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Mechanism of Ocrelizumab in B-Cell Depletion

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May 5, 2026

# How does ocrelizumab target CD20-positive B cells to drive B-cell depletion?

## Ocrelizumab binds to CD20 on B cells and drives their depletion primarily through antibody-dependent cellular cytotoxicity mediated by monocytes via Fcγ receptor pathways, achieving rapid and sustained reduction of circulating B cells while also depleting CD20-positive T-cell subsets.

# Abstract

Ocrelizumab is a humanized anti-CD20 monoclonal antibody that targets the same CD20 epitope as rituximab but achieves B-cell depletion primarily through antibody-dependent cellular cytotoxicity (ADCC) rather than complement-dependent cytotoxicity (CDC). The antibody was engineered with amino acid modifications to enhance binding to Fcγ receptor IIIa, resulting in two- to five-fold greater ADCC activity compared to rituximab. Monocytes serve as the dominant effector cells mediating B-cell depletion through FcγRI and FcγRIII-dependent pathways, achieving rapid onset within hours and near-complete depletion (>95%) of circulating B cells by week 2. However, depletion efficiency varies across anatomical compartments, with secondary lymphoid organs retaining resistant B-cell subpopulations and bone marrow showing the weakest reduction. Ocrelizumab also co-depletes CD20-positive T-cell subsets and novel dual-expressor lymphocytes expressing both T-cell and B-cell receptors.

B-cell recovery begins in bone marrow and spleen before appearing in blood, with median time to repletion of 72 weeks. The phenotype of reconstituting B cells varies by immunological context: in settings with ongoing antigen stimulation, recovery is characterized by expansion of differentiated, myelin-reactive B cells, whereas absence of stimulation favors naive B-cell reconstitution. Treatment induces secondary immunological changes including increased BAFF levels and decreased sTACI, which may enhance regulatory plasma cell development and contribute to therapeutic efficacy. The ADCC-predominant mechanism provides more sustained depletion than CDC-predominant antibodies while potentially offering better tolerability.

Methods

We analyzed 10 sources from an initial pool of 200, using 6 screening criteria. Each paper was reviewed for 6 key aspects that mattered most to the research question. More on methods

Records from Elicit search

n = 200

Papers screened using: Mechanistic Focus on CD20/B-cells, Specific Depletion Mechanisms, Study Type and Mechanistic Data, Mechanistic Content Present, CD20/B-cell Relevance, Study Rigor and Detail

n = 200

Papers screened out

n = 190

Papers included for extraction

n = 10

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## Paper search

We performed a semantic search across over 138 million academic papers from the Elicit search engine, which includes all of [Semantic Scholar](https://www.semanticscholar.org/) and [OpenAlex](https://openalex.org/).

We ran this query: “How does ocrelizumab target CD20-positive B cells to drive B-cell depletion?”

The search returned 200 total results from Elicit.

We retrieved 200 papers most relevant to the query for screening.

## Screening

We screened in sources based on their abstracts that met these criteria:

- **Mechanistic Focus on CD20/B-cells**: Does this study investigate ocrelizumab’s mechanism of action on CD20-positive B cells or B-cell depletion mechanisms induced by ocrelizumab?
- **Specific Depletion Mechanisms**: Does this study examine specific mechanisms of B-cell depletion including complement-dependent cytotoxicity (CDC), antibody-dependent cellular cytotoxicity (ADCC), apoptosis, CD20 binding affinity, epitope recognition, or receptor occupancy by ocrelizumab?
- **Study Type and Mechanistic Data**: Is this an in vitro, in vivo (animal model), human study, systematic review, or meta-analysis that provides mechanistic data about ocrelizumab?
- **Mechanistic Content Present**: Does this study provide mechanistic data beyond solely clinical efficacy or safety outcomes?
- **CD20/B-cell Relevance**: If this study investigates ocrelizumab effects on non-B cell populations, does it include reference to CD20 or B-cell depletion mechanisms?
- **Study Rigor and Detail**: Is this study something other than a case report, case series, conference abstract, or preliminary report lacking sufficient mechanistic investigation or methodological detail?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

## Data extraction

We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column.

- **CD20 Targeting**:

Extract detailed information about how ocrelizumab targets CD20-positive B cells, including:

- CD20 epitope specificity and binding characteristics
- Binding affinity (Kd values, if reported)
- Antibody structure features that affect CD20 binding (humanization level, glycosylation, Fc region modifications)
- Any comparison of binding characteristics to other anti-CD20 antibodies (rituximab, ofatumumab, ublituximab)
- Dose-response relationships for CD20 binding

- **Cytotoxic Mechanisms**:

Extract all mechanisms by which ocrelizumab induces B-cell death after CD20 binding, including:

- Complement-dependent cytotoxicity (CDC) activity and potency
- Antibody-dependent cellular cytotoxicity (ADCC) activity and potency
- Direct apoptosis induction
- Other cytotoxic pathways (if mentioned)
- Quantitative comparisons of mechanism potency (e.g., EC50 values, percentage lysis)
- Time course of cytotoxicity for each mechanism
- Any genetic or phenotypic factors that affect cytotoxic efficacy

- **Depletion Patterns**:

Extract detailed patterns of B-cell depletion caused by ocrelizumab, including:

- Specific B-cell subpopulations targeted (naive, memory, plasma cells, transitional B cells)
- B-cell populations that are resistant to depletion
- Anatomical compartments affected (blood, bone marrow, spleen, lymph nodes, CNS/brain)
- Degree of depletion in each compartment (percentage reduction, absolute counts)
- Any co-depletion of CD20+ T cells or other immune cells
- Factors that influence depletion efficiency (dose, patient characteristics, genetic polymorphisms)

- **Depletion Kinetics**:

Extract timing and kinetics of B-cell depletion and recovery with ocrelizumab, including:

- Time to initial B-cell depletion (onset)
- Duration of maximal depletion
- Recovery timeline for different B-cell populations
- Recovery timeline for different anatomical compartments
- Sequence of B-cell reconstitution (which populations recover first)
- Phenotype of reconstituting B-cells (naive vs memory characteristics)
- Any differences in recovery kinetics compared to other anti-CD20 antibodies

- **Molecular Determinants**:

Extract molecular and structural features of ocrelizumab that influence its mechanism of action, including:

- Antibody type (humanized, chimeric, fully human)
- Heavy and light chain sequences (if provided)
- Fc region modifications or glycoengineering
- Pharmacokinetic properties affecting mechanism (half-life, tissue distribution)
- Dosing regimen and how it relates to mechanism optimization
- Route of administration (IV vs subcutaneous) and mechanistic implications
- Any engineering specifically designed to enhance B-cell depletion

- **Comparative Mechanisms**:

Extract direct comparisons of ocrelizumab’s mechanism to other anti-CD20 antibodies, including:

- Head-to-head mechanistic comparisons with rituximab, ofatumumab, ublituximab, or other anti-CD20 agents
- Relative potency differences in CDC vs ADCC mechanisms
- Differences in B-cell depletion patterns between antibodies
- Differences in depletion kinetics between antibodies
- Unique mechanistic features of ocrelizumab vs competitors
- Clinical implications of mechanistic differences (efficacy, safety, dosing requirements)

# Results

## Characteristics of Included Studies

Study

Full text retrieved?

Study focus

Key features

J. Uchida et al., 2004

Yes

Mouse model of anti-CD20 therapy

Developed mouse anti-mouse CD20 antibody panel to study B-cell depletion mechanisms

B. Cree et al., 2025

Yes

Review of anti-CD20 antibody evolution

Comprehensive comparison of ocrelizumab, ofatumumab, ublituximab, and rituximab

Pacheco-Fernández et al., 2018

No

In vitro CDC comparison

Compared ofatumumab and ocrelizumab CDC activity using B-cell lines

Samantha Ho et al., 2023

Yes

B-cell regulating factors

Examined effects on BAFF-APRIL system over 2.5 years

Schneider-Hohendorf et al., 2025

No

B- and T-cell receptor repertoires

Analyzed adaptive immune changes in 35 MS patients on ocrelizumab

Alice Willison et al., 2025

Yes

Immune cell dynamics comparison

Compared ocrelizumab (n=34) and ofatumumab (n=25) with flow cytometry

A. Bar-Or et al., 2021

Yes

Clinical perspectives review

Detailed molecular and pharmacological attributes of four anti-CD20 antibodies

Darius Häusler et al., 2018

Yes

EAE model of B-cell recovery

Characterized B-cell depletion and reconstitution in murine MS model

S. A. Kornilov et al., 2024

No

Multi-omic characterization

Analyzed plasma proteome, metabolome, and lipidome changes in 14 RRMS patients

Prajita Paul et al., 2025

No

Dual-expressor lymphocytes

Identified novel TCR/IgM dual-expressor cells susceptible to anti-CD20 therapy

toof

Pageof

The studies employed diverse methodological approaches, including murine models, in vitro cell line assays, human longitudinal cohort studies, and comprehensive reviews. Full-text availability varied, with six studies providing complete manuscripts and four available only as abstracts. Study populations ranged from mouse models to human RRMS cohorts, with sample sizes for human studies ranging from 14 to 35 patients.

