Pre-analytical stability of serum biomarkers for neurological disease: Neurofilament-light, glial fibrillary acidic protein and contactin-1

Objectives: Neurofilament-light (NfL), glial fibrillary acidic protein (GFAP) and contactin-1 (CNTN1) are blood-based biomarkers that could contribute to monitoring and prediction of disease and treatment outcomes in neurological diseases. Pre-analytical sample handling might affect results, which could be disease-dependent. We tested common handling variations in serum of volunteers as well as in a defined group of patients with multiple sclerosis (pwMS). Methods: Sample sets from 5 pwMS and 5 volunteers at the outpatient clinic were collected per experiment. We investigated the effect of the following variables: collection tube type, delayed centrifugation, centrifugation temperature, delayed storage after centrifugation and freeze-thawing. NfL and GFAP were measured by Simoa and CNTN1 by Luminex. A median recovery of 90-110% was considered stable. Results: For most pre-analytical variables, serum NfL and CNTN1 levels remained unaffected. In the total group, NfL levels increased (121%) after 6 h of delay at 2-8 °C until centrifugation, while no significant changes were observed after 24 h delay at room temperature (RT). In pwMS specifically, CNTN1 levels increased from additional freeze-thaw cycles number 2 to 4 (111%-141%), whereas volunteer levels remained stable. GFAP showed good stability for all pre-analytical variables. Conclusions: Overall, the serum biomarkers tested were relatively unaffected by variations in sample handling. For serum NfL, we recommend storage at RT before centrifugation at 2-8 °C up to 6 h or at RT up to 24 h. For serum CNTN1, we advise a maximum of two freeze-thaw cycles. Our results confirm and expand on recently launched consensus standardized operating procedures

Serum Neurofilament Light Association With Progression in Natalizumab-Treated Patients With Relapsing-Remitting Multiple Sclerosis

Background and objectives: To investigate the potential of serum neurofilament light (NfL) to reflect or predict progression mostly independent of acute inflammatory disease activity in patients with relapsing-remitting multiple sclerosis (RRMS) treated with natalizumab. Methods: Patients were selected from a prospective observational cohort study initiated in 2006 at the VU University Medical Center Amsterdam, the Netherlands, including patients with RRMS treated with natalizumab. Selection criteria included an age of 18 years or older and a minimum follow-up of 3 years from natalizumab initiation. Clinical and MRI assessments were performed on a yearly basis, and serum NfL was measured at 5 time points during the follow-up, including on the day of natalizumab initiation (baseline), 3 months, 1 year, and 2 years after natalizumab initiation, and on last follow-up visit. Using general linear regression models, we compared the longitudinal dynamics of NfL between patients with and without confirmed Expanded Disability Status Scale (EDSS) progression between year 1 visit and last follow-up, and between individuals with and without EDSS+progression, a composite endpoint including the EDSS, 9-hole peg test, and timed 25-foot walk. Results: Eighty-nine natalizumab-treated patients with RRMS were included. Median follow-up time was 5.2 years (interquartile range [IQR] 4.3-6.7, range 3.0-11.0) after natalizumab initiation, mean age at time of natalizumab initiation was 36.9 years (SD 8.5), and median disease duration was 7.4 years (IQR 3.8-12.1). Between year 1 and the last follow-up, 28/89 (31.5%) individuals showed confirmed EDSS progression. Data for the EDSS+endpoint was available for 73 out of the 89 patients and 35/73 (47.9%) showed confirmed EDSS+progression. We observed a significant reduction in NfL levels 3 months after natalizumab initiation, which reached its nadir of close to 50% of baseline levels 1 year after treatment initiation. We found no difference in the longitudinal dynamics of NfL in progressors vs nonprogressors. NfL levels at baseline and 1 year after natalizumab initiation did not predict progression at last follow-up. Conclusion: In our cohort of natalizumab-treated patients with RRMS, NfL fails to capture or predict progression that occurs largely independently of clinical or radiologic signs of acute focal inflammatory disease activity. Additional biomarkers may thus be needed to monitor progression in these patients. Classification of evidence: This study provides Class II evidence that serum NfL levels are not associated with disease progression in natalizumab-treated patients with RRMS.</p

Extended interval dosing of ocrelizumab modifies the repopulation of B cells without altering the clinical efficacy in multiple sclerosis

Abstract Background Recent studies suggest that extended interval dosing of ocrelizumab, an anti-B cell therapy, does not affect its clinical effectiveness in most patients with multiple sclerosis (MS). However, it remains to be established whether certain B cell subsets are differentially repopulated after different dosing intervals and whether these subsets relate to clinical efficacy. Methods We performed high-dimensional single-cell characterization of the peripheral immune landscape of patients with MS after standard (SID; n = 43) or extended interval dosing (EID; n = 37) of ocrelizumab and in non-ocrelizumab-treated (control group, CG; n = 28) patients with MS, using mass cytometry by time of flight (CyTOF). Results The first B cells that repopulate after both ocrelizumab dosing schemes were immature, transitional and regulatory CD1d+ CD5+ B cells. In addition, we observed a higher percentage of transitional, naïve and regulatory B cells after EID in comparison with SID, but not of memory B cells or plasmablasts. The majority of repopulated B cell subsets showed an increased migratory phenotype, characterized by higher expression of CD49d, CD11a, CD54 and CD162. Interestingly, after EID, repopulated B cells expressed increased CD20 levels compared to B cells in CG and after SID, which was associated with a delayed repopulation of B cells after a subsequent ocrelizumab infusion. Finally, the number of/changes in B cell subsets after both dosing schemes did not correlate with any relapses nor progression of the disease. Conclusions Taken together, our data highlight that extending the dosing interval of ocrelizumab does not lead to increased repopulation of effector B cells. We show that the increase of CD20 expression on B cell subsets in EID might lead to longer depletion or less repopulation of B cells after the next infusion of ocrelizumab. Lastly, even though extending the ocrelizumab interval dosing alters B cell repopulation, it does not affect the clinical efficacy of ocrelizumab in our cohort of patients with MS

