Classifying flow cytometry data using Bayesian analysis helps to distinguish ALS patients from healthy controls

Introduction

Given its wide availability and cost-effectiveness, multidimensional flow cytometry (mFC) became a core method in the field of immunology allowing for the analysis of a broad range of individual cells providing insights into cell subset composition, cellular behavior, and cell-to-cell interactions. Formerly, the analysis of mFC data solely relied on manual gating strategies. With the advent of novel computational approaches, (semi-)automated gating strategies and analysis tools complemented manual approaches.

Methods

Using Bayesian network analysis, we developed a mathematical model for the dependencies of different obtained mFC markers. The algorithm creates a Bayesian network that is a HC tree when including raw, ungated mFC data of a randomly selected healthy control cohort (HC). The HC tree is used to classify whether the observed marker distribution (either patients with amyotrophic lateral sclerosis (ALS) or HC) is predicted. The relative number of cells where the probability q is equal to zero is calculated reflecting the similarity in the marker distribution between a randomly chosen mFC file (ALS or HC) and the HC tree.

Results

Including peripheral blood mFC data from 68 ALS and 35 HC, the algorithm could correctly identify 64/68 ALS cases. Tuning of parameters revealed that the combination of 7 markers, 200 bins, and 20 patients achieved the highest AUC on a significance level of p < 0.0001. The markers CD4 and CD38 showed the highest zero probability. We successfully validated our approach by including a second, independent ALS and HC cohort (55 ALS and 30 HC). In this case, all ALS were correctly identified and side scatter and CD20 yielded the highest zero probability. Finally, both datasets were analyzed by the commercially available algorithm 'Citrus', which indicated superior ability of Bayesian network analysis when including raw, ungated mFC data.

Discussion

Bayesian network analysis might present a novel approach for classifying mFC data, which does not rely on reduction techniques, thus, allowing to retain information on the entire dataset. Future studies will have to assess the performance when discriminating clinically relevant differential diagnoses to evaluate the complementary diagnostic benefit of Bayesian network analysis to the clinical routine workup.

Event-Driven Immunoprofiling Predicts Return of Disease Activity in Alemtuzumab-Treated Multiple Sclerosis

Background: Alemtuzumab is a highly effective drug for the treatment of multiple sclerosis (MS), characterized by specific patterns of depletion and repopulation. As an induction-like treatment concept, two mandatory infusion courses can inhibit long-term disease activity in the majority of patients, and additional courses can successfully manage subsequent re-emergence of disease activity. Currently, there are no biomarkers to identify patients with re-emergent disease activity requiring retreatment.

Methods: In this study, we systematically characterized 16 MS patients commencing alemtuzumab. Clinical parameters, MRI and detailed immunoprofiling were conducted every 3 months for up to 84 months.

Results: Alemtuzumab led to significant decrease in clinical disease activity in all evaluated patients. Nine out of 16 patients presented with no evidence of disease activity (NEDA)-3 up to 84 months (“complete-responder”), while 7 patients demonstrated clinical or/and subclinical MRI disease activity and received alemutzumab retreatment (“partial-responder”). In both response categories, all T- and B-cell subsets were markedly depleted after alemtuzumab therapy. In particular, absolute numbers of Th1 and Th17 cells were markedly decreased and remained stable below baseline levels—this effect was particularly pronounced in complete-responders. While mean cell numbers did not differ significantly between groups, analysis of event-driven immunoprofiling demonstrated that absolute numbers of Th1 and Th17 cells showed a reproducible increase starting 6 months before relapse activity. This change appears to predict emergent disease activity when compared with stable disease.

Conclusion: Studies with larger patient populations are needed to confirm that frequent immunoprofiling may assist in evaluating clinical decision-making of alemtuzumab retreatment.

New insights into the pharmacokinetics and pharmacodynamics of natalizumab treatment for patients with multiple sclerosis, obtained from clinical and in vitro studies

BACKGROUND

The monoclonal antibody natalizumab (NAT) inhibits the migration of lymphocytes throughout the blood-brain barrier by blocking very late antigen (VLA)-4 interactions, thereby reducing inflammatory central nervous system (CNS) activity in patients with multiple sclerosis (MS). We evaluated the effects of different NAT treatment regimens.

METHODS

We developed and optimised a NAT assay to measure free NAT, cell-bound NAT and VLA-4 expression levels in blood and cerebrospinal fluid (CSF) of patients using standard and prolonged treatment intervals and after the cessation of therapy.

RESULTS

In paired CSF and blood samples of NAT-treated MS patients, NAT concentrations in CSF were approximately 100-fold lower than those in serum. Cell-bound NAT and mean VLA-4 expression levels in CSF were comparable with those in blood. After the cessation of therapy, the kinetics of free NAT, cell-bound NAT and VLA-4 expression levels differed. Prolonged intervals greater than 4 weeks between infusions caused a gradual reduction of free and cell-bound NAT concentrations. Sera from patients with and without NAT-neutralising antibodies could be identified in a blinded assessment. The NAT-neutralising antibodies removed NAT from the cell surface in vivo and in vitro. Intercellular NAT exchange was detected in vitro.

CONCLUSIONS

Incorporating assays to measure free and cell-bound NAT into clinical practice can help to determine the optimal individual NAT dosing regimen for patients with MS.