## Mechanisms of CD20 Targeting and B-cell Depletion

### CD20 Binding Characteristics

Ocrelizumab is a humanized anti-CD20 monoclonal antibody that binds to the same epitope on the large extracellular loop of CD20 as rituximab. The antibody is classified as a type I anti-CD20 agent capable of crosslinking CD20 tetramers. Enhanced binding to low-affinity variants of Fcγ receptor IIIa distinguishes ocrelizumab from rituximab, contributing to its enhanced effector function. The antibody was specifically engineered through amino acid modifications in its Fc region to increase antibody-dependent cellular cytotoxicity (ADCC) activity.

### Cytotoxic Mechanisms

Ocrelizumab depletes B cells through multiple cytotoxic pathways, with ADCC as the primary mechanism. The antibody exhibits two- to five-fold greater ADCC activity compared to rituximab, while demonstrating three- to five-fold lower complement-dependent cytotoxicity (CDC) activity. Both ocrelizumab and ofatumumab can induce ADCC and CDC, though ofatumumab demonstrates stronger CDC effects, particularly at low CD20 expression levels. In vitro comparisons using human B-cell lines revealed that ocrelizumab induced less cell lysis through CDC compared to ofatumumab, with the strongest differentiation observed when complement was added 8 hours after antibody washing, showing the hierarchy: ofatumumab > rituximab > ocrelizumab.

The dominance of ADCC over CDC in ocrelizumab’s mechanism of action has important implications. Studies in mouse models demonstrated that B-cell depletion is completely dependent on effector cell Fc receptor expression, with monocytes serving as the dominant effector cells. The depletion pathway utilizes both FcγRI and FcγRIII-dependent mechanisms, while genetic deficiency in complement components (C3, C4, or C1q) did not prevent B-cell elimination, confirming the primacy of ADCC over CDC in vivo.

## Patterns of B-cell Depletion

### B-cell Subpopulations

Ocrelizumab targets CD20-positive B cells, resulting in significant reductions across multiple B-cell subsets including naive B cells, memory B cells, transitional B cells, and plasmablasts. Plasma cells, which lack CD20 expression, remain resistant to depletion, as do CD20-negative B-cell precursors and hematopoietic stem cells. An expansion of CD5+CD19+CD20− B cells was observed following treatment, indicating a population resistant to depletion. Circulating B-cell counts showed almost complete depletion by week 2, remaining depleted through week 96 with infusions every 24 weeks.

### Non-B-cell Targets

Beyond conventional B cells, ocrelizumab co-depletes CD20-positive T-cell subsets. Significant reductions occurred in double-negative (CD3+CD4−CD8−) T cells, which express higher CD20 levels compared to CD4 or CD8 positive T cells. Depletion was evident in highly expanded T-cell receptor beta chain and Vδ2+ T-cell receptor delta chain clonotypes. The treatment also targets novel dual-expressor cells (DEs) that co-express T-cell receptors and surface IgM, with up to 95% of DEs in cerebrospinal fluid expressing CD20. These DEs were found at significantly higher frequencies in RRMS patients compared to healthy controls and showed robust responses to myelin autoantigens.

### Anatomical Compartments

B-cell depletion occurs across multiple anatomical compartments but with varying efficiency. In blood, depletion is nearly complete, with more than 95% reduction in circulating B cells. However, B cells in lymphatic organs and the CNS are not depleted to the same extent as those in blood. Secondary lymphoid organs including the spleen and lymph nodes retain a subpopulation of CD20-positive B cells resistant to depletion, with splenic B cells showing more than 93% reduction. The bone marrow demonstrates the weakest reduction in B-cell numbers, with persistence of CD20-negative B-cell precursors and plasma cells. CSF B cells are substantially reduced following treatment, with tissue-resident memory cells in cerebrospinal fluid showing initial therapy resistance.

## Kinetics of Depletion and Recovery

### Depletion Timeline

The onset of B-cell depletion is rapid, occurring within 1 hour in mouse models, with CD19+ cell counts in humans almost completely depleted by week 2 after the first dose. Maximal depletion persists for extended periods, with B cells remaining extensively depleted through week 96 when infusions are administered every 24 weeks. In longitudinal human studies, persistent depletion of B cells and CD20+ T cells was observed for up to 2.5 years. Depletion at 6 months was associated with reduction in B-cell associated proteins.

### Recovery Patterns

B-cell recovery demonstrates complex kinetics varying by anatomical compartment and B-cell phenotype. In mouse models, bone marrow and spleen show simultaneous recovery, with B cells reappearing at 6 weeks and fully restored by 9 weeks, substantially preceding reappearance in blood. Lymph nodes and blood recover more slowly, with full repletion not achieved until 12 weeks. In humans, B-cell levels increase to baseline or the lower limit of normal in 90% of patients within 2.5 years after the last infusion, with a median time to repletion of 72 weeks.

The phenotype of reconstituting B cells depends on disease context. In experimental autoimmune encephalomyelitis (EAE) models involving activated B cells (MOG protein 1-117), recovery was characterized by expansion of mature, differentiated cells containing high frequencies of myelin-reactive B cells with restricted B-cell receptor gene diversity. These cells served as efficient antigen-presenting cells for myelin-specific T-cell activation. In contrast, in purely T-cell-mediated EAE models (MOG peptide 35-55), reconstituting B cells exhibited a naive phenotype without efficient antigen-presenting capacity. Human studies showed a bimodal distribution six months after initial depletion: some patients had few B cells with high somatic hypermutation consistent with incomplete depletion of differentiated B cells, while others had higher numbers of less differentiated B cells indicating reconstitution from germline. Extended interval dosing was associated with higher percentages of naive, transitional, and regulatory B cells, suggesting these populations recover first.

Fast B-cell repopulation starting around 6 months was associated with higher MRI activity and worsening disability, indicating clinical relevance of recovery kinetics. Studies showed that 26% of patients had B-cell repletion at 6 months.

## Comparative Analysis with Other Anti-CD20 Antibodies

### Mechanistic Distinctions

Ocrelizumab exhibits distinct mechanistic properties compared to other anti-CD20 antibodies. While all anti-CD20 agents induce both ADCC and CDC, their relative reliance on these pathways differs substantially. Ocrelizumab and ublituximab primarily use ADCC for B-cell depletion, whereas rituximab and ofatumumab rely more heavily on CDC. Ofatumumab demonstrates greater CDC potency compared to ocrelizumab, which may explain its efficacy at lower doses. Ublituximab shows more pronounced ADCC effects than rituximab, ocrelizumab, and ofatumumab due to its glycoengineered Fc segment.

The enhanced ADCC activity of ocrelizumab, achieved through amino acid engineering to increase binding to Fcγ receptor IIIa, is two- to five-fold greater than rituximab, while its CDC activity is three- to five-fold lower. This mechanistic profile has important clinical implications, as ADCC-mediated depletion is associated with fewer infusion-related reactions compared to CDC-mediated pathways.

### Depletion Efficiency and Clinical Impact

Ocrelizumab achieves more complete and sustained B-cell depletion compared to rituximab. The extent of B-cell depletion may affect clinical efficacy, with higher median B-cell levels potentially associated with higher rates of disability progression. Ocrelizumab shows slower B-cell repletion rates compared to ofatumumab, which may contribute to differences in dosing requirements. The antibody is administered intravenously at 300 mg initially, followed by 300 mg two weeks later, then 600 mg every 6 months, compared to ofatumumab’s low-dose subcutaneous regimen enabled by its stronger CDC activity.

## Secondary Effects on Immune Regulation

### BAFF-APRIL System Modulation

Ocrelizumab treatment induces profound changes in the B-cell activating factor (BAFF) and a proliferation-inducing ligand (APRIL) system. The treatment persistently enhances BAFF levels while reducing the endogenous soluble receptor and decoy sTACI in both serum and CSF. Levels of sTACI negatively correlate with BAFF levels, with reduction of sTACI associated with formation of sTACI-BAFF complexes. The pronounced increase in circulating BAFF following CD20 depletion represents a tentative compensatory mechanism that may contribute to reconstitution of targeted B cells.

