MALDI-TOF mass spectrometry distinguishes daratumumab from M-proteins Journal Article


Authors: Moore, L. M.; Cho, S.; Thoren, K. L.
Article Title: MALDI-TOF mass spectrometry distinguishes daratumumab from M-proteins
Abstract: Background: Daratumumab, a therapeutic IgG kappa monoclonal antibody, can cause a false positive interference on electrophoretic assays that are routinely used to monitor patients with monoclonal gammopathies. In this study, we evaluate the ability of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to distinguish daratumumab from disease-related IgG kappa monoclonal proteins (M-protein). Methods: Waste clinical samples from 31 patients who were receiving daratumumab and had a history of IgG kappa monoclonal gammopathy were collected. Immunoglobulins were purified from serum and analyzed by MALDI-TOF MS. Mass spectra were assessed for the presence of distinct monoclonal proteins. For samples in which only one monoclonal peak was identified near the expected m/z of daratumumab, the Hydrashift 2/4 Daratumumab Assay was used to confirm the presence of an M-protein. Results: Using MALDI-TOF MS, daratumumab could be distinguished from M-proteins in 26 out of 31 samples (84%). Results from 2 samples were inconclusive since the M-protein was not detected by the Hydrashift assay and may also be undetectable by MALDI-TOF MS. Comparatively, daratumumab was distinguishable from M-proteins in 14 out of 31 samples (45%) by immunofixation. Conclusions: MALDI-TOF MS offers greater specificity compared to immunofixation for distinguishing daratumumab from M-proteins. © 2019
Keywords: multiple myeloma; maldi-tof mass spectrometry; daratumumab; monoclonal immunoglobulin
Journal Title: Clinica Chimica Acta
Volume: 492
ISSN: 0009-8981
Publisher: Elsevier Science BV  
Date Published: 2019-05-01
Start Page: 91
End Page: 94
Language: English
DOI: 10.1016/j.cca.2019.02.017
PROVIDER: scopus
PUBMED: 30790545
PMCID: PMC6524149
DOI/URL:
Notes: Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Katie Lynn Thoren
    37 Thoren
  2. Sun Min Cho
    11 Cho
  3. Lauren Michelle Moore
    2 Moore