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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Ocrelizumab in Patients with Active Primary Progressive Multiple Sclerosis: Clinical Outcomes and Immune Markers of Treatment Response

M. Boziki, C. Bakirtzis, Styliani-Aggeliki Sintila, E. Kesidou, Evdoxia Gounari, A. Ioakimidou, V. Tsavdaridou, L. Skoura, A. Fylaktou, V. Nikolaidou, Maria Stangou, I. Nikolaidis, V. Giantzi, Eleni Karafoulidou, Paschalis Theotokis, N. Grigoriadis

Cells·

2022·

8 citations

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B-Cell Depletion Biomarkers

- Specific cell types/markers: CD19+, plasmablasts, transitional B-cells, marginal-zone-like B-cells, class-switched memory B-cells, non-switched CD27+ memory B-cells, naïve B-cells - Measurement units: Percentage of CD19+ cells - Baseline values and follow-up timepoints: Baseline and 6-month point estimate - Depletion patterns: Significant reduction in CD19+ cells and other B-cell subsets; reduced plasmablast depletion linked to sub-optimal response - Categorizations: Responders vs. non-responders based on plasmablast levels

Clinical Response Measures

- Relapse-related outcomes: Absence of relapses - Disability measures: EDSS scores, confirmed disability accumulation (CDA) - MRI outcomes: New/enlarging T2 lesions, brain volume changes - Composite measures: Optimal response at 24 months (absence of relapses, CDA, new/enlarging T2 lesions) - Timepoints: 12 and 24 months

Biomarker-Response Correlations

- Reduced peripheral blood plasmablasts (% of CD19+ cells) at 6 months are significantly correlated with optimal response to ocrelizumab at 24 months (p = 0.043). - Reduced plasmablast depletion is associated with sub-optimal response to ocrelizumab. - No significant correlation between serum cytokine levels (including IL-6) and clinical response to ocrelizumab. - No dose-response or threshold effects identified.

Patient Characteristics

- MS subtype: Active primary progressive multiple sclerosis (PPMS) - Disease duration: Mean disease duration was 9.05 ± 0.92 years - Baseline disability (EDSS): Mean EDSS was 4.91 ± 0.3 - Previous treatment history: Treatment-naive for immunosuppressants - Baseline disease activity: Not explicitly mentioned - Demographics: Mean age 48.5 ± 1.69 years; 12 males, 10 females - Sample size and follow-up duration: 22 patients; 24 months

Key Findings

- Primary findings: Reduced plasmablast depletion is linked to sub-optimal response to ocrelizumab in aPPMS. - Clinical implications: This correlation suggests that monitoring plasmablast levels could help predict treatment response. - Recommendations: Further studies are needed to validate these findings and explore other biomarkers. - Limitations: Low participant numbers may have limited the identification of other biomarker-response relationships. - Future research directions: Investigate the biological implications and clinical outcomes associated with ocrelizumab's effect on the immunological profile in aPPMS.

Ocrelizumab is a B-cell-depleting monoclonal antibody approved for the treatment of relapsing-remitting multiple sclerosis (RRMS) and active primary progressive MS (aPPMS). This prospective, uncontrolled, open-label, observational study aimed to assess the efficacy of ocrelizumab in patients with aPPMS and to dissect the clinical, radiological and laboratory attributes of treatment response. In total, 22 patients with aPPMS followed for 24 months were included. The primary efficacy outcome was the proportion of patients with optimal response at 24 months, defined as patients free of relapses, free of confirmed disability accumulation (CDA) and free of T1 Gd-enhancing lesions and new/enlarging T2 lesions on the brain and cervical MRI. In total, 14 (63.6%) patients and 13 patients (59.1%) were classified as responders at 12 and 24 months, respectively. Time exhibited a significant effect on mean absolute and normalized gray matter cerebellar volume (F = 4.342, p = 0.23 and F = 4.279, p = 0.024, respectively). Responders at 24 months exhibited reduced peripheral blood ((%) of CD19+ cells) plasmablasts compared to non-responders at the 6-month point estimate (7.69 ± 4.4 vs. 22.66 ± 7.19, respectively, p = 0.043). Response to ocrelizumab was linked to lower total and gray matter cerebellar volume loss over time. Reduced plasmablast depletion was linked for the first time to sub-optimal response to ocrelizumab in aPPMS.

