Elicit: B-cell Biomarkers and Ocrelizumab Response

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B-cell Biomarkers and Ocrelizumab Response

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May 5, 2026

Which biomarkers of B-cell depletion correlate with clinical response to ocrelizumab?

CD19+ B-cell counts, plasmablast levels, baseline regulatory cell populations (CD49d+ T-cells and B-1a cells), and CD8+ T-cell depletion correlate with clinical response to ocrelizumab, with the relative importance of each biomarker varying by MS subtype and baseline inflammatory status.

Abstract

Multiple biomarkers of B-cell depletion correlate with clinical response to ocrelizumab, though their predictive value varies by clinical context. Total CD19+ B-cell depletion showed consistent associations with outcomes in RRMS, with higher ocrelizumab exposure producing lower B-cell counts and reduced disability progression (HR 0.64, p=0.0135). Baseline CD19+ counts predicted repopulation kinetics, with counts ≥12-14% identifying patients at risk for fast repopulation and higher MRI activity (17.39% vs. 2.53%, p=0.008). Plasmablast levels emerged as particularly informative: responders at 24 months had 3-fold lower plasmablasts at 6 months compared to non-responders (7.69% vs. 22.66%, p=0.043), and plasmablast populations correlated positively with 12-month EDSS scores (R=0.564, p=0.004). However, in PPMS, baseline inflammatory status determined which biomarkers mattered—B-cell repopulation and serum neurofilament levels predicted outcomes in Gd+ patients, while serum IgA levels and T-cell remodeling were more relevant in Gd- patients. Elevated baseline regulatory populations (CD49d+ T-cells, B-1a cells) predicted achievement of NEDA-3 status, while CD8+ T-cell modulation correlated with disability outcomes (OR=1.01, p=0.02). Treatment-naive patients demonstrated stronger biomarker-outcome correlations than previously treated patients (HR=2.53 for radiological activity with prior treatment, p=0.039), suggesting prior therapies modify B-cell compartment dynamics and diminish predictive accuracy of standard depletion metrics.

Methods

We analyzed 10 sources from an initial pool of 200, using 8 screening criteria. Each paper was reviewed for 5 key aspects that mattered most to the research question. More on methods

Records from Elicit search

n = 200

Papers screened using: Ocrelizumab Treatment, B-cell Depletion Biomarkers, Clinical Response Outcomes, Quantitative Correlation Analysis, Follow-up Biomarker Measurements, Adequate Sample Size, Full-text Publication, Ocrelizumab-specific Data

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 and OpenAlex.

We ran this query: “Which biomarkers of B-cell depletion correlate with clinical response to ocrelizumab?”

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:

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.

Extract all biomarkers of B-cell depletion that were measured, including:

Extract all measures used to assess clinical response to ocrelizumab, including:

Extract all reported correlations, associations, or relationships between B-cell depletion biomarkers and clinical response outcomes, including:

Extract patient characteristics that may influence biomarker-clinical response relationships, including:

Extract the main conclusions about which biomarkers of B-cell depletion correlate with clinical response to ocrelizumab, including:

Results

Characteristics of Included Studies

All 10 studies evaluated biomarkers of B-cell depletion in patients treated with ocrelizumab, though they varied in MS subtype, sample size, and follow-up duration.

Study

Full text retrieved?

MS Subtype

Sample Size

Follow-up Duration

Previous Treatment

Baseline EDSS (median/mean)

S. Hauser et al., 2023

Yes

RRMS and PPMS

Not specified

96 weeks (RRMS), variable (PPMS)

Randomized to ocrelizumab or comparator

Not specified

Ece Akbayır et al., 2025

Yes

RRMS

31

12 months

Previously treated, resistant to first-line agents

Not specified

M. Boziki et al., 2022

Yes

Active PPMS

22

24 months

Treatment-naive for immunosuppressants

4.91 (mean)

J. I. Fernández-Velasco et al., 2021

Yes

PPMS

53

6 months

Not specified

6 (median)

Alice Willison et al., 2025

Yes

RRMS

34 OCR, 25 OFA, 20 treatment-naive

12 months

Median 1 previous DMT (OCR and OFA groups); 25 treatment-naive

2.00 (median)

M. Cellerino et al., 2021

Yes

RRMS (n=93), PPMS (n=43), SPMS (n=17)