Since sTACI is a decoy for APRIL, its reduction may enhance local APRIL activity, thereby promoting regulatory IgA+ plasma cells and astrocytic IL-10 production. This mechanism may contribute to the beneficial effects of anti-CD20 therapy, as exogenous sTACI (atacicept) worsened MS. The therapeutic effects of ocrelizumab extend beyond CD20-mediated B-cell lysis to implicate metabolic reprogramming.

### Broader Immunological Changes

B-cell depletion is accompanied by decreases in B-cell receptor and cytokine signaling pathways. At 6 months, treatment reduces plasma abundance of cytokines and cytotoxic proteins, markers of neuronal damage, and biologically active lipids including ceramides and lysophospholipids. This is followed by upregulation of numerous signaling and metabolic pathways at 12 months.

Ocrelizumab treatment also elevates exhaustion markers (CTLA-4, PD-1, TIGIT, TIM-3) across T, B, NK, and NKT cells. Regulatory T-cell numbers increase, especially in ocrelizumab-treated patients. Digital cytometry identified a putative increase in myeloid cells and a pro-inflammatory subset of T cells. These immune profile changes correlate with clinical parameters, suggesting pathophysiological relevance in RRMS.

## Synthesis

The mechanism by which ocrelizumab drives B-cell depletion involves multiple coordinated processes that vary by context, anatomical location, and B-cell phenotype. The apparent heterogeneity in depletion efficiency and recovery patterns across different compartments and studies can be reconciled through several key considerations.

### Mechanistic Heterogeneity by Anatomical Context

The differential depletion efficiency across anatomical compartments reflects distinct microenvironmental factors rather than conflicting findings. Blood shows near-complete depletion (>95%), whereas secondary lymphoid organs retain resistant subpopulations and bone marrow shows the weakest reduction. This pattern is mechanistically consistent with the ADCC-dependent mechanism of ocrelizumab. Blood provides optimal access for effector monocytes, while follicular B cells in lymphoid tissues may be protected by anatomical barriers or local factors that limit monocyte access. The persistence of plasma cells across all compartments reflects their lack of CD20 expression rather than resistance to the antibody’s cytotoxic mechanism.

### Recovery Phenotype Determined by Disease Activity

The divergent phenotypes of reconstituting B cells—ranging from highly mutated memory-like cells to naive populations—reflect the immunological context at recovery rather than inconsistent biology. In models where B cells are actively engaged in antigen presentation (MOG protein 1-117 EAE), recovery populations are enriched for mature, myelin-reactive cells with efficient APC capacity. In purely T-cell-mediated models (MOG peptide 35-55 EAE), naive phenotypes predominate. This suggests that ongoing antigenic stimulation during the recovery phase selectively expands differentiated B-cell clones that survive in lymphoid tissues, while absence of such stimulation favors reconstitution from naive precursors.

The bimodal distribution in human patients at 6 months—with some showing few highly mutated cells and others showing numerous less differentiated cells—likely represents these two extremes occurring within a single patient population. Patients with incomplete depletion of tissue-resident differentiated B cells will show the first pattern, while those with more complete depletion will demonstrate the second. The association of fast repopulation with worse clinical outcomes supports the pathogenic relevance of early-recovering differentiated populations.

### ADCC vs CDC Balance and Clinical Optimization

The comparative studies showing stronger CDC activity for ofatumumab versus ocrelizumab do not indicate superior efficacy, but rather different optimization strategies. Ofatumumab’s potent CDC enables low-dose subcutaneous administration, while ocrelizumab’s enhanced ADCC (2-5 fold greater than rituximab) allows for less frequent dosing and potentially better tolerability, as ADCC generates fewer infusion-related reactions than CDC. Both mechanisms achieve effective B-cell depletion; the choice reflects different balancing of efficacy, safety, and convenience.

### Compensatory Mechanisms and Long-term Effects

The increase in BAFF following B-cell depletion represents a predictable homeostatic response rather than a treatment failure. The simultaneous decrease in sTACI may actually enhance therapeutic benefit by allowing more free APRIL to promote regulatory plasma cells and IL-10 production. This explains the apparent paradox where exogenous sTACI (atacicept) worsened MS, despite its theoretical B-cell depleting effects. The therapeutic window for anti-CD20 therapy thus involves balancing B-cell depletion against preservation of regulatory mechanisms, with the BAFF-APRIL system serving as a key mediator of this balance.

## References

[J. Uchida, Y. Hamaguchi, J. Oliver, J. Ravetch, J. Poe, and 2 more\\
(2004).The Innate Mononuclear Phagocyte Network Depletes B Lymphocytes through Fc Receptor–dependent Mechanisms during Anti-CD20 Antibody Immunotherapy. Journal of Experimental Medicine](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-1219552/index.html)

[B. Cree, Joseph R Berger, Benjamin Greenberg\\
(2025).The Evolution of Anti-CD20 Treatment for Multiple Sclerosis: Optimization of Antibody Characteristics and Function. CNS Drugs](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-277547534/index.html)

[Thalia Pacheco-Fernández, Ismahane Touil, C. Perrot, G. Elain, D. Leppert, and 2 more\\
(2018).Anti-CD20 Antibodies Ofatumumab and Ocrelizumab Have Distinct Effects on Human B-cell Survival (S52.003). Neurology](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-81368773/index.html)

[Samantha Ho, E. Oswald, Hoi Kiu Wong, A. Vural, V. Yılmaz, and 8 more\\
(2023).Ocrelizumab Treatment Modulates B-Cell Regulating Factors in Multiple Sclerosis. Neurology: Neuroimmunology & Neuroinflammation](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-256303612/index.html)

[T. Schneider-Hohendorf, Christian Wünsch, A. Schulte-Mecklenbeck, Lisa Revie, Catarina Raposo, and 7 more\\
(2025).B- and T cell receptor sequencing elucidates characteristics of lymphocyte depletion by ocrelizumab. iScience](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-280106681/index.html)

[Alice Willison, Ramona Hagler, M. Weise, S. Elben, Niklas Huntemann, and 16 more\\
(2025).Effects of Anti-CD20 Antibody Therapy on Immune Cell Dynamics in Relapsing-Remitting Multiple Sclerosis. Cells](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-277674619/index.html)

[A. Bar-Or, S. M. O’Brien, M. Sweeney, E. Fox, Jeffrey A. Cohen\\
(2021).Clinical Perspectives on the Molecular and Pharmacological Attributes of Anti-CD20 Therapies for Multiple Sclerosis. CNS Drugs](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-236958170/index.html)

[Darius Häusler, Silke Häusser-Kinzel, L. Feldmann, Sebastian Torke, G. Lepennetier, and 5 more\\
(2018).Functional characterization of reappearing B cells after anti-CD20 treatment of CNS autoimmune disease. Proceedings of the National Academy of Sciences of the United States of America](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-52172874/index.html)

[S. A. Kornilov, Nathan D. Price, Richard Gelinas, Juan Acosta, M. Brunkow, and 10 more\\
(2024).Multi-Omic characterization of the effects of Ocrelizumab in patients with relapsing-remitting multiple sclerosis. Journal of Neurological Sciences](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-273959805/index.html)

[Prajita Paul, Marjan Behzadirad, Rafid Al Hallaf, Susana C. Dominguez-Peñuela, Carlos A. Pardo, and 3 more\\
(2025).Myelin-Reactive TCR/IgM Dual-Expresser Lymphocytes in Multiple Sclerosis: Linking Pathogenesis to Anti-CD20 Therapy. Immunological Investigations](/content/review/5a67b35a-0fa7-4f6d-9881-ec59ed3dbac8/source/ss-281329396/index.html)

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## Effects of Anti-CD20 Antibody Therapy on Immune Cell Dynamics in Relapsing-Remitting Multiple Sclerosis

Alice Willison, Ramona Hagler, M. Weise, S. Elben, Niklas Huntemann, L. Masanneck, S. Pfeuffer, Stefanie Lichtenberg, Kristin S. Golombeck, Lara-Maria Preuth, L. Rolfes, Menekşe Öztürk, T. Ruck, Nico Melzer, Melanie Korsen, S. L. Hauser, Hans-Peter Hartung, Philipp A Lang, M. Pawlitzki, Saskia Räuber, Sven G. Meuth

Cells·

2025·

3 citations

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CD20 Targeting

\- CD20 epitope specificity and binding characteristics: Not mentioned
\- Binding affinity (Kd values): Not mentioned
\- Antibody structure features that affect CD20 binding: OCR is an IgG anti-CD20 monoclonal antibody, type I antibody
\- Comparison of binding characteristics to other anti-CD20 antibodies: Not mentioned
\- Dose-response relationships for CD20 binding: OCR primarily induces ADCC; dosing regimen: 300 mg with a repeat dose of 300 mg 2 weeks later, then 600 mg every 6 months

Cytotoxic Mechanisms

\- Complement-dependent cytotoxicity (CDC) activity: OFA has a stronger CDC effect, potent even with low CD20 expression.
\- Antibody-dependent cellular cytotoxicity (ADCC) activity: OCR primarily induces ADCC.
\- Direct apoptosis induction: Not mentioned.
\- Other cytotoxic pathways: Not mentioned.
\- Quantitative comparisons of mechanism potency: Not mentioned.
\- Time course of cytotoxicity: Not mentioned.
\- Genetic or phenotypic factors affecting cytotoxic efficacy: Not mentioned.