1.Introduction

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease of the central nervous system, with T-cells of the adaptive immune system being regarded as the main mediators of the disease [1]. Recently, a role for B-cells in relation to disease activity and progression has emerged [2]. The contribution of B-cells in MS pathogenesis has long been suspected; however, several mechanisms of this contribution have recently been elucidated. For instance, oligoclonal bands (OCBs) have long been utilized as diagnostic markers for MS, in the frame of the widely diagnostic criteria [3]. OCBs are antibodies of the IgG class that are produced intrathecally in the context of the neuroinflammatory process in the cerebrospinal fluid (CSF) of people with MS. Clonotypic analysis indicates that OCBs are produced by plasmablasts and/or plasma cells that infiltrate the CNS from the periphery [4,5]. However, in addition to their role in antibody production, B-cells are likely to exert an effect on T-cells in the context of MS evolution. Specifically, B-cells are known to act as antigen-presenting cells, bearing molecules of the major histocompatibility complex (MHC) class II [6] and thus being capable of antigen internalization and subsequent participation in the APC-T-cell immunological synapse. B-cells were also shown to affect T-cell effector properties via the production of pro-inflammatory cytokines and further induction of pro-inflammatory effector phenotype in T-cells, thus contributing to MS pathogenesis [7]. Moreover, the identification of germinal center-like follicles in the leptomeninges of patients with secondary progressive MS (SPMS) shed new light on the concept of intrathecal B-cell maturation and its role in sustaining chronic inflammation in the progressive forms of MS [8]. Clonotypic homology across different counterparts, namely, blood, CSF and the CNS lesions, further supports the functional relevance of these clonotypes to pathogenic processes [9,10].

In MS, neuroinflammatory and neurodegenerative processes lie over a continuum, with the relative contribution of neurodegeneration increasing in the progressive forms of the disease [11]. We and others have previously described cognitive impairment, affect alterations and behavioral disturbance with deficient inhibitory control over the course of the disease [12][13][14]. Moreover, recent evidence indicates that the integration patterns of emotional stimuli into core executive functions, such as inhibitory control, may influence behavior in several neurodegenerative and psychiatric diseases [15]. The involvement of cortical and also subcortical areas, such as the amygdala, may, in fact, alter emotional perception, thus interfering with social cognition and functioning [16]. More specifically, the prefrontal cortex and the amygdala have been recognized to be among the anatomical brain regions implicated in human emotional learning and functional circuits, known to be impaired in MS and other neurodegenerative diseases [17]. The prefrontal cortex, a major cognitive structure, has been recognized to influence the activity of subcortical areas, such as the amygdala and hippocampus, thus providing a neurophysiological basis for neuro-cognitive emotional regulation [18]. Brain volume loss and the associated disturbance of neuronal circuits has been described in MS and other neurodegenerative diseases, and extensive research implicating precision diagnostics and machine-learning approaches in the management of the disease are currently under development [19]. These applications collectively aim to integrate genetic, clinical, radiological and molecular data in order to fingerprint the mechanisms of disease evolution and to identify diagnostic and/or prognostic biomarkers in relation to neuroinflammatory and/or neurodegenerative disease [20,21]. Vitamin D [22], molecular markers of oxidative stress [23] and markers indicative of systemic and cellular metabolic dysregulation [24][25][26], such as the tryptophankynurenine pathway, a metabolic pathway impaired in a wide range of neurodegenerative and autoimmune diseases, also implicated in the gut-brain axis homeostasis [27], are the object of extensive research.