153

1.9 years (median)

76.95% previously treated

Lower baseline EDSS associated with better outcomes

Hany Mohammed Amin Aref et al., 2024

No

RRMS

30

12 months

Most had two previous DMTs; 13.3% treatment-naive

4.5 (median)

G. Abbadessa et al., 2021

Yes

RRMS (n=127), SPMS (n=43), PPMS (n=48)

218 (155 at 12 months)

At least 6 months; 155 with 12-month follow-up

76.95% previously treated; 23.04% treatment-naive

Not specified

J. I. Fernández-Velasco et al., 2022

Yes

PPMS

69

12 months

Not specified

5.5 (median)

Nicola Capasso et al., 2022

Yes

RRMS, SPMS, PPMS

78

36.5 months (mean)

Previous DMTs collected at baseline

Not specified

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The included studies represented diverse MS populations, with sample sizes ranging from 22 to 218 patients. Most studies (8 of 10) had full-text availability. Follow-up durations ranged from 6 months to 36.5 months. The majority of patients across studies had prior treatment exposure, though several studies included treatment-naive cohorts. Baseline disability varied substantially, with median/mean EDSS scores ranging from 2.00 to 6.0, reflecting heterogeneity in disease severity.

B-Cell Depletion Biomarkers Measured

Studies employed diverse approaches to quantify B-cell depletion, though CD19+ B cells emerged as the most commonly measured biomarker. S. Hauser et al. measured CD19+ B cells in cells/μL and categorized patients by median B-cell levels (0, 1-5, and >5 cells/μL). Hany Mohammed Amin Aref et al. reported baseline CD19 B cell counts of 65.98 ± 79.04 cells/μl, which depleted to 3.59 ± 2.94 cells/μl at 1 month, 25.6 ± 43.98 cells/μl at 6 months, and 11.11 ± 19.53 cells/μl at 12 months. G. Abbadessa et al. measured CD19 as a percentage, reporting baseline values of 12.94% that decreased to 0.45% after 12 months.

Several studies examined specific B-cell subsets beyond total CD19+ counts. Ece Akbayır et al. quantified plasma cells (CD19+ CD38+ CD138+), which were significantly suppressed at 12 months, along with naive B-cells (CD19+ CD27-IgD+) and switched memory B-cells (CD19+ CD27+ IgD-). M. Boziki et al. used 9-color multiparametric flow cytometry to assess plasmablasts, transitional B-cells, marginal-zone-like B-cells, class-switched memory B-cells, non-switched CD27+ memory B-cells, and naive B-cells, with particular focus on plasmablast percentages of CD19+ cells. J. I. Fernández-Velasco et al. reported drastic reduction in naive and memory B cells, along with significant increases in the proportions of plasmablasts and transitional B cells relative to total CD19+ B cells.

Alice Willison et al. documented significant reductions in CD20+ T and B cells, various B-cell subsets (naive B cells, regulatory B cells, marginal zone B cells, memory B cells, transitional B cells), and noted an expansion of CD5+CD19+CD20− B cells at months 1 and 12. Nicola Capasso et al. measured total lymphocyte counts and CD19+ and CD20+ subpopulations, reporting reductions that remained stable after the first infusion.

Two studies categorized patients based on repopulation kinetics. G. Abbadessa et al. identified fast repopulation (FR) and slow repopulation (SR) groups, with FR patients showing higher baseline CD19 cell counts (12% for 6-month cut-off, 14% for 12-month cut-off). S. Hauser et al. noted complete depletion with slow repopulation, with median time to B-cell repletion of 72 weeks.

Clinical Response Measures

Studies used heterogeneous outcome measures to assess clinical response to ocrelizumab. Disability progression was measured using confirmed disability progression (CDP) at 24 weeks, confirmed disability accumulation (CDA), confirmed disability worsening, and EDSS progression. EDSS scores were reported at various timepoints across studies.

Relapse-related outcomes included annualized relapse rate (ARR), time to first relapse, and number of attacks. ARR was significantly reduced from 20 to 0 after 12 months in the Aref et al. study and reached very low levels (0.13-0.18) across exposure quartiles in the Hauser et al. analysis.