Depletion Patterns

\- Specific B-cell subpopulations targeted: naive B cells, memory B cells, transitional B cells, plasmablasts
\- B-cell populations resistant to depletion: plasma cells, CD5+CD19+CD20− B cells
\- Anatomical compartments affected: not specified
\- Degree of depletion: not specified
\- Co-depletion of CD20+ T cells: yes
\- Factors influencing depletion efficiency: not specified

Depletion Kinetics

Not mentioned (the paper does not provide specific details on the depletion kinetics of B-cells with ocrelizumab, including time to initial depletion, duration of maximal depletion, recovery timelines, or sequence of reconstitution)

Molecular Determinants

\- Antibody type: IgG anti-CD20 monoclonal antibody, type I
\- Heavy and light chain sequences: Not mentioned
\- Fc region modifications or glycoengineering: Not mentioned
\- Pharmacokinetic properties: Not mentioned
\- Dosing regimen: 300 mg initial dose, 300 mg repeat dose 2 weeks later, then 600 mg every 6 months
\- Route of administration: Intravenous
\- Engineering for B-cell depletion: Primarily induces ADCC

Comparative Mechanisms

\- Both OCR and OFA are type I antibodies that induce ADCC and CDC, but OCR primarily induces ADCC, while OFA has a stronger CDC effect.
\- The paper does not provide direct comparisons with other anti-CD20 agents like rituximab or ublituximab.
\- Differences in B-cell depletion patterns are suggested by the expansion of CD5+ B cells, indicating a differential effect of OCR compared to OFA.
\- No specific information on depletion kinetics or unique mechanistic features of OCR compared to other anti-CD20 agents is provided.
\- Clinical implications of these mechanistic differences are not explicitly discussed.

Introduction: The efficacy of anti-CD20 antibodies has significantly contributed to advancing our understanding of disease pathogenesis and improved treatment outcomes in relapsing-remitting multiple sclerosis (RRMS). A comprehensive analysis of the peripheral immune cell profile, combined with prospective clinical characterization, of RRMS patients treated with ocrelizumab (OCR) or ofatumumab (OFA) was performed to further understand immune reconstitution following B-cell depletion. Methods: REBELLION-MS is a longitudinal analysis of RRMS patients treated with either OCR (n = 34) or OFA (n = 25). Analysis of B, T, natural killer (NK) and natural killer T (NKT) cells at baseline, month 1, and 12 was performed by multidimensional flow cytometry. Data were analyzed by conventional gating and unsupervised computational approaches. In parallel, different clinical parameters were longitudinally assessed. Twenty treatment-naïve age/sex-matched RRMS patients were included as the control cohort. Results: B-cell depletion by OCR and OFA resulted in significant reductions in CD20+ T and B cells as well as B-cell subsets, alongside an expansion of CD5+CD19+CD20− B cells, while also elevating exhaustion markers (CTLA-4, PD-1, TIGIT, TIM-3) across T, B, NK, and NKT cells. Additionally, regulatory T-cell (TREG) numbers increased, especially in OCR-treated patients, and reductions in double-negative (CD3+CD4−CD8−) T cells (DN T cells) were observed, with these DN T cells having higher CD20 expression compared to CD4 or CD8 positive T cells. These immune profile changes correlated with clinical parameters, suggesting pathophysiological relevance in RRMS. Conclusions: Our interim data add weight to the argumentation that the exhaustion/activation markers, notably TIGIT, may be relevant to the pathogenesis of MS. In addition, we identify a potentially interesting increase in the expression of CD5+ on B cells. Finally, we identified a population of double-negative T cells (KLRG1+HLADR+, in particular) that is associated with MS activity and decreased with CD20 depletion.

1.Introduction

Multiple sclerosis (MS) is an immune-mediated inflammatory disorder of the central nervous system with a broad spectrum of severity and the potential to cause early-onset long-term disability in a cohort of predominantly young patients. For many years, MS was considered a primarily T-cell-mediated disease. However, the rise in the use of Bcell-depleting therapies for patients with more active disease, specifically monoclonal antibodies against CD20 like ocrelizumab (OCR) and ofatumumab (OFA), has significantly advanced our understanding of the complexity of RRMS pathogenesis and improved treatment outcomes \[1\]. Both therapies target the CD20 transmembrane protein expressed on the surface of B cells and have demonstrated high efficacy in clinical trials, decreasing disease activity and slowing disease progression \[2,3\]. Both OCR and OFA are IgG anti-CD20 monoclonal antibodies, with two light and two heavy chains, and both are type I antibodies, meaning they can crosslink two CD20 tetramers and translocate CD20 into lipid rafts, triggering antibody-dependent cellular phagocytosis (ADCC) and complement-dependent cytotoxicity (CDC) against CD20-expressing B cells \[4\]. OCR primarily induces ADCC, while OFA has a stronger CDC effect, which remains potent even with low CD20 expression \[4\]. It remains unclear exactly how CD20 depletion and subsequent immune system reconstitution contributes to amelioration of disease in RRMS. Additionally, understanding how long these changes last after treatment with OCR or OFA may aid in refining treatment approaches. This interim analysis from a non-invasive, prospective, observational study (Long-term reconstitution following B-cell depletion in multiple sclerosis (REBELLION-MS; this study is registered at ClinicalTrials.gov under the identifier NCT06586177)) reports peripheral immune reconstitution in the first 12 months of treatment following OCR and OFA therapy and how these changes are associated with the clinical disease course.

2.1.Study Population

Patients with a diagnosis of RRMS according to the 2017 McDonald criteria \[5\] were prospectively included in this study and divided into three groups: ocrelizumab, ofatumumab, and treatment-naïve relapsing-remitting multiple sclerosis (tnRRMS). The exclusion criteria were as follows: prior treatment with B cell-modulating therapies; any previous use of alemtuzumab, cyclophosphamide, mitoxantrone, azathioprine, mycophenolate mofetil, cyclosporine, methotrexate, total body irradiation, or bone marrow transplantation; impaired decision-making or consent capacity; ongoing immunosuppressive treatment for conditions other than RRMS; confirmed human immunodeficiency virus (HIV) or active/chronic hepatitis B/C infection. The decision to start OCR or OFA treatment was made independently of study inclusion. According to the Summary of Product Characteristics (SmPC), the dosage of subcutaneous ofatumumab is as follows: 20 mg at weeks 0, 1, and 2 followed by subsequent doses of 20 mg every 4 weeks, starting at week 4 and the dosage of intravenous ocrelizumab as follows: 300 mg with a repeat dose of 300 mg 2 weeks later, then 600 mg every 6 months. A simplified study design is depicted in Figure 1 . At month 1 of treatment (m1), 16 patients from the OCR group and 22 from the OFA group were assessed. At the time of data analysis, 24 patients in the OCR group and 17 patients in the OFA group had reached the 12-month (m12) follow-up point for assessment and were included in this interim analysis.

A simplified study design is depicted in Figure 1 . At month 1 of treatment (m1), 16 patients from the OCR group and 22 from the OFA group were assessed. At the time of data analysis, 24 patients in the OCR group and 17 patients in the OFA group had reached the 12-month (m12) follow-up point for assessment and were included in this interim analysis. BioRender.com/n12e680 \[6\]. EDSS-expanded disability status scale; OCR-ocrelizumab; OFAofatumumab; PBMCs-peripheral blood mononuclear cells.

2.2.Isolation of PBMCs and mFC Analysis (In-Depth mFC Cohort)

Blood samples for the study were collected concurrently with routine clinical blood draws. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood by Ficoll gradient with SepMate isolation tubes (StemCell Technologies, Vancouver, Canada) and were cryopreserved in liquid nitrogen. The samples were prepared for multidimensional flow cytometry (mFC) per standard protocol \[7\] using the 2024) BioRender.com/n12e680 \[6\]. EDSS-expanded disability status scale; OCR-ocrelizumab; OFA-ofatumumab; PBMCs-peripheral blood mononuclear cells.