Ocrelizumab is a B-cell-depleting anti-CD20 monoclonal antibody approved for the treatment of RRMS, as well as for active primary progressive MS (PPMS) [28]. In patients with relapsing MS, ocrelizumab was associated with improved outcomes of clinical and radiological activity, as well as with reduced rates of confirmed disability progression compared to patients receiving interferon β-1a [29]. In addition, in patients with PPMS, ocrelizumab was associated with lower rates of clinical and MRI progression, as compared with placebo [30] . Ocrelizumab targets primarily naïve and memory B-cells, whereas CD20-Cells 2022, 11, 1959 3 of 21 negative plasma cells escape the depletion, thus leaving the antibody production essentially unaffected. Upon repopulation, naïve and immature B-cells mainly re-emerge, together with a few memory B-cells and plasmablasts [31]. Evidence stemming from studies on ocrelizumab, as well as on previously available B-cell-depleting agents, such as rituximab, indicates that anti-CD20 targeting B-cell depletion also leads to elimination of a small population of CD20-bearing T-cells (CD20 dim T-cells) and to significant alterations in the T-cell effector phenotype and the cytokine production by T-cells [32][33][34]. In the clinical setting, however, the optimal treatment scheme, as well as predictors of poor response to anti-CD20 treatment for MS and other immune-mediated neurological diseases, remain to be defined. B-cell reconstitution profile may provide valuable information with respect to the optimal treatment response, but the available studies are limited [35][36][37][38][39][40][41].

The present study aims to assess the efficacy of ocrelizumab in a cohort of patients with active progressive MS and to dissect the clinical, radiological and laboratory attributes of treatment response.

2.1.Patients

Twenty-two patients with active primary progressive multiple sclerosis (PPMS) were included in this prospective, uncontrolled, open-label, observational study. All patients were followed by the Multiple Sclerosis Center of the 2nd Department of Neurology of the Aristotle University of Thessaloniki in AHEPA University General Hospital. Patients received ocrelizumab from February 2018 until August 2021, according to the European Medicines Agency (EMA) label. Upon ocrelizumab treatment initiation, all patients were over 18 years old. Patients who previously received immunosuppressants were excluded from the study. A minimum treatment duration with ocrelizumab for 24 months was necessary for inclusion in the analysis. Treatment with ocrelizumab was administered initially as two paired doses of 300 mg i.v. separated by a two-week interval, and cycles were repeated as single infusions of 600 mg i.v. every 6 months. The study was conducted in accordance with the Helsinki Declaration. All participants provided written informed consent. The study received the approval of the Bioethics' Committee of the School of Medicine of the Aristotle University of Thessaloniki (7/20.04.2021-7.382). Power analysis was conducted by the use of G*Power 3.1.9.7 for Windows. Analysis parameters for ANOVA-repeated measures, within-between interaction-were set as follows: effect size f = 0.25; α error probability = 0.05; power (1-β error probability) = 0.8; number of groups = 2, number of measurements = 4. Total sample size was calculated as N = 24 with actual power 0.82.

2.2.Study Procedures

Clinical and demographic characteristics of each patient were collected. Participants underwent neurological examination through the expanded disability status scale (EDSS) [42,43] by a certified EDSS rater. Cognitive assessment was performed with the Greek validated version of the Brief International Cognitive Assessment of Multiple Sclerosis (BICAMS) [12,44,45]. This battery included the Symbol Digit Modalities Test (SDMT) for the assessment of information processing speed [46], the Greek Verbal Learning Test (GVLT) [47,48] and the Brief Visuospatial Memory Test-Revised (BVMT-R) [49] for the assessment of verbal and visuospatial memory, respectively. The modified fatigue impact scale (MFIS) [50,51] was administered in order to quantify self-reported fatigue. All tests and measures were administered in the same order to all participants in a quiet room without distractions. The cognitive assessment was conducted at baseline and at 6-month intervals thereafter, using alternate forms. For assessment in relation to MRI measurements, only the time points corresponding to MRI assessment were taken into account, therefore, at baseline, at 12 and at 24 months.

MRI evaluation was performed prior to treatment onset (baseline) and annually thereafter. MRI studies were conducted in various facilities due to the fact that they were conducted as part of routine clinical practice, but they were performed with the same imaging protocol, in a 1.5T scanner. All patients received gadolinium at baseline, whereas gadolinium was not administered at follow-up MRI, as per recent MRI guidelines for the monitoring of people with MS [52]; therefore, the number of enhancing T1 lesions was not applicable as a measure in this study. Three-dimensional T1-weighted and T2-weighted FLAIR images with 1mm slice thickness were used in order to perform the analyses. First, all scans were evaluated for moving artifacts; then, we used the VolBrain TM platform [53] for the volumetric and lesion load analysis, as in Refs [54][55][56]. Lesion analysis with VolBrain TM was not conducted for cervical MRI, as this analysis is not available. However, cervical new T2 lesions evidenced by routine MRI evaluation were taken into account for the overall assessment of treatment response at 12 and at 24 months.