MRI outcomes varied across studies. S. Hauser et al. assessed new/enlarging T2 lesions, T1 gadolinium-enhancing lesions, and brain volume changes. M. Boziki et al. measured new/enlarging T2 lesions and brain volume changes, with significant effects on gray matter cerebellar volume (F=4.342, p=0.23) and normalized gray matter cerebellar volume (F=4.279, p=0.024). Several studies evaluated the presence or absence of Gd-enhancing lesions.

Composite measures were employed in multiple studies. NEDA-3 (no evidence of disease activity) was defined as absence of relapses, confirmed disability worsening, and MRI activity. Ece Akbayır et al. achieved NEDA-3 in 19 of 31 patients (61.3%). M. Boziki et al. defined optimal response as patients free of relapses, CDA, and new/enlarging T2 lesions, achieved by 63.6% at 12 months and 59.1% at 24 months. Hany Mohammed Amin Aref et al. used the Multiple Sclerosis Functional Composite score, which showed significant improvement across three timepoints.

Biomarker-Clinical Response Correlations

Total B-Cell and CD19+ Depletion

S. Hauser et al. demonstrated a dose-response relationship between ocrelizumab exposure, B-cell depletion, and clinical outcomes. Higher ocrelizumab exposure led to lower blood B-cell counts, and higher median B-cell levels were associated with greater rates of disability progression. The hazard ratio for disability progression was 0.64 (95% CI: 0.45-0.92; p=0.0135) favoring higher ocrelizumab exposure. Patients with more complete B-cell depletion showed greater reduction in risk of disability progression.

Hany Mohammed Amin Aref et al. found a statistically significant correlation between CD19 B cell counts and EDSS scores, with sustained CD19 B cell depletion associated with marked clinical and radiological improvement. However, no significant correlation was found with the Multiple Sclerosis Functional Composite score or MRI disease activity.

G. Abbadessa et al. identified baseline CD19 cell count as a predictor of repopulation kinetics and clinical outcomes. Higher baseline CD19 counts were associated with fast repopulation rate (FR). FR patients had significantly higher active MRI scans at 12 months compared to slow repopulation (SR) patients (17.39% vs. 2.53%; p=0.008). ROC curve analysis established cut-offs of 12% CD19 cells at baseline for predicting fast repopulation at 6 months (AUC=0.62) and 14% for 12 months (AUC=0.71).

B-Cell Subset Correlations

Plasmablasts emerged as a significant predictor of treatment response in multiple studies. M. Boziki et al. found that responders at 24 months had significantly reduced peripheral blood plasmablasts (7.69 ± 4.4% of CD19+ cells) compared to non-responders (22.66 ± 7.19%) at the 6-month timepoint (p=0.043). This was the first study to link reduced plasmablast depletion to suboptimal response in active PPMS.

Ece Akbayır et al. demonstrated that month 12 EDSS scores correlated positively with plasmablast populations (R=0.564, p=0.004). Additionally, reduced post-treatment populations of switched memory B-cells were negatively correlated with treatment efficacy, suggesting that failure to suppress these populations indicates reduced treatment response.

Memory B-cell dynamics showed complex patterns. While Ece Akbayır et al. found reduced switched memory B-cells associated with better outcomes, J. I. Fernández-Velasco et al. reported significant reductions in both naive and memory B cells that correlated with decreased serum neurofilament light chain (sNfL) levels (p<0.0001 for B-cell reduction; p=0.008 for sNfL reduction).

J. I. Fernández-Velasco et al. (2022) observed that low repopulation rate of inflammatory B cells and reduced sNfL levels were associated with NEDA status in patients with gadolinium-enhancing lesions (Gd+). In Gd- patients, low B-cell numbers and sNfL levels were observed, but increases in serum IgA levels and IgA/IgM ratio were more significant for NEDA status.

Regulatory Cell Populations

Elevated baseline populations of regulatory cells predicted better outcomes. Ece Akbayır et al. found that baseline regulatory CD49d+ T-cells and B-1a cells were positively correlated with achieving NEDA-3 status. Alice Willison et al. reported that CD5 expression on B cells was negatively correlated with EDSS at baseline (r=-0.496, p=0.001) and positively correlated with time since last relapse (r=0.456, p=0.004).

