Abstract
Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.
Highlights
Monoclonal gammopathies are a group of diseases characterized by increased amounts of abnormal immunoglobulin produced by a clone of plasma or lymphoid cells
Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells (PC) that abrogate physiological hematopoiesis in the bone marrow; these cells are heavily dependent on the bone marrow microenvironment for survival[2]
That Artificial Neural Networks (ANNs) can recognize informative patterns in mass spectra acquired from diseased tissues[25] or stem cells cultures[26]
Summary
Monoclonal gammopathies are a group of diseases characterized by increased amounts of abnormal immunoglobulin produced by a clone of plasma or lymphoid cells. Discrimination of disease-specific molecular patterns in peripheral blood of MM patients can provide a promising approach for early diagnostics and follow-up. We were curious whether information hidden in complex spectral patterns can provide a suitable input for ANNs to classify low mass spectral profiles of MM patients and age/sex-matched healthy donors.
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