Current and future biomarkers for risk-stratification and treatment personalisation in multiple myeloma

DAZAP1 facilitates the alternative splicing of KITLG to promote multiple myeloma cell proliferation via ERK signaling pathway

Multiple myeloma (MM) is an incurable plasma cell malignancy, in which alternative pre-mRNA splicing (AS) acts as one of the key transcriptome modifier. The Deleted in Azoospermia-Associated Protein 1 (DAZAP1) is a splicing factor that has been identified as an oncogene in multiple cancers, yet its role in MM proliferation remains unclear. We first analyzed MM clinical databases and found that MM patients with elevated DAZAP1 had a poor survival. Furthermore, we overexpressed DAZAP1 by lentiviral transfection and utilized siRNA silencing the expression of DAZAP1 in MM cells. DAZAP1 promoted MM cell proliferation in vitro and accelerated MM xenograft tumor growth in vivo. KEGG pathway enrichment analysis showed that ERK signaling pathway was activated in DAZAP1-OE MM cells. The analyses of RIP-seq and RIP-qPCR revealed that DAZAP1 activated alternative splicing of KIT proto-oncogene ligand (KITLG) mRNA. Further study validated that DAZAP1 increased ERK phosphorylation via modulating alternative splicing of KITLG mRNA to promote MM cell proliferation. In conclusion, we establish DAZAP1 as a tumor-promoting gene with therapeutic potential and provide mechanistic insights into targeting DAZAP1 as a new strategy for the diagnosis and treatment of MM.

Clinical Application of Biomarkers for Hematologic Malignancies

Over the last decade, significant advancements have been made in the molecular mechanisms, diagnostic methods, prognostication, and treatment options in hematologic malignancies. As the treatment landscape continues to expand, personalized treatment is much more important.

With the development of new technologies, more sensitive evaluation of residual disease using flow cytometry and next generation sequencing is possible nowadays. Although some conventional biomarkers preserve their significance, novel potential biomarkers accurately detect the mutational landscape of different cancers, and also, serve as prognostic and predictive biomarkers, which can be used in evaluating therapy responses and relapses. It is likely that we will be able to offer a more targeted and risk-adapted therapeutic approach to patients with hematologic malignancies guided by these potential biomarkers. This chapter summarizes the biomarkers used (or proposed to be used) in the diagnosis and/or monitoring of hematologic neoplasms.;

Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response

Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers through Machine Learning (ML) algorithms still remains to be fully explored. Here, we present a Multi Learning Training approach (MuLT) combining supervised, unsupervised and self-supervised learning algorithms, to examine the predictive value of heterogeneous treatment outcomes for Multiple Myeloma (MM). We show that gene expression values improve the treatment sensitivity prediction and recapitulates genetic abnormalities detected by Fluorescence in situ hybridization (FISH) testing. MuLT performance was assessed by cross-validation experiments, in which it predicted treatment sensitivity with 68.70% of AUC. Finally, simulations showed numerical evidences that in average 17.07% of patients could get better response to a different treatment at the first line.

Case Report of a Rare Incidence of IgH Amplification Leading to Acute Kidney Injury in a Multiple Myeloma Patient

We present a case report of a 62-year-old male, treated for kappa light chain multiple myeloma with chemotherapy followed by autologous stem cell transplant (ASCT) in 2014. He has been in complete remission for 4 years. In 2018, he was evaluated for hypercreatinemia and acute kidney injury(AKI) with a suspicion of disease relapse; he underwent evaluation with bone marrow aspiration cytology which showed no evidence of relapse. However, careful cytogenetic analyses showed IgH amplification (14q32) which probably was the cause for AKI in the absence of any structural abnormality in the kidney. Heavy chain deposition leads to AKI in multiple myeloma, and its association with IgH amplification leading to AKI is reported here. Though heavy chain deposition leading to AKI is common, IgH amplification at chromosome level is the first case observed.

Metabolic markers for diagnosis and risk-prediction of multiple myeloma

AIMS

To discriminate metabolic biomarkers for diagnosis and risk prediction of multiple myeloma (MM) on a basis of metabolic characteristics in systemic circulation and local pathogenic niche.

MAIN METHODS

A gas chromatography mass spectrometry-based untargeted metabolomics analysis was performed within the bone marrow (BM) supernatants and peripheral plasma from healthy donors and patients with MM.

KEY FINDINGS

Distinct metabolic features between MM patients and healthy volunteers were profiled in both BM and plasma. Metabolic profiles of subgroups in which MM patients undergo high/medium/low risk displayed risk-dependent metabolic shift especially in BM. In MM patients, up-regulated glutamate level and down-regulated glutamine level in BM indicated enhanced glutamate metabolism which provided NH₄⁺ for ammonia utilization. This resulted in increased level of urea and creatinine produced from urea cycle, arginine and proline metabolism in both BM and plasma collected from MM patients. The disorders of tricarboxylic acid cycle and carnitine synthesis were unique in BM of MM patients. Receiver operating characteristic curve analysis indicated that aspartate was a candidate plasma biomarker for diagnosis with the highest sensitivity and specificity in both BM and plasma. Threonine was identified as a preferential plasma biomarker for risk prediction due to significant relation with various risk indexes of MM in both BM and plasma.