Naive T-cell populations also showed protective associations. Ece Akbayır et al. found increased post-treatment populations of naive T-cells were positively correlated with lower EDSS scores at month 6 (R=-0.515 and R=-0.602, p=0.003) and higher rates of NEDA-3 status.

T-Cell and Exhaustion Markers

M. Cellerino et al. identified CD8+ cells as a potential biomarker, with the decrease in CD8+ cells being less pronounced in patients with inflammatory activity (p=0.022). CD8+ cell count was independently associated with early inflammatory activity (OR 1.005, 95% CI 1.001-1.009, p=0.019).

Nicola Capasso et al. found that higher probability of EDSS progression was associated with reduced modulation (i.e., higher levels) of CD8 lymphocytes (OR=1.01, 95% CI=1.00-1.05, p=0.02), suggesting that progressive reduction of CD8 cytotoxic T-lymphocytes correlates with clinical stability.

Alice Willison et al. documented extensive correlations with exhaustion markers. TIGIT expression on CD8+ T cells was negatively correlated with EDSS at baseline (r=-0.35, p=0.029) and positively correlated with time since disease manifestation (r=0.422, p=0.007). TIM-3 expression on CD4+ T cells showed negative correlation with baseline EDSS (r=-0.393, p=0.013). PD-1 expression on B cells was positively correlated with EDSS at month 1 (r=0.37, p=0.0341), while CTLA-4 expression on NKT cells was negatively correlated with EDSS at month 1 in OCR-treated patients (r=-0.516, p=0.0487).

Double-negative T cells (KLRG1-HLADR+ DN T cells) showed a positive correlation with the number of relapses prior to baseline (r=0.322, p=0.01) and negative correlation with EDSS at month 12 (r=-0.573, p=0.0161).

Patient-Level Predictors

Lower baseline EDSS was independently associated with reduced risk of disability worsening (HR=1.45, 95% CI 1.05-2.00, p=0.024). Previous treatment exposure was independently associated with increased probability of radiological activity (HR=2.53, 95% CI 1.05-6.10, p=0.039), suggesting treatment-naive patients may respond better to ocrelizumab.

Synthesis

The studies reveal complex, context-dependent relationships between B-cell depletion biomarkers and clinical response to ocrelizumab. Rather than a single biomarker universally predicting outcomes, different markers appear relevant in different clinical contexts.

MS Subtype as a Context Modifier

The relevance of specific biomarkers varies by MS subtype. In RRMS populations, total B-cell depletion metrics (CD19+ counts, repopulation kinetics) showed strong associations with clinical outcomes. The Hauser et al. post-hoc analysis of the OPERA trials demonstrated clear dose-response relationships in RRMS, with hazard ratios for disability progression ranging from 0.70 in the lowest exposure quartile to 0.34 in the highest quartile versus interferon β-1a.

In PPMS populations, the pattern differed substantially. While Hauser et al. observed similar trends (hazard ratios ranging from 0.88 to 0.55 across quartiles), the PPMS-specific studies revealed that baseline inflammatory status fundamentally altered which biomarkers mattered. Fernández-Velasco et al. (2022) demonstrated that in Gd+ PPMS patients, B-cell repopulation rate and sNfL levels predicted NEDA status, whereas in Gd- patients, the same B-cell metrics showed no predictive value, with serum IgA levels and T-cell/innate immune remodeling instead correlating with outcomes. This suggests that in non-inflammatory PPMS, B-cell depletion enables immune modulation through indirect mechanisms rather than directly suppressing inflammatory B cells.

The Plasmablast Paradox

Multiple studies identified plasmablasts as paradoxically important despite representing a small fraction of circulating B cells. Boziki et al. found that responders had 3-fold lower plasmablasts (7.69% vs. 22.66% of CD19+ cells, p=0.043) at 6 months, while Akbayır et al. showed plasmablast populations correlated positively with 12-month EDSS scores (R=0.564, p=0.004). Fernández-Velasco et al. (2021) noted that plasmablast proportions increased significantly after ocrelizumab, seemingly contradicting their role as a negative prognostic marker.

This apparent contradiction resolves when considering that ocrelizumab depletes CD20+ B cells but spares CD20-negative plasmablasts and plasma cells. The increase in plasmablast proportion (relative to depleted total B cells) does not indicate absolute expansion. Studies measuring absolute plasmablast depletion efficiency—rather than proportional changes—consistently found that incomplete plasmablast suppression predicts worse outcomes. This suggests residual plasmablasts may sustain pathogenic antibody production or represent reservoirs for rapid B-cell reconstitution.

