Liquid biopsy of peripheral blood using mass spectrometry detects primary extramedullary disease in multiple myeloma patients
Jazyk angličtina Země Anglie, Velká Británie 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
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