Personalized B-cell tailored dosing of ocrelizumab in patients with multiple sclerosis during the COVID-19 pandemic

In this observational study, 159 patients with multiple sclerosis received personalized dosing of ocrelizumab incentivized by the COVID-19 pandemic. Re-dosing was scheduled when CD19 B-cell count was ⩾10 cells/µL (starting 24 weeks after the previous dose, repeated 4-weekly). Median interval until re-dosing or last B-cell count was 34 [30–38] weeks. No clinical relapses were reported and a minority of patients showed Expanded Disability Status Scale (EDSS) progression. Monthly serum neurofilament light levels remained stable during extended intervals. Two (1.9%) of 107 patients with a follow-up magnetic resonance imaging (MRI) scan showed radiological disease activity. Personalized dosing of ocrelizumab could significantly extend intervals with low short-term disease activity incidence, encouraging future research on long-term safety and efficacy

Additional file 2 of Extended interval dosing of ocrelizumab modifies the repopulation of B cells without altering the clinical efficacy in multiple sclerosis

Additional file 2: Table S1. Median expression of markers. Table S2. Mean and standard deviation of the percentage of each immune cell subset. Table S3. Mean and standard deviation of the percentage of each B cell subset among all CD19+ cells. Table S4. Median expression markers of migratory markers. Table S5. Median expression markers of migratory markers. Table S6. Mean and standard deviation of the percentage of each B cell subset among all CD45+ cells with follow-up sample. Table S7. Statistics of Δ. Table S8. Mean and standard deviation of the percentage of each B cell subset among all CD45+ cells with follow-up sample in GC patients

Additional file 1 of Extended interval dosing of ocrelizumab modifies the repopulation of B cells without altering the clinical efficacy in multiple sclerosis

Additional file 1: Figure S1. Pre-gating strategy of the CyTOF. A. Representation of dotplots of the pre-gating strategy of the data obtained with the CyTOF. (1) Removal of cell debris, beads and doublets, (2) cleaning signal over time with flowCut, (3) debarcoding, (4) selection of CD45+ live cells and (5) batch normalization with CytoNorm. B. Dotplot represents the pre-gating of CD3+CD19−, CD3−CD19+ and CD3−CD19− cells for further analysis. Figure S2. B cell subclustering. A. Heatmap displays the median scaled intensities of all the markers across the annotated B cell subclusters. B. Bar plots of the percentage of each annotated B cell subpopulation out of the total CD19+ cells from CG, SID and EID patients. Each data point corresponds to each individual, columns and error bars show mean ± SEM. P-values indicate the statistical differences after a GLM model with age, sex and type of MS as covariates. *adjusted p-value < 0.05, **adjusted p-value < 0.01, ***adjusted p-value < 0.001, ****adjusted p-value < 0.0001. GLM = multivariate general linear model; CG = control group; SID = standard interval dosing; EID = extended interval dosing. Figure S3. T cell and rest of immune cell subclustering. A. Heatmap shows the median scaled intensities of all the markers across the annotated T cell subclusters. B. Heatmap represents the median scaled intensities of all the markers across the annotated CD3−CD19− cell subclusters. Th = T helper cells; Tregs = regulatory T cells; Temra = T effector memory re-expressing CD45RA cells; NKT = natural killer T cells; DN = double negative; DP = double positive cells; NK = natural killer cells. Figure S4. Longitudinal cohort after treatment with standard or extended interval dosing of ocrelizumab. A. Bar plots display the percentage of annotated B cell subsets out of the total CD45 + cells from patients that went from SID to SID, SID to EID, EID to SID and EID to EID. B. Violin plots display the Δ or subtraction of the percentage of annotated B cell subsets out of the total CD45+ cells at the second blood sampling minus the percentage of annotated B cell subsets out of the total CD45+ cells at the first blood sampling. P-values indicate the statistical differences after a GLM model of the change of percentages between groups of patients, with age, sex and type of MS as covariates. C. Schematic overview of the longitudinal study design and timeline for CG patients. D. Percentage of annotated B cell subsets out of the total amount of CD45+ cells from patients that went from CG to SID and CG to EID. A and D. Each data point corresponds to each individual, columns and error bars show mean ± SEM. P-values indicate the statistical differences after a GLMM model with age, sex and type of MS as covariates and patient ID as a random effect. *adjusted p-value < 0.05, **adjusted p-value < 0.01, ***adjusted p-value < 0.001, ****adjusted p-value < 0.0001; #unadjusted p-value < 0.05. OCR = ocrelizumab; GLM = multivariate general linear model; GLMM = multivariate general linear mixed model; CG = control group; SID = standard interval dosing; EID = extended interval dosing; T1 = first-time point/blood sampling; T2 = second-time point/blood sampling