Baseline Biomarkers Versus Dynamic Markers

Studies diverged on whether baseline or post-treatment biomarker levels better predicted outcomes, likely reflecting different mechanistic questions. Baseline CD19+ counts predicted repopulation kinetics: Abbadessa et al. established that baseline counts ≥12-14% identified patients who would experience fast repopulation and higher MRI activity. This suggests baseline B-cell burden determines the “depth” of achievable depletion with standard dosing.

Conversely, post-treatment dynamics—particularly at 6 months—predicted clinical trajectories in multiple studies. Boziki et al. found 6-month plasmablast levels distinguished responders from non-responders at 24 months, while Akbayır et al. showed that 6-month switched memory B-cell levels and naive T-cell populations correlated with NEDA-3 status. This 6-month timepoint may represent a critical window when the balance between depletion efficacy and early reconstitution becomes apparent, before full disease reactivation occurs.

The T-Cell Connection: Indirect Effects of B-Cell Depletion

Several studies identified T-cell populations as biomarkers despite ocrelizumab targeting CD20. Cellerino et al. found CD8+ cell counts independently associated with inflammatory activity (OR 1.005, p=0.019), while Capasso et al. showed reduced CD8+ modulation predicted EDSS progression (OR=1.01, p=0.02). These findings reflect that ocrelizumab depletes CD20+ T cells alongside B cells, with Cellerino et al. explicitly linking CD8+ depletion to reduced CD20+ T cells.

Willison et al. provided the most comprehensive T-cell profiling, demonstrating that exhaustion markers (TIGIT, TIM-3, PD-1, CTLA-4) on T, B, NK, and NKT cells increased with ocrelizumab and correlated with clinical parameters. TIGIT expression showed particularly consistent protective correlations across cell types (negative correlation with EDSS, positive correlation with time since manifestation). These findings suggest ocrelizumab induces a broader immune remodeling toward an exhausted/regulatory phenotype, potentially explaining efficacy beyond simple B-cell depletion.

Regulatory T-cell expansion provided mechanistic insight: Akbayır et al. found elevated baseline regulatory CD49d+ T-cells and B-1a cells predicted NEDA-3 status, while Willison et al. documented CD5+CD19+CD20− B cell expansion. This suggests successful treatment may depend on concurrent expansion of regulatory populations that suppress residual effector cells.

Treatment History as a Confounding Factor

Previous treatment exposure consistently modified biomarker-response relationships. Cellerino et al. found prior DMT exposure independently increased radiological activity risk (HR=2.53, p=0.039), while treatment-naive patients had higher NEDA-3 probabilities. Abbadessa et al.’s cohort was 76.95% previously treated, potentially explaining why baseline CD19 counts varied so widely (sufficient to create meaningful quartiles for analysis).

This likely reflects that prior treatments alter the B-cell compartment’s composition and repopulation capacity. Patients who failed previous therapies may have more aggressive disease biology, enrichment for treatment-resistant B-cell clones, or altered B-cell homeostatic mechanisms. The stronger biomarker-outcome correlations in treatment-naive cohorts suggest that without prior perturbations, B-cell depletion metrics more directly reflect disease activity.

Dose-Response and Threshold Effects

The Hauser et al. exposure-response analysis revealed a critical asymmetry: while ARR and MRI lesions reached near-zero levels across all exposure quartiles (suggesting a ceiling effect), disability progression showed continuous exposure-dependent improvement. This suggests different disease processes have different B-cell dependency thresholds. Inflammatory relapses and focal lesions may require only moderate B-cell depletion (achievable at lower exposures), while disability progression—potentially driven by compartmentalized inflammation or chronic B-cell activity in CNS tissues—benefits from maximal depletion.

Abbadessa et al.’s ROC analysis established practical cut-offs (12% and 14% baseline CD19), but the modest AUCs (0.62 and 0.71) indicate substantial overlap between responders and non-responders. This suggests baseline counts provide risk stratification rather than deterministic prediction, and that other factors (genetic, compartmentalized immunity) substantially influence outcomes.

