Liquid biopsy of peripheral blood using mass spectrometry detects primary extramedullary disease in multiple myeloma patients
Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
NU21-03-00076
Ministerstvo Zdravotnictví Ceské Republiky
MUNI/A/1587/2023
Masarykova Univerzita
LX22NPO5102
Next generation EU
CZ.02.01.01/00/22_008/0004644
Ministerstvo skolstvi, mladeze a telovychovy
PubMed
39138296
PubMed Central
PMC11322162
DOI
10.1038/s41598-024-69408-1
PII: 10.1038/s41598-024-69408-1
Knihovny.cz E-zdroje
- Klíčová slova
- Extramedullary disease, Liquid biopsy, MALDI-TOF mass spectrometry, Machine learning, Multiple myeloma, Partial least squares-discriminant analysis, Principal component analysis,
- MeSH
- lidé středního věku MeSH
- lidé MeSH
- mnohočetný myelom * krev diagnóza MeSH
- nádorové biomarkery krev MeSH
- senioři MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice metody MeSH
- tekutá biopsie metody MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- nádorové biomarkery MeSH
Multiple myeloma (MM) is the second most prevalent hematological malignancy, characterized by infiltration of the bone marrow by malignant plasma cells. Extramedullary disease (EMD) represents a more aggressive condition involving the migration of a subclone of plasma cells to paraskeletal or extraskeletal sites. Liquid biopsies could improve and speed diagnosis, as they can better capture the disease heterogeneity while lowering patients' discomfort due to minimal invasiveness. Recent studies have confirmed alterations in the proteome across various malignancies, suggesting specific changes in protein classes. In this study, we show that MALDI-TOF mass spectrometry fingerprinting of peripheral blood can differentiate between MM and primary EMD patients. We constructed a predictive model using a supervised learning method, partial least squares-discriminant analysis (PLS-DA) and evaluated its generalization performance on a test dataset. The outcome of this analysis is a method that predicts specifically primary EMD with high sensitivity (86.4%), accuracy (78.4%), and specificity (72.4%). Given the simplicity of this approach and its minimally invasive character, this method provides rapid identification of primary EMD and could prove helpful in clinical practice.
Central European Institute of Technology Masaryk University Brno Czech Republic
Department of Chemistry Faculty of Science Masaryk University Brno Czech Republic
Department of Clinical Hematology University Hospital Brno Brno Czech Republic
Department of Hematooncology Faculty of Medicine University of Ostrava Ostrava Czech Republic
Department of Hematooncology University Hospital Ostrava Ostrava Czech Republic
Department of Histology and Embryology Faculty of Medicine Masaryk University Brno Czech Republic
Department of Internal Medicine Hematology and Oncology University Hospital Brno Brno Czech Republic
International Clinical Research Center St Anne's University Hospital Brno Brno Czech Republic
Research Centre for Applied Molecular Oncology Masaryk Memorial Cancer Institute Brno Czech Republic
Zobrazit více v PubMed
Rajkumar, S. V. Multiple myeloma: 2022 update on diagnosis, risk-stratification and management. Am. J. Hematol.97, 1086–1107 (2022). PubMed PMC
Rajkumar, S. V. et al. International myeloma working group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol.15, e538-548 (2014). PubMed
Blade, J. et al. Extramedullary disease in multiple myeloma: A systematic literature review. Blood Cancer J.12, 1–10 (2022). PubMed PMC
Rosinol, L. et al. Expert review on soft-tissue plasmacytomas in multiple myeloma: Definition, disease assessment and treatment considerations. Br. J. Haematol.194, 496–507 (2021). PubMed
Sevcikova, S. et al. Extramedullary disease in multiple myeloma - controversies and future directions. Blood Rev.36, 32–39 (2019). PubMed
