa is inextricably linked with chronic lung infections. In this study, the well-conserved Fap operon was found to be essential for pathoadaptation in pulmonary infection in a rat lung model. Moreover, the presence of Fap increased pathogenesis and biofilm sustainability by modulating bacterial physiology. Hence, a pathoadaptive role of Fap in pulmonary infections can be exploited for clinical application by targeting amyloids. Furthermore, genetic conservation and extracellular exposure of Fap make it a commendable target for such interventions.

DIGE Analysis of Immunodepleted Plasma

This chapter focuses on upstream immunodepletion of high-abundance proteins from plasma samples and subsequent analysis by fluorescence two-dimensional difference gel electrophoresis (2D-DIGE). The abundances of proteins in biofluid proteomes, such as serum, plasma, saliva, and bronchoalveolar lavage fluid (BALF), can exceed ten orders of magnitude. This substantial dynamic range is problematic for the detection of medium and low-abundance proteins by 2D-DIGE analysis. To increase the detection, quantification, and identification of medium-low-abundance proteins, the targeted depletion of known abundant proteins with antibody columns has been successfully employed. From the literature, it is clear that the performance of abundant protein depletion with immunodepletion columns has been successful in broadening the coverage of the biofluid proteome and facilitating the identification of disease-specific biomarkers. The task for a successful biomarker strategy involves the combination of a reproducible and robust fractionation method, coupled with a highly accurate quantitative method, a task that is exemplified by combining both immunodepletion and 2D-DIGE together to discover significant proteins associated with the disease phenotype.

DIGE-Based Biomarker Discovery in Blood Cancers

Cancer of blood or bone marrow-derived cells dysregulates normal hematopoiesis and accounts for over 6% of all cancer cases annually. Proteomic analyses of blood cancers have improved our understanding of disease mechanisms and identified numerous proteins of clinical relevance. For many years, gel-based proteomic studies have aided in the discovery of novel diagnostic, prognostic, and predictive biomarkers, as well as therapeutic targets, in various diseases, including blood cancer. Fluorescence two-dimensional difference gel electrophoresis (2D-DIGE) facilitates comparative proteomic research to identify differential protein expression in a simple and reproducible manner. The versatility of 2D-DIGE as a quantitative proteomic technique has provided insight into various aspects of blood cancer pathology, including disease development, prognostic subtypes, and drug resistance. The ability to couple 2D-DIGE with additional downstream mass spectrometry-based techniques yields comprehensive workflows capable of identifying proteins of biological and clinical significance. The application of 2D-DIGE in blood cancer research has significantly contributed to the increasingly important initiative of precision medicine. This chapter will focus on the influential role of 2D-DIGE as a tool in blood cancer research.

Identification of Ubiquitination-Associated Proteins Using 2D-DIGE

Ubiquitination is a post-translational modification, in which a small regulatory protein (~8.6 kDa) is tagged as a single moiety or as a chain to target proteins. Ubiquitination is the most versatile cellular regulatory mechanism, essential to the physiological and pathophysiological cellular events that regulate protein turnover, gene transcription, cell cycle progression, DNA repair, apoptosis, viral budding, and receptor-mediated endocytosis. Changes and abnormalities within the ubiquitination process can result in a plethora of diseases, including various cancers. The ubiquitination process is tightly controlled in a stepwise manner by four enzymes: E1 ubiquitin-activating enzymes, E2 ubiquitin-conjugating enzymes, E3 ubiquitin-ligating enzymes, and deubiquitinating proteases. Using fluorescence two-dimensional difference gel electrophoresis (2D-DIGE) to detect and quantitate cellular proteins associated with the ubiquitination process will facilitate the evaluation of this post-translational modification associated with the pathophysiological phenotype.