Reconciling Heterogeneity: Toward an Integrated Model

The apparent heterogeneity in biomarker findings reflects that B-cell depletion operates through multiple mechanisms that vary by clinical context:

  1. In inflammatory RRMS/active PPMS: Direct suppression of pathogenic B cells (measured by CD19+ counts, plasmablast depletion) correlates with reduced relapses and MRI activity.

  2. In non-inflammatory PPMS: B-cell depletion enables immune remodeling (measured by regulatory cell expansion, IgA changes, T-cell modulation) that indirectly affects progression.

  3. Across all subtypes: Completeness of depletion (measured by repopulation kinetics, residual plasmablasts) predicts durability of response, while baseline regulatory populations (CD49d+ T-cells, B-1a cells) predict capacity for beneficial immune remodeling.

  4. In previously treated patients: Altered B-cell compartments and potentially resistant clones diminish predictive value of standard depletion metrics.

Rather than seeking a single “best” biomarker, these findings suggest clinical response depends on achieving adequate depletion and favorable immune reconstitution. Baseline metrics (CD19 counts, regulatory cells) predict depletion depth and reconstitution quality, while early post-treatment measures (6-month plasmablasts, memory B-cells, T-cell profiles) predict the realized treatment response before clinical outcomes fully manifest.

References

Ece Akbayır, Tugce Kizilay, Ruziye Erol, Duygu OZKAN‐YASARGUN, E. Tuzun, and 2 more\ (2025).B‐Cell and T‐Cell Populations in Peripheral Blood Linked to Ocrelizumab Treatment Efficacy in Multiple Sclerosis. In Vivo

M. Boziki, C. Bakirtzis, Styliani-Aggeliki Sintila, E. Kesidou, Evdoxia Gounari, and 11 more\ (2022).Ocrelizumab in Patients with Active Primary Progressive Multiple Sclerosis: Clinical Outcomes and Immune Markers of Treatment Response. Cells

J. I. Fernández-Velasco, J. Kuhle, E. Monreal, V. Meca-Lallana, J. Meca-Lallana, and 20 more\ (2021).Effect of Ocrelizumab in Blood Leukocytes of Patients With Primary Progressive MS. Neurology: Neuroimmunology & Neuroinflammation

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

M. Cellerino, G. Boffa, C. Lapucci, F. Tazza, E. Sbragia, and 11 more\ (2021).Predictors of Ocrelizumab Effectiveness in Patients with Multiple Sclerosis. Neurotherapeutics

Hany Mohammed Amin Aref, Azza Abd El-Nasser Abdelaziz, Abeer Al-Sayed Ali Shehab, D. Zamzam, Alaa Mohamed Abou Steit, and 1 more\ (2024).Follow Up of Total B Cells (CD 19) Count after B Cell Depleting Therapy in Relapse Remitting Multiple Sclerosis Disorder in Egyptian Patients. The Quarterly journal of medicine

G. Abbadessa, G. Miele, P. Cavalla, P. Valentino, G. Marfia, and 10 more\ (2021).CD19 Cell Count at Baseline Predicts B Cell Repopulation at 6 and 12 Months in Multiple Sclerosis Patients Treated with Ocrelizumab. International Journal of Environmental Research and Public Health

J. I. Fernández-Velasco, E. Monreal, J. Kuhle, V. Meca-Lallana, J. Meca-Lallana, and 26 more\ (2022).Baseline Inflammatory Status Reveals Dichotomic Immune Mechanisms Involved In Primary-Progressive Multiple Sclerosis Pathology. Frontiers in Immunology

Nicola Capasso, R. Palladino, Vincenza Cerbone, A. Spiezia, B. Covelli, and 8 more\ (2022).Ocrelizumab effect on humoral and cellular immunity in multiple sclerosis and its clinical correlates: a 3-year observational study. Journal of Neurology

S. Hauser, A. Bar-Or, Martin S. Weber, H. Kletzl, A. Günther, and 8 more\ (2023).Association of Higher Ocrelizumab Exposure With Reduced Disability Progression in Multiple Sclerosis. Neurology: Neuroimmunology & Neuroinflammation

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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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B-Cell Depletion Biomarkers

