BACKGROUND: Multiple myeloma (MM) represents the second most common hematological malignancy characterized by the infiltration of the bone marrow by plasma cells that produce monoclonal immunoglobulin. While the quality and length of life of MM patients have significantly increased, MM remains a hard-to-treat disease; almost all patients relapse. As MM is highly heterogenous, patients relapse at different times. It is currently not possible to predict when relapse will occur; numerous studies investigating the dysregulation of non-coding RNA molecules in cancer suggest that microRNAs could be good markers of relapse. RESULTS: Using small RNA sequencing, we profiled microRNA expression in peripheral blood in three groups of MM patients who relapsed at different intervals. In total, 24 microRNAs were significantly dysregulated among analyzed subgroups. Independent validation by RT-qPCR confirmed changed levels of miR-598-3p in MM patients with different times to relapse. At the same time, differences in the mass spectra between groups were identified using matrix-assisted laser desorption/ionization time of flight mass spectrometry. All results were analyzed by machine learning. CONCLUSION: Mass spectrometry coupled with machine learning shows potential as a reliable, rapid, and cost-effective preliminary screening technique to supplement current diagnostics.

• Keywords
• Liquid biopsy, MALDI-TOF MS, Machine learning, Multiple myeloma, Relapse, Small RNA seq, microRNA,
• Publication type
• Journal Article MeSH

Multiple myeloma is a plasma cell malignancy characterized by an abnormal increase in monoclonal immunoglobulins. Despite significant advances in treatment, some patients progress to more aggressive forms of multiple myeloma, including extramedullary disease or plasma cell leukemia. Although the exact molecular mechanisms are not known, several studies have confirmed the involvement of small extracellular vesicle-enriched microRNAs in multiple myeloma progression. Therefore, we performed expression profiling of these molecules in bone marrow plasma of multiple myeloma, extramedullary disease, and plasma cell leukemia patients using small RNA sequencing to identify novel molecules involved in disease pathogenesis. In total, 42 microRNAs were significantly dysregulated among analyzed subgroups. Independent validation by RT-qPCR confirmed elevated levels of miR-140-3p, miR-584-5p, miR-191-5p, and miR-143-3p in multiple myeloma patients compared to extramedullary disease and plasma cell leukemia patients. Subsequent statistical analysis revealed significant correlations between patient clinical characteristics or flow cytometry parameters and microRNA expression. These results indicate that dysregulation of microRNAs could contribute to multiple myeloma progression.

• Keywords
• extramedullary disease, microRNAs, multiple myeloma, plasma cell leukemia, small RNA sequencing, small extracellular vesicles,
• MeSH
• Adult MeSH
• Extracellular Vesicles * metabolism   genetics   pathology   MeSH
• Bone Marrow * pathology   metabolism   MeSH
• Middle Aged MeSH
• Humans MeSH
• MicroRNAs * genetics   MeSH
• Multiple Myeloma * genetics   pathology   metabolism   MeSH
• Biomarkers, Tumor genetics   MeSH
• Plasma Cells metabolism   MeSH
• Leukemia, Plasma Cell * genetics   pathology   metabolism   MeSH
• Aged MeSH
• Gene Expression Profiling MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• MicroRNAs * MeSH
• Biomarkers, Tumor MeSH

Monoclonal gammopathies are a group of blood diseases characterized by presence of abnormal immunoglobulins in peripheral blood and/or urine of patients. Multiple myeloma and plasma cell leukemia are monoclonal gammopathies with unclear etiology, caused by malignant transformation of bone marrow plasma cells. Mass spectrometry with matrix-assisted laser desorption/ionization and time-of-flight detection is commonly used for investigation of the peptidome and small proteome of blood plasma with high accuracy, robustness, and cost-effectivity. In addition, mass spectrometry coupled with advanced statistics can be used for molecular profiling, classification, and diagnosis of liquid biopsies and tissue specimens in various malignancies. Despite the fact there have been fully optimized protocols for mass spectrometry of normal blood plasma available for decades, in monoclonal gammopathy patients, the massive alterations of biophysical and biochemical parameters of peripheral blood plasma often limit the mass spectrometry measurements. In this paper, we present a new two-step extraction protocol and demonstrated the enhanced resolution and intensity (>50×) of mass spectra obtained from extracts of peripheral blood plasma from monoclonal gammopathy patients. When coupled with advanced statistics and machine learning, the mass spectra profiles enabled the direct identification, classification, and discrimination of multiple myeloma and plasma cell leukemia patients with high accuracy and precision. A model based on PLS-DA achieved the best performance with 71.5% accuracy (95% confidence interval, CI = 57.1-83.3%) when the 10× repeated 5-fold CV was performed. In summary, the two-step extraction protocol improved the analysis of monoclonal gammopathy peripheral blood plasma samples by mass spectrometry and provided a tool for addressing the complex molecular etiology of monoclonal gammopathies.

• Keywords
• MALDI-TOF mass spectrometry, fingerprinting, machine learning, molecular profiling, monoclonal gammopathy, multiple myeloma, partial least-squares-discriminant analysis, plasma cell leukemia, principal component analysis,
• MeSH
• Plasma MeSH
• Humans MeSH
• Multiple Myeloma * diagnosis   MeSH
• Paraproteinemias * diagnosis   MeSH
• Leukemia, Plasma Cell * MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

In multiple myeloma (MM), malignant plasma cells infiltrate the bone marrow. In some cases, plasma cells migrate out of the bone marrow creating either para-skeletal plasmacytomas (PS) or infiltrating soft tissues as extramedullary plasmacytomas (EMD). The aim of this study was to define risk groups in newly diagnosed MM (NDMM) patients with PS and EMD plasmacytomas. In total, 523 NDMM patients with PS plasmacytomas and 196 NDMM patients with EMD plasmacytomas were diagnosed in the Czech Republic between 2004 and 2021 using modern imaging methods. Patients’ data were analyzed from the Registry of Monoclonal Gammopathies of the Czech Myeloma Group. In NDMM patients with PS plasmacytomas, we found a subgroup with <5% of bone-marrow plasma cells to have the best prognosis (mPFS: 58.3 months (95% CI: 33.0−NA); mOS: not reached). The subgroup with >5% of bone-marrow plasma cells and ≥3 plasmacytomas had the worst prognosis (mPFS: 19.3 months (95% CI: 13.4−28.8), p < 0.001; mOS: 27.9 months (95% CI: 19.3−67.8), p < 0.001). Our results show association between tumor burden and prognosis of NDMM patients with plasmacytomas. In the case of PS plasmacytomas, NDMM patients with low BM PC infiltration have an excellent prognosis.

• Keywords
• multiple myeloma, plasmacytomas, risk factors, survival,
• Publication type
• Journal Article MeSH