Thomas Dejoie

Monitoring multiple myeloma patients treated with daratumumab: teasing out monoclonal antibody interference

Abstract Background: Monoclonal antibodies are promising anti-myeloma treatments. As immunoglobulins, monoclonal antibodies have the potential to be identified by serum protein electrophoresis (SPE) and immunofixation electrophoresis (IFE). Therapeutic antibody interference with standard clinical SPE and IFE can confound the use of these tests for response assessment in clinical trials and disease monitoring. Methods: To discriminate between endogenous myeloma protein and daratumumab, a daratumumab-specific immunofixation electrophoresis reflex assay (DIRA) was developed using a mouse anti-daratumumab antibody. To evaluate whether anti-daratumumab bound to and shifted the migration pattern of daratumumab, it was spiked into daratumumab-containing serum and resolved by IFE/SPE. The presence (DIRA positive) or absence (DIRA negative) of residual M-protein in daratumumab-treated patient samples was evaluated using predetermined assessment criteria. DIRA was evaluated for specificity, limit of sensitivity, and reproducibility. Results: In all of the tested samples, DIRA distinguished between daratumumab and residual M-protein in commercial serum samples spiked with daratumumab and in daratumumab-treated patient samples. The DIRA limit of sensitivity was 0.2 g/L daratumumab, using spiking experiments. Results from DIRA were reproducible over multiple days, operators, and assays. The anti-daratumumab antibody was highly specific for daratumumab and did not shift endogenous M-protein. Conclusions: As the treatment of myeloma evolves to incorporate novel monoclonal antibodies, additional solutions will be needed for clinical monitoring of patient responses to therapeutic regimens. In the interim, assays such as DIRA can inform clinical outcomes by distinguishing daratumumab from endogenous M-protein by IFE.

Long-term outcome of monoclonal (type 1) cryoglobulinemia.

The aim of this study is to investigate long‐term outcome of symptomatic type 1 cryoglobulinemia (CG) and its determinants. Retrospective cohort study was conducted in two French University Hospitals. Patients with type 1 CG were identified using laboratory databases. Inclusion criterion was the presence of persistent symptoms of CG. Among 227 screened patients, 36 were included. Skin or vasomotor symptoms were the most frequent features (75%). Nephropathy and neuropathy occurred in 30% and 47% of cases, respectively. The underlying B cell disease (BCD) was a nonmalignant monoclonal gammopathy (NMMG) in 13 (36%) and a hematologic malignancy (HM) in 23 (64%; Waldenstrom macroglobulinemia (WM) in 12, low‐grade non‐Hodgkin lymphoma (NHL) in 6, multiple myeloma (MM) in 4, and chronic lymphocytic leukemia in 1 patient. Severe manifestations affected half the patients and were more frequent with IgG (82 vs. 30% (P = 0.006)). At last follow‐up, 64% of patients had suffered no hematologic manifestation. Potent chemotherapeutic regimens were mainly used in HM. For patients with NMMG, WM, or NHL, fludarabine or rituximab‐based regimens appeared to yield better responses. Five‐year survival rate was 82%. In multivariate analysis, mortality was significantly higher in older patients (HR: 1.17 per year [95% CI: 1.06–1.28], P = 0.001) and those with nephropathy (HR: 8.9 [95% CI: 1.9–43], P = 0.006). Kidney disease, infections, Richters transformation, and second malignancies were important sources of morbi‐mortality. Despite its limitations, this series provide novel information regarding type 1 CG. Further studies are needed to improve its management. To date, therapeutic strategy should be tailored according to patients characteristics (age, comorbidities, underlying BCD), and therapeutic target.Am. J. Hematol. 89:156–161, 2014.

Recognition and management of common, rare, and novel serum protein electrophoresis and immunofixation interferences

Protein electrophoresis and immunofixation are subject to a variety of analytical interferences that may affect monoclonal protein diagnostics performed in the context of monoclonal gammopathies. Interferences include endogenous substances, such as hemoglobin and fibrinogen, and exogenous compounds, such as radiocontrast dyes, antibiotics, and monoclonal antibody therapies. General approaches to managing interferences begin with recognition of the problem. Provided herein are examples of common, rare, and novel interferences with the goal of providing a comprehensive overview. With each example, specific methods and strategies are provided to manage analytical interferences to ensure that interpretative reports are accurate. Longstanding and newer technologies are also described to contextualize where interferences may be identified and avoided.

Analysis of a Compartmental Model of Endogenous Immunoglobulin G Metabolism with Application to Multiple Myeloma

Immunoglobulin G (IgG) metabolism has received much attention in the literature for two reasons: (i) IgG homeostasis is regulated by the neonatal Fc receptor (FcRn), by a pH-dependent and saturable recycling process, which presents an interesting biological system; (ii) the IgG-FcRn interaction may be exploitable as a means for extending the plasma half-life of therapeutic monoclonal antibodies, which are primarily IgG-based. A less-studied problem is the importance of endogenous IgG metabolism in IgG multiple myeloma. In multiple myeloma, quantification of serum monoclonal immunoglobulin plays an important role in diagnosis, monitoring and response assessment. In order to investigate the dynamics of IgG in this setting, a mathematical model characterizing the metabolism of endogenous IgG in humans is required. A number of authors have proposed a two-compartment nonlinear model of IgG metabolism in which saturable recycling is described using Michaelis–Menten kinetics; however it may be difficult to estimate the model parameters from the limited experimental data that are available. The purpose of this study is to analyse the model alongside the available data from experiments in humans and estimate the model parameters. In order to achieve this aim we linearize the model and use several methods of model and parameter validation: stability analysis, structural identifiability analysis, and sensitivity analysis based on traditional sensitivity functions and generalized sensitivity functions. We find that all model parameters are identifiable, structurally and taking into account parameter correlations, when several types of model output are used for parameter estimation. Based on these analyses we estimate parameter values from the limited available data and compare them with previously published parameter values. Finally we show how the model can be applied in future studies of treatment effectiveness in IgG multiple myeloma with simulations of serum monoclonal IgG responses during treatment.