- Specific cell types/markers: CD20+ T and B cells, naïve B cells, CD24+CD27+ regulatory B cells, CD38+CD24+ regulatory B cells, HLADR+ B cells, marginal zone B cells, memory B cells, transitional B cells, plasmablasts, CD5+CD19+CD20− B cells, CD3+CD20+ T cells - Measurement units: Not specified - Baseline values and follow-up values: Not specified - Depletion patterns: Significant reductions at m1 and m12; expansion of CD5+CD19+CD20− B cells - Categorizations: Not specified

Clinical Response Measures

- Relapse-related outcomes: Median annualized relapse rate (ARR) at baseline was 1 for OCR and OFA groups. - Disability measures: Baseline EDSS scores had a median of 2.00 for OCR and OFA groups. - Correlations: TIM-3 and TIGIT expression correlated with EDSS scores; KLRG1 -HLADR + DN T cells correlated with relapse number and EDSS. - Timepoints: Baseline, month 1 (m1), and month 12 (m12) follow-ups.

Biomarker-Response Correlations

- CD5 expression on B cells: Negative correlation with EDSS at baseline (r = -0.496, p = 0.001); Positive correlation with time since last relapse (r = 0.456, p = 0.004) - TIM-3 expression on CD4+ T cells: Negative correlation with EDSS at baseline (r = -0.393, p = 0.013) - TIGIT expression on CD8+ T cells: Negative correlation with EDSS at baseline (r = -0.35, p = 0.029); Positive correlation with time since disease manifestation (r = 0.422, p = 0.007) - TIGIT expression on T cells: Negative correlation with EDSS at month 1 (r = -0.663, p = 0.0187; r = -0.71, p = 0.0097; r = -0.678, p = 0.0155; r = -0.595, p = 0.0412) - PD-1 expression on B cells: Positive correlation with EDSS at month 1 (r = 0.37, p = 0.0341) - TIM-3 expression on B cells: Positive correlation with EDSS at month 1 in OFA-treated patients (r = 0.49, p = 0.039) - TIGIT expression on NKT cells: Positive correlation with time since disease manifestation (r = 0.327, p = 0.042) - PD-1 expression on NKT cells: Positive correlation with EDSS at month 1 in OFA-treated patients (r = 0.524, p = 0.0256) - CTLA-4 expression on NKT cells: Negative correlation with EDSS at month 1 in OCR-treated patients (r = -0.516, p = 0.0487) - TIM-3 expression at month 12: Positive correlation with EDSS at month 12 in OFA-treated patients (r = 0.538, p = 0.0258) - TIGIT expression on NK cells: Negative correlation with EDSS at baseline (r = -0.461, p = 0.003) - KLRG1 -HLADR + DN T cells: Positive correlation with number of relapses prior to baseline (r = 0.322, p = 0.01); Negative correlation with EDSS at month 12 (r = -0.573, p = 0.0161)

Patient Characteristics

- MS subtype: RRMS - Disease duration: Median 3.42 years (OCR), 3.42 years (OFA), 0.63 years (tnRRMS) - Baseline disability (EDSS): Median 2.00 (OCR), 2.00 (OFA), 1.25 (tnRRMS) - Previous treatment history: Median 1 previous DMT (OCR), 1 previous DMT (OFA), 0 previous DMTs (tnRRMS); 25 patients treatment-naive - Baseline disease activity: Median ARR 1 (OCR), 1 (OFA), 1 (tnRRMS) - Demographics: Median age 36 years (OCR), 37 years (OFA), 37 years (tnRRMS); Female percentage 71% (OCR), 80% (OFA), 90% (tnRRMS) - Sample size and follow-up duration: 34 OCR-treated, 25 OFA-treated, 20 tnRRMS; Follow-up at month 1 and month 12

Key Findings

- Primary findings: B-cell depletion by OCR and OFA leads to reductions in CD20+ T and B cells, expansion of CD5+CD19+CD20− B cells, and increased exhaustion markers (CTLA-4, PD-1, TIGIT, TIM-3). - Clinical implications: TIGIT expression may be protective; CD5 expression on B cells is associated with reduced disability and longer relapse-free periods. - Limitations: Small patient cohort; future studies needed to confirm findings in a larger cohort. - Future research directions: Further investigation into the role of these biomarkers in MS pathogenesis and their potential as treatment targets.

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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