Blood samples for the study were collected concurrently with routine clinical blood draws. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood by Ficoll gradient with SepMate isolation tubes (StemCell Technologies, Vancouver, Canada) and were cryopreserved in liquid nitrogen. The samples were prepared for multidimensional flow cytometry (mFC) per standard protocol \[7\] using the fluorochrome-conjugated antibodies detailed in Supplementary Table S1 . For intracellular staining (FoxP3), the Foxp3/Transcription Factor Staining Buffer Set (eBioscience, San Diego, CA, USA) was used following cell surface marker staining according to the manufacturer's instructions.

A CytoFLEX-S (Beckman Coulter) was used to acquire data. Manual gating was performed with the software 'Kaluza Flow Cytometry Analysis' version 2.1 (Supplementary Table S1 , Supplementary Figures S1-S5 ). The percentage of all living cells was calculated for every cell population and was compared among groups. Furthermore, unsupervised analysis was performed using the platform OMIQ from Dotmatics (www.omiq.ai, www. dotmatics.com, last accessed on 27 March 2025) \[8\]. For this, compensated, pre-gated event data (CD19 + B cells or CD3 + lymphocytes) were exported as .csv files with the software 'Kaluza Flow Cytometry Analysis'. The gating strategies employed for cell identification were as follows: CD19+ B cells were identified using the sequence: singlets → living cells → lineage-negative (CD3-/CD14-/CD56-) → CD19+. For CD3+ T cells, the gating sequence was singlets → living cells → CD3+ lymphocytes. To maximize sensitivity and ensure comprehensive data acquisition, the CytoFLEX-S flow cytometer was manually monitored during data collection, and the flow rate was adjusted to a low setting to ensure optimal detection and minimize cell loss, allowing, insofar as possible, the entire prepared sample volume to pass through the laser for analysis. The event data and the corresponding metadata were uploaded to the platform OMIQ. Optimized t-distributed Stochastic Neighbor Embedding (Opt-SNE) plots using the mFC data from all three groups were created using the default parameters (max iterations = 1000, opt-SNE end = 5000, perplexity = 30, theta = 0.5, components = 2, random seed = 1759, verbosity = 25). The algorithm FlowSOM (xdim = 12, ydim = 12, rlen = 10, distance metric euclidean) was used for cluster identification. A clustered heatmap of concatenated files was created to visualize the median marker expression of each cluster.

2.3.Data Analysis

RStudio (2023.06.1) was used for data analysis and visualization. p-values were calculated using analysis of variance (ANOVA) with post-hoc Tukey Honestly Significant Difference (HSD), if normality could be assumed based on the Shapiro-Wilk test, otherwise, the Kruskal-Wallis test was used with the Dunn post hoc test (p-adjustment method: Benjamini-Hochberg). A p-value of < 0.05 was considered statistically significant. Violin plots were created with the R package 'ggplot2' (v3.4.4). R packages 'umap' (v0.2.10.0) and 'ggplot2' (v3.4.4) were used to create UMAPs (uniform manifold approximation and projection for dimension reduction). To perform correlation analyses, Spearman correlation coefficients were calculated as normality of data could not be assumed. Correlation plots were created with 'ggplot2' (v3. 4.4) . Correlation was performed between the CD5 mean fluorescent intensity (MFI) of B cells, TIM-3, TIGIT, PD-1, and CTLA-4 MFIs of B cells, T cells, NK cells, and NKT cells, as well as percentages of regulatory T cells (T REG ), B cell cluster 16, T cell cluster 8, 11, 13, 15, and different clinical parameters (the Expanded Disability Status Scale (EDSS)), time since disease manifestation, time since last relapse, the annual relapse rate prior to sampling, and the total number of previous relapses). The graphical data were processed and refined for presentation using Inkscape (Harrington, B. et al., 2004 (Harrington, B. et al., -2005)) , a vector graphics software, to create the figures (Inkscape, http://www.inkscape.org/, last accessed on 27 March 2025) \[9\].

3.1.Study Population

34 OCR-treated patients, 25 OFA-treated patients, and 20 tnRRMS patients were included in the analysis. The median age of participants was 36 years (range 19-55) in the OCR group, 37 years (range 22-54) in the OFA group, and 37 years (range 19-57) in the tnRRMS group. The percentage of female patients was 71% in the OCR group, 80% in the OFA group, and 90% in the treatment-naïve group. The median disease duration was 3.42 years in both the OCR (range 0.17-22.08) and OFA (range 0.08-25.33) groups, while it was 0.63 years (range 0.00-19.67) in the treatment-naïve group. Baseline EDSS scores had a median of 2.00 (range 0.0-6.5) in the OCR group, 2.00 (range 0.0-3.5) in the OFA group, and 1.25 (range 0.0-5.0) in the treatment-naïve group. The median annualized relapse rate (ARR) at baseline (the number of relapses in the year prior to treatment) was 1 (range 0-2) for OCR, 1 (range 0-3) for OFA, and 1 (range 0-3) for the treatment-naïve group. The median number of previous disease-modifying therapies (DMTs) was 1 (range 0-4) in the OCR group, 1 (range 0-5) in the OFA group, and 0 (range 0) in the tnRRMS group (Table 1 ). The last previous DMTs were glatiramer acetate in six patients (four OFA, two OCR), dimethyl fumarate in four patients (one OFA, three OCR), interferon in four patients (one OFA, three Cells 2025, 14, 552 5 of 20 OCR), cladribine in one OFA patient, teriflunomide in six patients (three OFA, three OCR), natalizumab in eight patients (three OFA, five OCR), and fingolimod in five patients (two OFA, three OCR). A total of 25 patients (10 OFA, 15 OCR) were treatment-naïve prior to OFA or OCR initiation (Supplementary Table S2 ).

3.2.Effective Depletion of B-Cell Subsets and CD20 + T Cells Occurs Following Treatment with Anti-CD20-Antibodies

We first explored the effect of OCR and OFA on different B-cell subsets and CD20 + T cells, compared to the tnRRMS patients. UMAP analysis demonstrated longitudinal clustering of OFA-and OCR-treated patients, indicating similarities in the peripheral immune cell profile, distinct from tnRRMS (Figure 2A ). Compared to the tnRRMS patients at m1 and m12, a significant reduction in CD20 + B cells and all analyzed B-cell subsets (naïve B cells, CD24 + CD27 + regulatory B cells (Bregs), CD38 + CD24 + Bregs, HLADR + B cells, marginal zone B cells, memory B cells, transitional B cells), as well as in plasmablasts occurred (Figure 2B-K ). Plasma cell populations were expectedly not dramatically affected by Bcell depletion (Figure 2L ). In addition, the percentage of CD3 + CD20 + T cells significantly decreased at m1 and m12 (Figure 2M ).

3.3.CD5 + CD19 + B Cell Population Increases Following Anti-CD20 Antibody Treatment

A significant increase in CD5 expression was observed on the remaining B cells at m1 and m12 in OCR-and OFA-treated RRMS patients compared to tnRRMS (Figure 2N ). Unsupervised clustering was performed on the B-cell population (Figure 2O ), which identified the expansion of a CD19 + CD20 -CD5 + B-cell population in OCR-and OFAtreated patients at m1 and m12 compared to tnRRMS patients (Figure 2P ). A significantly larger population expansion was observed in the OCR-compared to OFA-treated and tnRRMS patients at m12, which was not observed at m1. We found a negative correlation between CD5 expression on B cells and EDSS at m0 (correlation coefficient r = -0.496, p = 0.001) and a positive correlation with the time since the last relapse (r = 0.456; p = 0.004) in tnRRMS patients (Figure 2Q ). In addition, we observed a negative correlation between

3.4.Increased Expression of Exhaustion/Activation Marker on Immune Cells Following Anti-CD20 Antibody Treatment

Moreover, we identified an increase in TIM-3, TIGIT, and CTLA-4 expression on T cells in B-cell-depleted patients, particularly in the OCR group, with correlations observed between these markers and clinical parameters in tnRRMS patients. At m1, TIM-3 expression on CD8 + T cells was higher in B-cell-depleted patients compared to tnRRMS (Figure 3A ). At m12, this increase was more pronounced in the OCR group than OFA but remained significant for both (Figure 3B ). TIGIT expression on CD8 + T cells also increased in the OCR group compared to tnRRMS at m12 (Figure 3C ). On CD4 + T cells at m12, significantly higher expression of CTLA-4, TIM-3, and TIGIT in the OCR group compared to tnRRMS was observed, with TIM-3 expression in the OFA group also reaching significantly higher expression at m12 (Figure 3D-F ). This pattern was also seen in CD3 + T cells (Figure 3G-I ). At m12 on CD4 + CD8 + T cells, TIM-3 expression in the OCR group was markedly increased compared to tnRRMS and to a lesser extent when compared to the OFA group; comparing OFA to tnRRMS, the increase in TIM-3 expression was also observed (Figure 3J ). TIGIT expression on CD4 + CD8 + T cells increased in the OCR group compared to tnRRMS at m12 (Figure 3K ). At baseline in tnRRMS patients, TIM-3 expression on CD4 + T cells and TIGIT expression on CD8 + T cells correlated negatively with EDSS (r = -0.393, p = 0.013 and r = -0.35, p = 0.029) (Figure 4A, B ). In addition, TIGIT expression on CD8 + T cells and on CD4 + CD8 + T cells was positively correlated with the time since disease manifestation (r = 0.422, p = 0.007 and r = 0.341, p = 0.034) (Figure 4C, D ). Furthermore, we observed a negative correlation between TIGIT expression on T cells (CD3 + , CD4 + , CD8 + , CD4 + CD8 + ) in treatment-naïve OCR und OFA patients at baseline and EDSS at m1 (r = -0.663, p = 0.0187; r = -0.71, p = 0.0097; r = -0.678, p = 0.0155; r = -0.595, p = 0.0412) (Figure 4E-H ).