Patient-Derived Extracellular Vesicles Proteins as New Biomarkers in Multiple Myeloma - A Real-World Study

Multiple myeloma (MM) is a hematological malignancy of clonal antibody–secreting plasma cells (PCs). MM diagnosis and risk stratification rely on bone marrow (BM) biopsy, an invasive procedure prone to sample bias. Liquid biopsies, such as extracellular vesicles (EV) in peripheral blood (PB), hold promise as new minimally invasive tools. Real-world studies analyzing patient-derived EV proteome are rare. Here, we characterized a small EV protein content from PB and BM samples in a cohort of 102 monoclonal gammopathies patients routinely followed in the clinic and 223 PB and 111 BM samples were included. We investigated whether EV protein and particle concentration could predict an MM patient prognosis. We found that a high EV protein/particle ratio, or EV cargo &gt;0.6 µg/108 particles, is related to poorer survival and immune dysfunction. These results were supported at the protein level by mass spectrometry. We report a set of PB EV-proteins (PDIA3, C4BPA, BTN1A1, and TNFSF13) with a new biomarker potential for myeloma patient outcomes. The high proteomic similarity between PB and BM matched pairs supports the use of circulating EV as a counterpart of the BM EV proteome. Overall, we found that the EV protein content is related to patient outcomes, such as survival, immune dysfunction, and possibly treatment response.

iTRAQ-Based Proteomic Analysis Reveals Potential Serum Biomarkers for Pediatric Non-Hodgkin's Lymphoma

Non-Hodgkin’s lymphoma (NHL) is the third most common malignant tumor among children. However, at initial NHL diagnosis, most cases are at an advanced stage because of nonspecific clinical manifestations and currently limited diagnostic methods. This study aimed to screen and verify potential serum biomarkers of pediatric NHL using isobaric tags for relative and absolute quantification (iTRAQ)-based proteomic analysis. Serum protein expression profiles from children with B-NHL (n=20) and T-NHL (n=20) and healthy controls (n=20) were detected by utilizing iTRAQ in combination with two-dimensional liquid chromatography-tandem mass spectrometry (2D LC–MS/MS) and analyzed by applying Ingenuity Pathway Analysis (IPA). The candidate biomarkers S100A8 and LRG1 were further validated by using enzyme-linked immunosorbent assays (ELISAs). Receiver operating characteristic (ROC) analysis based on ELISA data was used to evaluate diagnostic efficacy. In total, 534 proteins were identified twice using iTRAQ combined with 2D LC–MS/MS. Further analysis identified 79 and 73 differentially expressed proteins in B-NHL and T-NHL serum, respectively, compared with control serum according to our defined criteria; 34 proteins were overexpressed and 45 proteins underexpressed in B-NHL, whereas 45 proteins were overexpressed and 28 proteins underexpressed in T-NHL (p < 0.05). IPA demonstrated a variety of signaling pathways, including acute phase response signaling and liver X receptor/retinoid X receptor (LXR/RXR) activation, to be strongly associated with pediatric NHL. S100A8 and LRG1 were elevated in NHL patients compared to normal controls according to ELISA (p < 0.05), which was consistent with iTRAQ results. The areas under the ROC curves of S100A8, LRG1, and the combination of S100A8 and LRG1 were 0.873, 0.898 and 0.970, respectively. Our findings indicate that analysis of the serum proteome using iTRAQ combined with 2D LC–MS/MS is a feasible approach for biomarker discovery. Serum S100A8 and LRG1 are promising candidate biomarkers for pediatric NHL, and these differential proteins illustrate a novel pathogenesis and may be clinically helpful for NHL diagnosis in the future.

Nontargeted and targeted metabolomics approaches reveal the key amino acid alterations involved in multiple myeloma

PURPOSE

Multiple myeloma (MM), a kind of malignant neoplasm of clonal plasma cells in the bone marrow, is a refractory disease. Understanding the metabolism disorders and identification of metabolomics pathways as well as key metabolites will provide new insights for exploring diagnosis and therapeutic targets of MM.

