Vertical cutoff methods in serum protein electrophoresis for the measurement of monoclonal protein concentrations: Which is best?

Recommendations for the study of monoclonal gammopathies in the clinical laboratory. A consensus of the Spanish Society of Laboratory Medicine and the Spanish Society of Hematology and Hemotherapy. Part I: Update on laboratory tests for the study of monoclonal gammopathies

Monoclonal gammopathies (MG) are characterized by the proliferation of plasma cells that produce identical abnormal immunoglobulins (intact or some of their subunits). This abnormal immunoglobulin component is called monoclonal protein (M-protein), and is considered a biomarker of proliferative activity. The identification, characterization and measurement of M-protein is essential for the management of MG. We conducted a systematic review of the different tests and measurement methods used in the clinical laboratory for the study of M-protein in serum and urine, the biochemistry and hematology tests necessary for clinical evaluation, and studies in bone marrow, peripheral blood and other tissues. This review included literature published between 2009 and 2022. The paper discusses the main methodological characteristics and limitations, as well as the purpose and clinical value of the different tests used in the diagnosis, prognosis, monitoring and assessment of treatment response in MG. Included are methods for the study of M-protein, namely electrophoresis, measurement of immunoglobulin levels, serum free light chains, immunoglobulin heavy chain/light chain pairs, and mass spectrometry, and for the bone marrow examination, morphological analysis, cytogenetics, molecular techniques, and multiparameter flow cytometry.

Development and validation of a deep learning-based protein electrophoresis classification algorithm

Background

Protein electrophoresis (PEP) is an important tool in supporting the analytical characterization of protein status in diseases related to monoclonal components, inflammation, and antibody deficiency. Here, we developed a deep learning-based PEP classification algorithm to supplement the labor-intensive PEP interpretation and enhance inter-observer reliability.

Methods

A total of 2,578 gel images and densitogram PEP images from January 2018 to July 2019 were split into training (80%), validation (10%), and test (10.0%) sets. The PEP images were assessed based on six major findings (acute-phase protein, monoclonal gammopathy, polyclonal gammopathy, hypoproteinemia, nephrotic syndrome, and normal). The images underwent processing, including color-to-grayscale and histogram equalization, and were input into neural networks.

Results

Using densitogram PEP images, the area under the receiver operating characteristic curve (AUROC) for each diagnosis ranged from 0.873 to 0.989, and the accuracy for classifying all the findings ranged from 85.2% to 96.9%. For gel images, the AUROC ranged from 0.763 to 0.965, and the accuracy ranged from 82.0% to 94.5%.

Conclusions

The deep learning algorithm demonstrated good performance in classifying PEP images. It is expected to be useful as an auxiliary tool for screening the results and helpful in environments where specialists are scarce.

To skim or splice? Comparing the quantification of M-proteins using two peak-integration protocols across multiple electrophoresis platforms

OBJECTIVES

M-protein quantification by peak integration in serum protein electrophoresis (SPE) plays a central role in diagnosing, prognosing and monitoring monoclonal gammopathies. The conventional perpendicular drop (PD) integration approach integrates M-spikes from the baseline, which performs acceptably when the M-protein concentration is relatively high compared to the amount of background proteins present. The alternative peak-integration protocol by tangential skim (TS), however, allows for more accurate M-protein estimations by excluding background proteins. Despite some guideline recommendations, TS has been poorly adopted, making an understanding of the differences between the two protocols and their potential impacts paramount when considering a change from PD to TS.

DESIGN & METHODS

We conducted retrospective investigations of the differences in M-protein quantification over large concentration ranges between PD and TS on 3 of the most popular electrophoresis platforms.

RESULTS

Compared to PD, TS gave consistently lower results; the differences between the two methods increased tremendously and became more sporadic as M-protein concentrations dropped below 15 g/L in all 3 platforms. At < 15 g/L, the average % difference ranged from -81 % to -95 %, while above 15 g/L, the average % difference was only -13 to -31 %. Medical decision point analyses using linear regression predicted statistically significant and platform-dependent differences, which could impact clinical interpretation.

CONCLUSIONS

Careful consideration of the magnitude of concentration changes and the potential impacts on patient classification and management should be made when switching to TS for M-protein quantification.