expression on CD4 T cells and TIGIT expression on CD8 T cells correlated negatively with EDSS (r = -0.393, p = 0.013 and r = -0.35, p = 0.029) (Figure 4A, B ). In addition, TIGIT expression on CD8 + T cells and on CD4 + CD8 + T cells was positively correlated with the time since disease manifestation (r = 0.422, p = 0.007 and r = 0.341, p = 0.034) (Figure 4C, D ). Furthermore, we observed a negative correlation between TIGIT expression on T cells (CD3 + , CD4 + , CD8 + , CD4 + CD8 + ) in treatment-naïve OCR und OFA patients at baseline and EDSS at m1 (r = -0.663, p = 0.0187; r = -0.71, p = 0.0097; r = -0.678, p = 0.0155; r = -0.595, p = 0.0412) (Figure 4E-H ). Beyond the increased expression of exhaustion/activation marker on T cells of B-celldepleted patients, we observed significant changes in their expression levels on B, NK, and NKT cells. At m1, expression of TIGIT, PD-1, and CTLA-4 on B cells was increased in both OCR-and OFA-treated patients compared to tnRRMS (Figure 5A-C ). This was also observed at m12, with expression of TIM-3 also significantly increased at this time point (Figure 5D-G ). On NKT cells at m12, expression of TIM-3, PD-1, TIGIT, and CTLA-4 was higher in OCR-treated patients compared to tnRRMS (Figure 5H-K ), with PD-1 expression also higher than in OFA-treated patients (Figure 5I ). Expression of TIM-3 and TIGIT was also higher on NK cells in OCR-treated patients compared to tnRRMS at m12 (Figure 5L, M ), with TIM-3 expression higher in OCR-compared to OFA-treated patients (Figure 5L ). At baseline in tnRRMS patients, TIGIT expression on B cells negatively correlated with EDSS (r = -0.552, p = 0.0003) (Figure 4I ). In the treatment naïve OCR-and OFA-treated patients, TIGIT expression on B cells at m0 negatively correlated with EDSS at m1 (r = -0.602, p = 0.0382) (Figure 4J ). In OCR-and OFA-treated patients, EDSS at m1 positively correlated with PD-1 expression on B cells at m1 (r = 0.37, p = 0.0341) (Figure 4K ). EDSS at m1 also correlated positively with TIM-3 expression on B cells at m1 in OFA-treated patients (r = 0.49, p = 0.039) (Figure 4L ). TIGIT expression on NKT cells at baseline positively correlated with time since disease manifestation (r = 0.327, p = 0.042) (Figure 4M ). In OFA-treated patients, PD-1 expression on NKT cells at m1 positively correlated with EDSS at m1 (r = 0.524, p = 0.0256) (Figure 4N ). EDSS at m1 negatively correlated with CTLA-4 expression on NKT cells at m1 in OCR-treated patients (r = -0.516, p = 0.0487) (Figure 4O ). In OFA-treated patients, TIM-3 expression at m12 positively correlated with EDSS at m12 (r = 0.538, p = 0.0258) (Figure 4P ). TIGIT expression on NK cells at baseline negatively correlated with EDSS at baseline (r = -0.461, p = 0.003) (Figure 4Q ). Beyond the increased expression of exhaustion/activation marker on T cells of B-celldepleted patients, we observed significant changes in their expression levels on B, NK, and NKT cells. At m1, expression of TIGIT, PD-1, and CTLA-4 on B cells was increased in both OCR-and OFA-treated patients compared to tnRRMS (Figure 5A-C ). This was also observed at m12, with expression of TIM-3 also significantly increased at this time point (Figure 5D-G ). On NKT cells at m12, expression of TIM-3, PD-1, TIGIT, and CTLA-4 was higher in OCR-treated patients compared to tnRRMS (Figure 5H -K), with PD-1 expression also higher than in OFA-treated patients (Figure 5I ). Expression of TIM-3 and TIGIT was also higher on NK cells in OCR-treated patients compared to tnRRMS at m12 (Figure 5L ,M), with TIM-3 expression higher in OCR-compared to OFA-treated patients (Figure 5L ). At baseline in tnRRMS patients, TIGIT expression on B cells negatively correlated with EDSS (r = -0.552, p = 0.0003) (Figure 4I ). In the treatment naïve OCR-and with EDSS at m1 (r = 0.524, p = 0.0256) (Figure 4N ). EDSS at m1 negatively correlated with CTLA-4 expression on NKT cells at m1 in OCR-treated patients (r = -0.516, p = 0.0487) (Figure 4O ). In OFA-treated patients, TIM-3 expression at m12 positively correlated with EDSS at m12 (r = 0.538, p = 0.0258) (Figure 4P ). TIGIT expression on NK cells at baseline negatively correlated with EDSS at baseline (r = -0.461, p = 0.003) (Figure 4Q ).

3.5.An Increase in Regulatory T Cells and Decrease in Double Negative T Cell Subsets in OCRand OFA-Treated RRMS Patients

An increase in regulatory T cells (T REG ) at m1 was observed in OFA-treated patients compared to tnRRMS (Figure 6A ). At m12, T REG were higher in both OCR-and OFA-treated patients compared to those with tnRRMS; however, significance was only reached in the OCR-treated patients (Figure 6A ). Unsupervised clustering was performed on the CD3 + T cell population (Figure 6B ), allowing for the identification of three subsets of double negative (CD4 -CD8 -) T cells (DN T cells), which were reduced in OCR-and OFA-treated RRMS compared to tnRRMS patients. The percentage of KLRG1 -HLADR + DN T cells decreased in OFA-treated patients compared to tnRRMS at m12 (Figure 6C ). The percentage of KLRG1 + HLADR -DN T cells and KLRG1 + HLADR + DN T cells in patients treated with B cell depletion decreased significantly compared to tnRRMS patients at both m1 and m12 (Figure 6D, E ). Compared to CD4 and/or CD8 positive T cells, significantly more DN T cells expressed CD20 (Figure 6F ). Furthermore, we observed a positive correlation between T REG in treatment-naïve OCR and OFA patients at baseline and EDSS at m1 (r = 0.627, p = 0.029) (Figure 6G ). The percentage of KLRG1 -HLADR + DN T cells correlated positively with the number of relapses prior to baseline (r = 0.322, p = 0.01) (Figure 6H ). In addition, we observed a negative correlation between KLRG1 -HLADR + DN T cells in OFA-treated patients at m12 and EDSS in this group at m12 (r = -0.573, p = 0.0161) (Figure 6I ). m1 and m12 (Figure 6D and E ). Compared to CD4 and/or CD8 positive T cells, significantly more DN T cells expressed CD20 (Figure 6F ). Furthermore, we observed a positive correlation between TREG in treatment-naïve OCR and OFA patients at baseline and EDSS at m1 (r = 0.627, p = 0.029) (Figure 6G ). The percentage of KLRG1 -HLADR + DN T cells correlated positively with the number of relapses prior to baseline (r = 0.322, p = 0.01) (Figure 6H ). In addition, we observed a negative correlation between KLRG1 - HLADR + DN T cells in OFA-treated patients at m12 and EDSS in this group at m12 (r = -0.573, p = 0.0161) (Figure 6I ). and the 25th as well as the 75th percentiles. The whiskers extend from the hinge to the largest and smallest values, respectively, but no further than 1.5 \* IQR from the hinge. p-values were calculated using analysis of variance (ANOVA) with post hoc Tukey Honestly Significant Difference (HSD), if normality could be assumed based on the Shapiro-Wilk test, otherwise the Kruskal-Wallis test was used with the Dunn post hoc test (p-adjustment method: Benjamini-Hochberg). (F) Violin plots with overlaying boxplots illustrating the percentage of CD20 + T cells among all DN T cells compared to the percentage of CD20 + T cells among all CD4 + and/or CD8 + T cells. Boxes show the median and the 25th as well as the 75th percentiles. The whiskers extend from the hinge to the largest and smallest values, respectively, but no further than 1.5 \* IQR from the hinge. p-values were calculated with paired Wilcoxon's rank-sum test. (G-I) Correlation analysis between T REG s and EDSS at m1 (G), KLRG1 -HLADR + DN T cells and relapse quantity at m0 (H), as well as KLRG1 -HLADR + DN T cells and EDSS at m12 (I). Spearman correlation coefficients were calculated as normality of data could not be assumed. DN Tc-CD4 -CD8 -T cells; EDSS-expanded disability status scale; m-month(s); OCR-ocrelizumab; OFA-ofatumumab; pos. Tc-CD4 + and/or CD8 + T cells; PB-peripheral blood; RRMS-relapsing-remitting multiple sclerosis; tnRRMS-treatment-naïve relapsing-remitting multiple sclerosis; tnOCR/OFA-OCR-and OFA-treated RRMS patients who were treatment-naïve at m0; Tc-T cells; Treg-regulatory T cells. Statistical significance: p ≤ 0.05 (\*), p ≤ 0.01 (\*\*), p ≤ 0.001 (\*\*\*), p ≤ 0.0001 (\*\*\*\*).

