Improved Screening of Monoclonal Gammopathy Patients by MALDI-TOF Mass Spectrometry

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Publikace nespadá pod Pedagogickou fakultu, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.
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PEČINKA Lukáš VLACHOVÁ Monika MORÁŇ Lukáš GREGOROVÁ Jana POROKH Volodymyr KOVAČOVICOVÁ Petra ALMÁŠI Martina POUR Luděk ŠTORK Martin HAVEL Josef ŠEVČÍKOVÁ Sabina VAŇHARA Petr

Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj Journal of the American Society for Mass Spectrometry
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www https://pubs.acs.org/doi/10.1021/jasms.3c00166
Doi http://dx.doi.org/10.1021/jasms.3c00166
Klíčová slova MALDI-TOF mass spectrometry; multiple myeloma; plasma cell leukemia; monoclonal gammopathy; principal component analysis; machine learning; partial least-squares-discriminant analysis; molecular profiling; fingerprinting
Přiložené soubory
Popis 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.
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