2.3.B-Cell Immunophenotype

B-cell populations were assessed by a highly sensitive 9-colour multiparametric flow cytometry (FC) analysis of peripheral blood (PB). As ocrelizumab is administered at treatment initiation as two equally divided doses, 15 days apart, and as a single infusion at 6-month intervals thereafter, baseline analysis included two time points. More specifically, analysis was performed immediately before treatment infusion, at pre-first baseline infusion (BLa), as well as 15 days afterward, at pre-second baseline infusion (BLb). The analysis was repeated in 6-month intervals thereafter, again, pre-infusion. Primary B-cell subsets were defined as follows: plasmablasts (CD19+(weak)/CD27++/CD38++), transitional B-cells (CD19+/CD38++/IgM++/24++), marginal-zone-like B-cells (CD19+/CD27+/IgD+), class-switched memory B-cells (CD19+/CD27+/IgM-/IgD-), non-switched CD27+ memory B-cells (CD19+/CD27+/IgM+/IgD-), naïve B-cells (CD19+/CD27-/IgD+). CD14+ and CD3+ cells were selectively gated out as contaminating myeloid and T lymphocytes in order to achieve a more accurate enumeration of B-cells and, subsequently, B-cell subpopulation immunophenotyping. For B-cell immunophenotype, samples were stained with IgD-FITC and IgM-PE Polyclonal Anti-Human Rabbit (Fab2) antibodies, as well as CD19-ECD clone J3-119, CD27-PC7 clone 1A4LDG5, CD24-APC clone ALB9, CD38-APC-A700 clone LS198-4-3, CD14-APC-A750 clone RMO52, CD3-PB clone UCHT1 and CD45-KrOr clone J33 antibodies, after bulk lysis. Samples were processed on a Navios cytometer (Beckman-Coulter) and analyzed with KALUZA software. Immunophenotype strategy was applied as in Refs [57,58]. A gating example of BLa and BLb time points is presented in Figure 1 . MRI evaluation was performed prior to treatment onset (baseline) and annually thereafter. MRI studies were conducted in various facilities due to the fact that they were conducted as part of routine clinical practice, but they were performed with the same imaging protocol, in a 1.5T scanner. All patients received gadolinium at baseline, whereas gadolinium was not administered at follow-up MRI, as per recent MRI guidelines for the monitoring of people with MS [52]; therefore, the number of enhancing T1 lesions was not applicable as a measure in this study. Three-dimensional T1-weighted and T2-weighted FLAIR images with 1mm slice thickness were used in order to perform the analyses. First, all scans were evaluated for moving artifacts; then, we used the VolBrain TM platform [53] for the volumetric and lesion load analysis, as in Refs [54][55][56]. Lesion analysis with VolBrain TM was not conducted for cervical MRI, as this analysis is not available. However, cervical new T2 lesions evidenced by routine MRI evaluation were taken into account for the overall assessment of treatment response at 12 and at 24 months.

2.4.Serum Cytokine Profile

Serum cytokine analysis (IL-6; INFγ; TNFα; IL-1β; IL-12; IL-2; IL-4; IL-5; IL-10; IL-17A) was performed with flow cytometry, with the use of a multiplexed bead-based immunoassay panel (AimPlex Biosciences, Inc., Pomona, CA, USA) [59, 60] . Samples were acquired in a NAVIOS Beckman Coulter Three Laser 10-color cytometer and analyzed with FlowJo™ Software (BD Biosciences, Becton, Dickinson and Company, NJ, USA). The primary efficacy outcome was the proportion of patients with optimal response at 24 months, hereafter mentioned as "responders". Optimal response was defined as the proportion of patients free of relapse, free of confirmed disability accumulation (CDA) evidenced by EDSS evaluation and free of new and/or enlarging T2 lesions on the brain and cervical MRI. Of note, all patients during the study remained free of relapse in the context of PPMS. In this respect, CDA occurred in the absence of relapses, thus essentially corresponding to confirmed disability progression (CDP) by EDSS evaluation [61]. Although relapses are not typical for patients with PPMS, nevertheless, as per the 2013 revised classification of the types of MS [62], a patient with progressive disease, including primary progressive disease, may present as "active with progression" or "active without progression", whereby activity is determined by clinical relapses assessed at least annually, and/or MRI activity is typically not completely excluded. Due to this fact, we retained the assessment of possible relapses in the definition of treatment response in the present study. Patients with evidence of CDA and/ and/or new/enlarging T2 lesions on the brain and cervical MRI are hereafter collectively mentioned as "non-responders".