4.Discussion

Previous studies have characterized the repopulation of immune cells following B-cell depletion, with a particular focus on how the T-cell compartment repopulates \[10\]\[11\]\[12\]\[13\]\[14\]. Expanding on these observations, we used a comprehensive panel of antibodies to characterize immune reconstitution in detail following B-cell depletion. We found that B-cell depletion by OCR and OFA resulted in significant reductions in CD20 + T and B cells. alongside an expansion of CD5 + CD19 + CD20 -B cells, particularly in OCR-treated patients at 12 months. Exhaustion/activation markers (CTLA-4, PD-1, TIGIT, TIM-3) across T, B, NK, and NKT cells were increased in anti-CD20 treated individuals. Expression of TIGIT generally demonstrated correlations with disease parameters that indicate a positive effect on disease course. Clinical correlations of PD-1, TIM-3, and CTLA-4 expression did not demonstrate a clear effect on disease course. Additionally, T REG s increased, particularly in OCR-treated patients, and a reduction in potentially disease-relevant double-negative (CD4 -CD8 -) T-cell subsets were observed.

4.1.The Relevance of CD5 + B Cells in MS

The CD5-expressing B-cell subsets are considered a complex, heterogeneous population of human B cells involved in 'natural' or polyreactive innate immunity, B-cell stimulation and autoreactive antibody production \[15,16\]. CD5 + murine B-1 cells have been defined as innate, natural-antibody-producing immature cells with a limited repertoire of V genes and less junctional diversity than bone marrow-derived B-2 cells. Human B1-like CD5 + cells have been found in human fetal lymph nodes \[17\], spleen \[18\], and umbilical vein cord blood \[19\], but due to the phenotypical overlap with activated B cells, this natural population of B-1-like cells is difficult to characterize in humans. CD5 + B cells have, however, been implicated in the pathogenesis and regulation of MS as detailed below.

In mouse models, a protective role of CD5 + B cells has been observed. Yanaba et al. identified a distinct regulatory subset of CD1d(hi)CD5 + B cells that modulated T cellmediated inflammation \[20\]. Interleukin (IL)-10 production was restricted to this subset and diminished in CD19-deficient mice. Adoptive transfer of this B-cell population to the CD19-deficient and wild-type mice normalized T-cell-mediated inflammation, supporting the regulatory potency of IL-10-producing CD5 + B cells \[20\]. Niino et al. demonstrated that in patients with MS, the percentage of CD5 + cells in the memory B cell (CD27 + ) subset is significantly higher during the remitting stage compared to the relapsing stage \[21\]. In a later publication, Niino et al. observed that CD5 expression on B cells decreases notably in secondary progression MS (SPMS) patients \[22\].

However, CD5 expression on B cells has also been demonstrated to be significantly higher in patients with active MS compared to those with stable disease, correlating with active lesions and myelin basic protein (MBP) antibodies \[23\]. Villar et al. investigated the role of oligoclonal immunoglobulin M (IgM) bands in cerebrospinal fluid (CSF) as a prognostic marker in MS and identified CD5 + B cells as the subpopulation responsible for secreting IgM antibodies, particularly against non-protein molecules like myelin lipids. They found that patients with these anti-lipid IgM antibodies experienced more aggressive disease \[24\]. Fluorescence-activated cell sorting (FACS) analysis of CSF and peripheral blood by Correale et al. demonstrated that CD5 + CD19 + B cells were increased intrathecally in MS compared to control and that HLA-DR expression was lower in CD5 + B cells than CD5 -B cells. The authors therefore viewed the function of CD5 + B cells to be intrathecal autoantibody production and not presentation \[25\].

The increase in CD5 expression on B cells at both m1 and m12 after B-cell depletion that we observed in our analysis suggests a compensatory or adaptive role for this population following B cell-targeted therapies. The more significant expansion observed in OCRtreated patients at m12 could indicate a differential effect of OCR compared to OFA on the B cell compartment. The negative correlation between CD5 expression and EDSS suggests that higher CD5 expression on B cells might be associated with less disability. This could indicate a protective or regulatory role for CD5 + B cells. The positive correlation between CD5 expression and the time since the last relapse suggests that CD5 positivity on B cells might contribute to disease quiescence. These findings resonate with previous studies that have highlighted the complex role of CD5 + B cells in MS. The expansion of this cell population following B cell depletion suggests that they could be involved in re-establishing immune balance after such therapies, and our population may represent B10 cells or B regulatory cells \[26\].

4.2.CTLA-4, TIGIT, TIM-3, and PD-1 Expression in MS

CTLA-4, TIGIT, TIM-3, and PD-1 are markers of immune cell exhaustion and activation \[27\]\[28\]\[29\]\[30\]\[31\]. We found significant increases in CTLA-4, TIGIT, TIM-3, and PD-1 across CD4 + , CD8 + T cells, B cells, NK cells, and NKT cells following B cell-depleting therapies compared to tnRRMS patients.

In MS patients, compared to healthy controls, expression of CTLA-4, PD-1, and TIM-3 has been found to be downregulated \[32\]. Asashima et al. explored the role of B-cell function in RRMS and found that impaired TIGIT expression may drive disease pathogenesis \[33\]. Trabattoni et al. investigated costimulatory molecule expression and cytokine production in patients with RRMS, finding that PD-1-expressing T cells and PD-L1-expressing B cells and monocytes were significantly elevated in stable MS compared to acute MS. This upregulation of the PD-1/PD-L1 pathway during stable phases of MS was linked to enhanced IL-10 production and reduced proliferation of MBP-specific CD4 + and CD8 + T cells \[34\]. Yang et al. found that TIM-3 is dysregulated in untreated MS patients, leading to impaired TIM-3-mediated immunoregulation. Blocking TIM-3 in healthy controls increased IFN-γ secretion, but this effect was absent in untreated MS patients, indicating a defect that was reversed by treatment with glatiramer acetate or IFN-β, implying that patients with MS have impaired TIM-3 expression. The restoration of TIM-3 expression and function in treated patients suggests that these therapies may partially work by restoring TIM-3-mediated regulation of T-cell function \[35\]. Koguchi et al. generated 104 T cell clones from the CSF of six patients with MS. They found that MS CSF clones, despite secreting higher levels of IFN-γ, paradoxically exhibited lower TIM-3 and T-bet expression compared to controls. IL-12 polarization further increased IFN-γ secretion while reducing TIM-3 levels in MS clones, indicating dysregulated TIM-3 expression. Reduced TIM-3 expression was linked to resistance to tolerance and enhanced T-cell proliferation and IFN-γ secretion, suggesting that impaired upregulation of TIM-3 in inflammatory sites may be an intrinsic defect contributing to MS pathogenesis \[36\]. The literature in this field of research raises interesting questions, and further characterization of immune checkpoint proteins in MS is ongoing-and necessary.