METHODS

We conducted nontargeted metabolomics analysis of MM patients and normal controls (NC) using ultra-high-performance liquid chromatography (UHPLC) combined with quadrupole time-of-flight mass spectrometry (Q-TOF-MS) in 40 cases of cohort 1 subjects. The targeted metabolomics analysis of amino acids using multiple reaction monitoring-mass spectrometry (MRM-MS) was also performed in 30 cases of cohort 1 and 30 cases of cohort 2 participants, to comprehensively investigate the metabolomics disorders of MM.

RESULTS

The nontargeted metabolomics analysis in cohort 1 indicated that there was a significant metabolic signature change between MM patients and NC. The differential metabolites were mainly enriched in metabolic pathways related to amino acid metabolism, such as protein digestion and absorption, and biosynthesis of amino acids. Further, the targeted metabolomics analysis of amino acids in both cohort 1 and cohort 2 revealed differential metabolic profiling between MM patients and NC. We identified 12 and 14 amino acid metabolites with altered abundance in MM patients compared to NC subjects, in cohort 1 and cohort 2, respectively. Besides, key differential amino acid metabolites, such as choline, creatinine, leucine, tryptophan, and valine, may discriminate MM patients from NC. Moreover, the differential amino acid metabolites were associated with clinical indicators of MM patients.

CONCLUSIONS

Our findings indicate that amino acid metabolism disorders are involved in MM. The differential profiles reveal the potential utility of key amino acid metabolites as diagnostic biomarkers of MM. The alterations in metabolome, especially the amino acid metabolome, may provide more evidences for elucidating the pathogenesis and development of MM.

Xenograft cancer vaccines prepared from immunodeficient mice increase tumor antigen diversity and host T cell efficiency against colorectal cancers

Autologous cancer vaccines (ACVs) are a desirable approach for personalized medicine, but the efficiency of ACVs remains unsatisfactory due to their low immunogenicity. This study developed a platform that can enhance the immunogenicity of ACVs by transplanting the tumors into immunodeficient mice. The CT26 cell line was inoculated into severe combined immunodeficient mice (SCID) for vaccine preparation where escalates tumor development, subsequently diversifying the tumor antigenic topology. CT26/SCID cancer vaccines significantly inhibited tumor growth, increased the amount of tumor infiltrating lymphocytes, and triggered Th-1 predominant immune responses. Tumor antigenic profiles of CT26/SCID cells were further analyzed by liquid chromatography-tandem mass spectrometry. Compared to CT26 parental cells, a total of 428 differentially expressed proteins (DEPs) were detected. These DEPs revealed that CT26/SCID cells overexpressed several novel therapeutic targets, including KNG1, apoA-I and, β2-GPI, which can trigger cytotoxic T cells towards Th-1 predominant immune responses and directly suppress proliferation in tumors. CT26/SCID cancer vaccines can be easily manufactured, while traits of triggering stronger antigen-specific Th-1 immune activity against tumors, are retained. Results of this study provide an effective proof-of-concept of an ACV for personalized cancer immunotherapy.

Clinical Proteomics of Biofluids in Haematological Malignancies

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.

Next-Generation Biomarkers in Multiple Myeloma: Understanding the Molecular Basis for Potential Use in Diagnosis and Prognosis

Multiple myeloma (MM) is considered to be the second most common blood malignancy and it is characterized by abnormal proliferation and an accumulation of malignant plasma cells in the bone marrow. Although the currently utilized markers in the diagnosis and assessment of MM are showing promising results, the incidence and mortality rate of the disease are still high. Therefore, exploring and developing better diagnostic or prognostic biomarkers have drawn global interest. In the present review, we highlight some of the recently reported and investigated novel biomarkers that have great potentials as diagnostic and/or prognostic tools in MM. These biomarkers include angiogenic markers, miRNAs as well as proteomic and immunological biomarkers. Moreover, we present some of the advanced methodologies that could be utilized in the early and competent diagnosis of MM. The present review also focuses on understanding the molecular concepts and pathways involved in these biomarkers in order to validate and efficiently utilize them. The present review may also help in identifying areas of improvement for better diagnosis and superior outcomes of MM.

Quantitative proteomics characterization of cancer biomarkers and treatment

Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.