Cavo, M. et al. Role of 18F-FDG PET/CT in the diagnosis and management of multiple myeloma and other plasma cell disorders: A consensus statement by the International Myeloma Working Group. Lancet Oncol.18, e206–e217 (2017). PubMed
Besse, L. et al. Cytogenetics in multiple myeloma patients progressing into extramedullary disease. Eur. J. Haematol.97, 93–100 (2016). PubMed
Pour, L. et al. Soft-tissue extramedullary multiple myeloma prognosis is significantly worse in comparison to bone-related extramedullary relapse. Haematologica99, 360–364 (2014). PubMed PMC
Stork, M. et al. Unexpected heterogeneity of newly diagnosed multiple myeloma patients with plasmacytomas. Biomedicines10, 2535 (2022). PubMed PMC
Stork, M. et al. Identification of patients at high risk of secondary extramedullary multiple myeloma development. Br. J. Haematol.196, 954–962 (2022). PubMed PMC
Jelinek, T. et al. More than 2% of circulating tumor plasma cells defines plasma cell leukemia-like multiple myeloma. J. Clin. Oncol.41, 1383–1392 (2023). PubMed PMC
Manier, S. et al. Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma. Nat. Commun.9, 1691 (2018). PubMed PMC
Vrabel, D. et al. Dynamics of tumor-specific cfDNA in response to therapy in multiple myeloma patients. Eur. J. Haematol.104, 190–197 (2020). PubMed PMC
Besse, L. et al. Circulating serum MicroRNA-130a as a novel putative marker of extramedullary myeloma. PLoS One10, e0137294 (2015). PubMed PMC
Kubiczkova, L. et al. Circulating serum microRNAs as novel diagnostic and prognostic biomarkers for multiple myeloma and monoclonal gammopathy of undetermined significance. Haematologica99, 511–518 (2014). PubMed PMC
Sedlarikova, L. et al. Deregulated expression of long non-coding RNA UCA1 in multiple myeloma. Eur. J. Haematol.99, 223–233 (2017). PubMed
Deulofeu, M. et al. Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma. Sci. Rep.9, 7975 (2019). PubMed PMC
Wang, Q.-T. et al. Construction of A multiple myeloma diagnostic model by magnetic bead-based MALDI-TOF mass spectrometry of serum and pattern recognition software. Anatom. Record292, 604–610 (2009). PubMed
Manier, S. et al. Prognostic role of circulating exosomal miRNAs in multiple myeloma. Blood129, 2429–2436 (2017). PubMed PMC
Barcelo, F. et al. MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance. PLoS One13, e0201793 (2018). PubMed PMC
Cho, Y.-T. et al. Matrix-assisted laser desorption ionization/time-of-flight mass spectrometry for clinical diagnosis. Clin. Chim. Acta415, 266–275 (2013). PubMed
Chung, L. et al. Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer. Breast Cancer Res.16, R63 (2014). PubMed PMC
Eveillard, M. et al. Using MALDI-TOF mass spectrometry in peripheral blood for the follow up of newly diagnosed multiple myeloma patients treated with daratumumab-based combination therapy. Clin. Chim. Acta516, 136–141 (2021). PubMed PMC
Jannetto, P. J. & Fitzgerald, R. L. Effective use of mass spectrometry in the clinical laboratory. Clin. Chem.62, 92–98 (2016). PubMed
Willrich, M. A. V., Murray, D. L. & Kyle, R. A. Laboratory testing for monoclonal gammopathies: Focus on monoclonal gammopathy of undetermined significance and smoldering multiple myeloma. Clin. Biochem.51, 38–47 (2018). PubMed
Wolrab, D. et al. Lipidomic profiling of human serum enables detection of pancreatic cancer. Nat. Commun.13, 124 (2022). PubMed PMC
Pecinka, L. et al. Improved screening of monoclonal gammopathy patients by MALDI-TOF mass spectrometry. J. Am. Soc. Mass Spectrom.34, 2646 (2023). PubMed PMC
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw.28, 1–26 (2008). PubMed
Mithraprabhu, S., Chen, M., Savvidou, I., Reale, A. & Spencer, A. Liquid biopsy: An evolving paradigm for the biological characterisation of plasma cell disorders. Leukemia35, 2771–2783 (2021). PubMed
El-Khoury, H. et al. Prevalence of monoclonal gammopathies and clinical outcomes in a high-risk US population screened by mass spectrometry: A multicentre cohort study. Lancet Haematol.9, e340–e349 (2022). PubMed PMC
