Wearing-off at the end of natalizumab dosing intervals is associated with low receptor occupancy

Patients

We invited all patients older than 18 years with a diagnosis of RRMS who had received a minimum of 6 natalizumab infusions at the Department of Neurology, Haukeland University Hospital (n = 45) to participate in this cross-sectional study; 40 agreed to participate. At inclusion, we obtained baseline demographic and clinical patient characteristics from the patients' medical journal including age, sex, disease duration (years since first MS symptoms), natalizumab treatment duration (years since first natalizumab infusion), numbers of new MRI lesions and clinical relapses in the last year before inclusion, serum vitamin D level, Symbol Digit Modalities Test score,⁸ and Expanded Disability Status Scale score.⁹ Each patient filled in questionnaires on fatigue (Fatigue Severity Scale),¹⁰ and on working status, smoking habits, weight, height, and whether they had wearing-off symptoms (never, sometimes, and regularly), and, if applicable, type of symptoms.

Standard protocol approvals, registrations, and patient consents

The study was approved by the Regional Committee for Medical Research Ethics, Western Norway (REK 2016/579), and written informed consent was obtained from all participating patients.

Blood samples

At inclusion, we collected blood before and after natalizumab infusion. For mass cytometry analysis, whole blood was collected in heparinized Vacutainer tubes (Greiner Bio-One GmbH, Kremsmünster, Austria), incubated with Proteomic Stabilizer (Smart Tube, Inc, San Carlos, CA) for 10 minutes, and stored at −80°C. Whole blood was then thawed, and red blood cell lysis was performed with Thaw-lyse buffer I (Smart Tube, Inc) to obtain peripheral blood leukocytes (PBLs). For neurofilament measurement, whole blood was collected in Vacutainer tubes with no additives (BD, Plymouth, United Kingdom), incubated at room temperature for 60 minutes, and centrifuged at room temperature at 3,200g for 13 minutes before the serum was retrieved and stored at −80°C.

Neurofilament measurement

Serum samples were thawed, and the concentration of neurofilament light chain (NF-L) was measured with a single-molecule array (Simoa) assay (Quanterix, Billerica, MA) according to the manufacturer's protocol.

Mass cytometry RO assay

PBLs were stained with a 36-parameter mass cytometry antibody panel (table e-1, links.lww.com/NXI/A190). Bound natalizumab was detected with an anti-IgG4 antibody (conjugated to ¹⁶⁹Tm). Total α4 integrin was detected with an anti-CD49d antibody (conjugated to ¹⁴¹Pr) specific for a different epitope than natalizumab. The 36 metal-conjugated antibodies were purchased preconjugated (Fluidigm, South San Francisco, CA), or antibodies were purchased (BioLegend, San Diego, CA, R&D Systems, Minneapolis, MN, and Abcam, Cambridge, Great Britain) and conjugated in-house to metals with the Maxpar Antibody Labeling Kit (Fluidigm). Briefly, we thawed, barcoded (Cell-ID 20-Plex Pd Barcoding Kit, Fluidigm), and pooled PBL samples in batches of 20 randomly distributed samples, keeping paired samples from the same patients in the same batch. A control PBL sample from 1 healthy donor was included in each batch. Pooled PBLs were first incubated in Maxpar cell stain buffer with 100 U/mL heparin (LEO Pharma A/S) for 20 minutes at room temperature¹¹ and then incubated with the antibody cocktail for 30 minutes at room temperature. Stained PBLs were washed, fixed in fresh 2% paraformaldehyde (Thermo Scientific, Waltham, MA) in Maxpar PBS for 10 minutes at room temperature, and then incubated in 125 nM Cell-ID^TM Intercalator-Ir in Maxpar Fix and Perm Buffer (Fluidigm) at 4°C overnight. We performed all centrifugation steps at room temperature at 800g.

Before mass cytometry analysis, PBLs were resuspended in 0.1× EQ Four Element Calibration Beads (Fluidigm) in Maxpar cell acquisition solution (Fluidigm) and filtered (Corning Falcon Test Tube with Cell Strainer Snap Cap, Fisher Scientific, Hampton, NN). We used antibody binding QSC beads (Bangs Laboratories, Inc., catalog number 815A, Fishers, IN) to standardize signal intensities from anti-IgG4 (conjugated to ¹⁶⁹Tm) and anti-CD49d (conjugated to ¹⁴¹Pr) as previously described in detail.¹² We analyzed PBL and QSC beads with the same standard settings on a Helios® mass cytometer (Fluidigm) after tuning (CyTOF Tuning Solution, Fluidigm) and calibration (EQ Four Element Calibration Beads, Fluidigm) according to the manufacture's guidelines. Healthy control PBLs served as a negative control for anti-IgG4 in the absence of natalizumab, and patient PBLs incubated in vitro with natalizumab to an expected RO of 100% served as positive controls. Further quality control experiments were performed as previously described in detail.¹²

Data processing, analysis, and statistics

After acquisition of QSC beads, we normalized (Fluidigm normalizer) and exported FCS files to Cytobank software (Cytobank, Inc., Beckman Coulter, Brea, CA) and created QSC bead standard curves (QuickCal template, Bangs Laboratories). After acquisition of PBL samples, we normalized (Fluidigm normalizer), debarcoded (Fluidigm Debarcoder), and exported the FSC files to Cytobank software for gating and downstream analysis (figure e-1, links.lww.com/NXI/A190). We performed clean-up gating to obtain single PBLs, and the data were arcsinh transformed with a scale argument of 5.¹³ We then performed 2 independent analyses for RO calculation in single PBLs: one was a manual analysis, and one used an unsupervised approach.

In the manual approach, we first identified the following 11 leukocyte subtypes of interest by manual gating: CD8⁺ central memory (T_CM), effector memory (T_EM), effector memory RA (T_EMRA) T cells; CD4⁺ T_EM, T_CM, and T_EMRA cells; CD34⁺ cells; memory B cells; natural killer (NK) cells; monocytes; and conventional dendritic cells (cDCs). We then plotted the signal intensities (dual count 90th percentiles) of anti-IgG4 and anti-CD49d in each of these cell types with the QSC bead standard curves to obtain bead standardized values and calculated %RO by the following formula:

We compared ROs in the leukocyte subtypes in different patient groups using a Kruskal-Wallis test.

In the unsupervised approach, we used R (version 3.4.3) to add an extra variable into the FSC files: the ratio between signal intensities of anti-IgG4 and anti-CD49d in each cell. This resulted in an RO estimate for each cell. For visualization of high-dimensional single-cell data, we performed automated dimensionality reduction with stochastic neighborhood embedding (viSNE, Cytobank).¹⁴ We analyzed the new RO variable with the cluster identification, characterization, and regression tool CITRUS (Cytobank), an algorithm that automatically identifies statistically significant differences between patient groups.¹⁵ We applied the correlative model Significance Analysis of Microarrays with a false discovery rate (adjusted for multiple hypothesis testing) of 1%. CITRUS was run with 10 repetitions.

The relationship between baseline demographic variables and wearing-off was compared using a Kruskal-Wallis test. Statistical differences with p < 0.05 were considered significant using a 2-sided comparison. To test the age-corrected relationship between baseline variables and wearing-off, we used a likelihood ratio test between a linear model with only age and a linear model with age and the relevant baseline variable as predictors. We conducted a linear regression of the association between RO and body mass index (BMI) and used a t test to assess whether the slope was significantly different from zero. We used R version 3.4.3¹⁶ for statistical analysis and correlation plots.

Data availability

FCS files from anonymized patient PBL samples can be accessed in the Flow Repository (ID: FR-FCM-Z2A9).