Relapse or first presentation of MS is a rare but serious complication following immune checkpoint inhibitor (ICI) treatment. In an analysis of the FDA Adverse Event Reporting System (FAERS) database and additional case reports, 14 cases of MS were identified in patients who had received an ICI for metastatic cancer \[37\]. In all cases, ICI therapy was discontinued upon clear clinical diagnosis of MS or MS relapse. One patient was retreated with the CTLA-4-blocking antibody ipilimumab after a mild MS relapse due to the favorable cancer response and experienced significantly worsened MS symptoms, leading to discontinuation of the retreatment. In two cases, the MS relapse was severe, marked by rapid progression and poor response to MS therapy \[38\] . Gerdes et al. presented a case study of a patient who developed MS during treatment with ipilimumab. After two courses of ipilimumab, the patient experienced clinical episodes of MS, with one episode showing a marked increase in MRI activity. A brain biopsy confirmed active, T cell-mediated MS \[39\]. However, the ACCLAIM study did not demonstrate an effect of abatacept (CTLA-4-Ig) on the number of new gadolinium-enhancing MRI lesions, or clinical measures of disease activity in RRMS \[40\].

Our findings contribute to the growing body of evidence that immune checkpoint markers, particularly CTLA-4, TIGIT, TIM-3, and PD-1, may be significant in the immunopathology of MS. Clinical correlations demonstrated a possibly protective effect of TIGIT expression. A clear effect of CTLA-4, TIM-3, and PD-1 on disease course was not observed in this study. Impaired expression of these markers, notably TIGIT, may impact MS severity.

4.3.CD20 + T Cells, Regulatory T Cells, and Double Negative T-Cell Subsets

CD20 + T cells have been implicated in the pathogenesis of MS through cytokine production, reduction under disease-modifying therapies, association with white matter injury, and presence intrathecally \[41\] \[42\]\[43\]. We demonstrate that CD20+ T cells are depleted by both OFA and OCR, which may contribute to their therapeutic efficacy. T REG s regulate the number and function of autoreactive T cells, with clear relevance to the pathophysiology of MS. In line with our findings, increased T REG frequencies following CD20 + depletion have been reported, indicating a shift towards an anti-inflammatory state \[44,45\]. The positive correlation between T REG in treatment-naïve OCR and OFA patients at baseline and EDSS at m1 in our cohort could indicate that the OCR-or OFA-mediated increase in T REG s might be especially beneficial for patients with low T REG counts at baseline. DN T cells, which lack CD4 and CD8 molecules necessary for stabilizing T-cell receptormajor histocompatibility complex (TCR-MHC) interactions, are involved in non-classical antigen presentation primarily through CD1 molecules \[43\]. These cells exhibit natural suppressor activity that is not MHC-restricted, and their activation and regulatory functions are thought to be influenced by direct cell-to-cell contact and TCR signaling, rather than cytokine mediation \[46\]. DN T cells are highly activated in autoimmune diseases and have been implicated in conditions such as SLE, rheumatoid arthritis, type 1 diabetes, and Sjögren's syndrome \[44,45\]. These cells are associated with increased inflammation and tissue damage, particularly in systemic lupus erythematosus (SLE) \[47\]\[48\]\[49\]\[50\]. Using unsupervised clustering of the CD3 + T-cell population, we identified three distinct subsets of DN T cells, which were notably reduced in OCR-and OFA-treated RRMS patients compared to tnRRMS patients. Specifically, the percentage of KLRG1 -HLADR + DN T cells decreased significantly in OFA-treated patients at m12 compared to tnRRMS patients. Additionally, both KLRG1 + HLADR -DN T cells and KLRG1 + HLADR + DN T cells were significantly reduced in patients treated with B-cell depletion therapy at both m1 and m12, suggesting a sustained impact on these DN T cell subsets over time. Moreover, we found that a significantly higher proportion of DN T cells expressed CD20 compared to CD4 + and CD8 + T cells, providing a potential explanation for their reduction following OCR and OFA treatment. The reduction in DN T cells in our study aligns with an anti-inflammatory reconstitution of the immune system following B-cell depletion, with therapies possibly directly or indirectly reducing pathogenic DN T-cell populations in RRMS. Furthermore, the percentage of KLRG1 -HLADR + DN T cells was positively correlated with the number of relapses prior to baseline and negatively correlated with EDSS in ofatumumab-treated patients at m12, suggesting that this specific DN T-cell subset may be involved in disease activity and relapse frequency in MS. The reduction in this subset following B-cell depletion may therefore contribute to the overall clinical efficacy of OCR and OFA in reducing MS relapse rates.

5.Conclusions

Our 12-month interim analysis provides insight into immune reconstitution following B-cell depletion with OCR and OFA in MS. Apart from the expected decrease in CD20 + B and T cells, we observed an expansion of CD5 + CD19 + B cells and an increase in exhaustion/activation markers like CTLA-4, PD-1, TIGIT, and TIM-3 across various immune cell types. Furthermore, OCR-and OFA-treatment led to an increase in T REG and a reduction in DN T cells. The correlations we found between CD5 + B cells and clinical parameters, such as reduced disability scores and longer periods since the last relapse, suggest a potentially protective role for these cells in the disease course. The increased expression of exhaustion markers across various immune cells following B-cell depletion indicates a shift towards an immunological state characterized by reduced inflammatory potential. The significant reduction in DN T cells following treatment suggests that OCR and OFA may help diminish a potentially pathogenic cell population involved in MS progression. A better understanding of the specific functions and regulatory mechanisms of DN T cells and CD5 + B-cell subsets, as well as the implications of increased exhaustion marker expression, could optimize treatment strategies. Additionally, exploring how these therapies influence the balance between pro-inflammatory and regulatory roles in these cell populations could provide valuable insights toward improving the treatment of MS patients.

6.Limitations

Our study is limited by the small patient cohort. Due to the small sample size of patients with available multidimensional flow cytometry data from all three time points (m0, m1, and m12) no longitudinal analyses were performed. The aim of this current study was to provide an exploratory analysis of how the immune system reconstitutes following B-cell depletion, and future data will address in a larger and statistically higherpowered cohort why these changes occur. Moreover, the expression of CD5 and the exhaustion/activation markers CTLA-4, PD-1, TIM-3, and TIGIT on B cells is measured against the entire B-cell population, and not subpopulations of B cells due to the limited number of channels in conventional flow cytometry. Therefore, it is possible that the depletion of CD20 + B cells artificially increases other B-cell populations.

availability

Data Availability Statement: Data underlying this study are registered with the ABCD-J data catalog at https://data.abcd-j.de/dataset/b611539b-02f7-5008-bc21-3f3bb4a3dfcd/1.0, accessed on 27 March 2025. Further information, resources, anonymized clinical and flow cytometry data can be requested via the catalog item and will be fulfilled by Saskia Räuber (saskiajanina.raeuber@med.uniduesseldorf.de).

Supplementary Materials:

The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cells14070552/s1, Figure S1 Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest:

AGW reports personal fees from Merck, travel reimbursements and meeting attendance fees from Novartin, Merck, and Sanofi; RH declares no relevant competing interests; MW declares no relevant competing interests; SE declares no relevant competing interests; NH reports personal fees from ArgenX, Merck, Novartis, and Viatris, travel reimbursements and meeting attendance fees from Alexion, ArgenX, Merck, and Novartis, research support by the UKD FUTURE program of the Deutsche Forschungsgesellschaft outside the scope of this study; LM reports no conflicts of interest related to this study; he has received honoraria for lecturing, consulting, and travel expenses for attending meetings from Biogen, Merck, Sanofi, argenX, Roche, Alexion, and Novartis, all outside the scope of this work, his research is funded by the German Multiple Sclerosis Foundation (DMSG) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-493659010; SP received travel grants from Sanofi-Aventis, Alexion, Novartis, and Merck, lecturing honoraria or compensation for consulting from Alexion, Hexal, Merck, Novartis, Sanofi-Aventis, Mylan Healthcare, Roche, and Biogen, and research support from Diamed, Merck, Biogen, Novartis, and the German Multiple Sclerosis Society North-Rhine-Westphalia; SL declares no relevant competing interests; LMP declares no relevant competing interests; LR received travel reimbursements from Merck Serono and Sanofi-Aventis; MÖ declares no relevant competing interests; TR reports grants from German Ministry of Education, Science, Research and Technology, grants from the German Research Foundation (DFG), grants and personal fees from Sanofi, Argenx, and Alexion, personal fees from Biogen, Roche, UCB, Novartis, and Teva, personal fees and nonfinancial support from Merck, outside the submitted work; NM has received honoraria for lecturing and travel expenses for attending meetings from Biogen Idec, GlaxoSmith Kline, Teva, Novartis Pharma, Bayer Healthcare, Genzyme, Alexion Pharmaceuticals, Fresenius Medical Care, Diamed, UCB Pharma, AngeliniPharma, Bial, and

Acknowledgements

Acknowledgments:We thank Annette Heß, Julia Jadasz, Mary Bayer, Andrea Issberner, Zippora Kohne, Birgit Blomenkamp, Katharina Wagner-Berres, and Annette Laufer for assistance in sample collection and Christina Berg for assistance with clinical assessments.

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