SIGNIFICANCE

The perturbed glutamate metabolism and carnitine synthesis in BM of MM patients provided a new sight on pathogenesis of MM. The plasma level of aspartate and threonine may become a preferential metabolic marker for diagnosis and risk prediction of MM, respectively.

Development of novel methods for non-canonical myeloma protein analysis with an innovative adaptation of immunofixation electrophoresis, native top-down mass spectrometry, and middle-down de novo sequencing

Objectives

Multiple myeloma (MM) is a malignant plasma cell neoplasm, requiring the integration of clinical examination, laboratory and radiological investigations for diagnosis. Detection and isotypic identification of the monoclonal protein(s) and measurement of other relevant biomarkers in serum and urine are pivotal analyses. However, occasionally this approach fails to characterize complex protein signatures. Here we describe the development and application of next generation mass spectrometry (MS) techniques, and a novel adaptation of immunofixation, to interrogate non-canonical monoclonal immunoproteins.

Methods

Immunoprecipitation immunofixation (IP-IFE) was performed on a Sebia Hydrasys Scan2. Middle-down de novo sequencing and native MS were performed with multiple instruments (21T FT-ICR, Q Exactive HF, Orbitrap Fusion Lumos, and Orbitrap Eclipse). Post-acquisition data analysis was performed using Xcalibur Qual Browser, ProSight Lite, and TDValidator.

Results

We adapted a novel variation of immunofixation electrophoresis (IFE) with an antibody-specific immunosubtraction step, providing insight into the clonal signature of gamma-zone monoclonal immunoglobulin (M-protein) species. We developed and applied advanced mass spectrometric techniques such as middle-down de novo sequencing to attain in-depth characterization of the primary sequence of an M-protein. Quaternary structures of M-proteins were elucidated by native MS, revealing a previously unprecedented non-covalently associated hetero-tetrameric immunoglobulin.

Conclusions

Next generation proteomic solutions offer great potential for characterizing complex protein structures and may eventually replace current electrophoretic approaches for the identification and quantification of M-proteins. They can also contribute to greater understanding of MM pathogenesis, enabling classification of patients into new subtypes, improved risk stratification and the potential to inform decisions on future personalized treatment modalities.

Proteomics-inspired precision medicine for treating and understanding multiple myeloma

Introduction

Remarkable progress in molecular characterization methods has led to significant improvements in how we manage multiple myeloma (MM). The introduction of novel therapies has led to significant improvements in overall survival over the past 10 years. However, MM remains incurable and treatment choice is largely based on outdated risk-adaptive strategies that do not factor in improved treatment outcomes in the context of modern therapies.

Areas covered

This review discusses current risk-adaptive strategies in MM and the clinical application of proteomics in the monitoring of treatment response, disease progression, and minimal residual disease (MRD). We also discuss promising biomarkers of disease progression, treatment response, and chemoresistance. Finally, we will discuss an immunomics-based approach to monoclonal antibody (mAb), vaccine, and CAR-T cell development.

Expert opinion

It is an exciting era in oncology with basic scientific knowledge translating in novel therapeutic approaches to improve patient outcomes. With the advent of effective immunotherapies and targeted therapies, it has become crucial to identify biomarkers to aid in the stratification of patients based on anticipated sensitivity to chemotherapy. As a paradigm of diseases highly dependent on protein homeostasis, multiple myeloma provides the perfect opportunity to investigate the use of proteomics to aid in precision medicine.

Mechanical segregation and capturing of clonal circulating plasma cells in multiple myeloma using micropillar-integrated microfluidic device

Multiple myeloma (MM), the disorder of plasma cells, is the second most common type of hematological cancer and is responsible for approximately 20% of deaths from hematological malignancies. The current gold standard for MM diagnosis includes invasive bone marrow aspiration. However, it lacks the sensitivity to detect minimal residual disease, and the nonuniform distribution of clonal plasma cells (CPCs) within bone marrow also often results in inaccurate reporting. Serum and urine assessment of monoclonal proteins, such as Kappa light chains, is another commonly used approach for MM diagnosis. Although it is noninvasive, the level of paraprotein elevation is still too low for detecting minimal residual disease and nonsecretive MM. Circulating CPCs (cCPCs) have been reported to be present in the peripheral blood of MM patients, and high levels of cCPCs were shown to correlate with poor survival. This suggests a potential noninvasive approach for MM disease progress monitoring and prognosis. In this study, we developed a mechanical property-based microfluidic platform to capture cCPCs. Using human myeloma cancer cell lines spiked in healthy donor blood, the microfluidic platform demonstrates high enrichment ratio (>500) and sufficient capture efficiency (40%-55%). Patient samples were also assessed to investigate the diagnostic potential of cCPCs for MM by correlating with the levels of Kappa light chains in patients.