Fatica, E. M. et al. MALDI-TOF mass spectrometry can distinguish immunofixation bands of the same isotype as monoclonal or biclonal proteins. Clin. Biochem.97, 67–73 (2021). PubMed
Li, J. et al. MALDI-TOF-MS for rapid screening analysis of M-protein in serum. Front. Oncol.12, 1073479 (2022). PubMed PMC
Murray, D. L. et al. Mass spectrometry for the evaluation of monoclonal proteins in multiple myeloma and related disorders: An international myeloma working group mass spectrometry committee report. Blood Cancer J.11, 1–6 (2021). PubMed PMC
Eveillard, M. et al. Comparison of MALDI-TOF mass spectrometry analysis of peripheral blood and bone marrow based flow cytometry for tracking measurable residual disease in patients with multiple myeloma. Br. J. Haematol.189, 904–907 (2020). PubMed PMC
Liu, C. et al. MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of hepatocellular carcinoma. Am. J. Clin. Pathol.134, 235–241 (2010). PubMed
Long, S. et al. Nanoporous silica coupled MALDI-TOF MS detection of Bence-Jones proteins in human urine for diagnosis of multiple myeloma. Talanta200, 288–292 (2019). PubMed
Santockyte, R. et al. High-throughput therapeutic antibody interference-free high-resolution mass spectrometry assay for monitoring M-proteins in multiple myeloma. Anal. Chem. 93, 834–842 (2021). PubMed
Anderson, K. C. et al. Minimal residual disease in myeloma: Application for clinical care and new drug registration. Clin. Cancer Res.27, 5195–5212 (2021). PubMed PMC
Barnidge, D. R., Krick, T. P., Griffin, T. J. & Murray, D. L. Using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to detect monoclonal immunoglobulin light chains in serum and urine. Rapid Commun. Mass Spectrom.29, 2057–2060 (2015). PubMed
Barnidge, D. R. et al. Using mass spectrometry to monitor monoclonal immunoglobulins in patients with a monoclonal gammopathy. J. Proteom. Res.13, 1419–1427 (2014). PubMed
Bergen, H. R. et al. Clonotypic light chain peptides identified for monitoring minimal residual disease in multiple myeloma without bone marrow aspiration. Clin. Chem. 62, 243–251 (2016). PubMed PMC
Chapman, J. R. & Thoren, K. L. Tracking of low disease burden in multiple myeloma: Using mass spectrometry assays in peripheral blood. Best Pract. Res. Clin. Haematol.33, 101142 (2020). PubMed
Dasari, S. et al. Detection of plasma cell disorders by mass spectrometry: A comprehensive review of 19,523 cases. Mayo Clin. Proc.97, 294–307 (2022). PubMed
Murray, D. et al. Detection and prevalence of monoclonal gammopathy of undetermined significance: a study utilizing mass spectrometry-based monoclonal immunoglobulin rapid accurate mass measurement. Blood Cancer J.9, 102 (2019). PubMed PMC
He, A. et al. Detection of serum tumor markers in multiple myeloma using the CLINPROT system. Int. J. Hematol.95, 668–674 (2012). PubMed
Bai, J. et al. Variability of serum novel serum peptide biomarkers correlates with the disease states of multiple myeloma. Clin. Proteom.16, 17 (2019). PubMed PMC
Vanhara, P. et al. Intact cell mass spectrometry for embryonic stem cell biotyping. In Mass spectrometry in life sciences and clinical laboratory (ed. Mitulovic, G.) (IntechOpen, 2020).
Gibb, S. & Strimmer, K. MALDIquant: A versatile R package for the analysis of mass spectrometry data. Bioinformatics28, 2270–2271 (2012). PubMed
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria). Available at https://www.R-project.org (2021).
Bromba, M. U. A. & Ziegler, H. Application hints for Savitzky-Golay digital smoothing filters. Anal. Chem.53, 1583–1586 (1981).
Rousseeuw, P. J. & Croux, C. Alternatives to the median absolute deviation. J. Am. Stat. Assoc.88, 1273–1283 (1993).
Ryan, C. G., Clayton, E., Griffin, W. L., Sie, S. H. & Cousens, D. R. SNIP, a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms34, 396–402 (1988).
Kuhn, M. & Johnson, K. Data pre-processing. In Applied Predictive Modeling (eds. Kuhn, M. & Johnson, K.) 27–59 (Springer, New York, NY, 2013).