The proportion of patients with optimal response at 12 months. Optimal response is defined as absence of relapses, absence of new/enlarged T2 lesions on the brain and cervical MRI and absence of CDA, defined as 1 point of EDSS increase (0.5 point if baseline EDSS ≥ 5.5, confirmed after 6 months from the previous last evaluation) at 12 and at 24 months.

The mean time to optimal response for patients who exhibited optimal response at 24 months. As per study design, patients were evaluated every 6 months.

The proportion of patients with CDA defined as 1 point of EDSS increase (0.5 point if baseline EDSS ≥ 5.5), at 12 and at 24 months, relative to the last previous evaluation time point.

The proportion of patients with MRI activity at 12 and 24 months (defined as the presence of new/enlarged T2 lesions with respect to previous brain MRI.

2.5.3.Exploratory Outcomes

• Mean change in EDSS from baseline at 12-and 24-month point estimates (for all patients and for responders vs. non-responders at 24 months).

Mean change in total number of new and/or enlarged T2 lesions at 12-and 24-month point estimates (for all patients and for responders vs. non-responders at 24 months).

Mean change in cognitive performance scales' scores, namely, BICAMS (including SDMT, GVLT, BVMT-R), for the assessment of processing speed, verbal and visuospatial memory, respectively, from baseline at 12-and 24-month point estimates (for all patients and for responders vs. non-responders at 24 months).

Mean change in volumetric parameters in cm 3 and in percentage of total brain volume from baseline at 12-and 24-month point estimates (for all patients and for responders vs. non-responders at 24 months). Example measurements include, but are not restricted to, overall, cerebrum and cerebellar white and gray matter, brainstem and areas of subcortical gray matter.

Mean change in lesion (white matter) analysis parameters, namely, lesion count, lesion volume in cm 3 and normalized lesion volume, as well as overall lesion burden, from baseline at 12-and 24-month point estimates (for all patients and for responders vs. non-responders at 24 months).

Possible association between mean EDSS score and/or mean cognitive scores and MRI volumetry and/or lesion analysis at 12-and 24-month point estimates.

Mean change in B-cell subtypes from baseline at 6-, 12-, 18-and 24-month point estimates (for all patients and for responders vs. non-responders at 24 months).

Mean change in serum cytokine profile from baseline at 6-and 12-month point estimates (for all patients and for responders vs. non-responders at 24 months).

2.6.Statistical Analysis

Normality was assessed by use of the Kolmogorov-Smirnoff test. At point estimates, the non-parametric Mann-Whitney U-test and Chi-square test were used for the comparison of continuous variables (and the mean change from baseline for continuous variables) and dichotomous/categorical variables, respectively. For evaluation of the effect of time on mean EDSS, the mean number of new/enlarged T2 lesions and mean scores for cognitive tests at 12 and at 24 months, as well as MRI volumetry and lesion analysis parameters compared to baseline, general linear models in a repeated-measures setting were applied, according to which, gender, age (years), MS duration (years), EDSS at baseline and degree of brain MRI activity at baseline (number of new/enlarging T2 and Gd+ lesions) were used as covariates, and group allocation based on the response at 24 months was used as a betweensubjects factor. Similarly, for the comparison of mean change from baseline at 12-and 24-month point estimates between responders and non-responders, a repeated-measures general linear model was implemented.

Possible association between mean EDSS score and/or mean cognitive scores and volumetry and/or lesion analysis were evaluated with the use of hierarchical linear models (HLM) with repeated-measures data. For this reason, the mixed procedure was implemented, with each set of repeated measures of EDSS and/or cognitive score defined as a dependent variable, case ID defined as a factor, and time, as well as each set of the respective repeated measures of volumetry or lesion analysis, defined as covariates. Covariates were set as fixed effects, whereas time was set as a random effect. For each model, the -2 restricted log likelihood, as well as the estimates and the significance of fixed effects were assessed. The analysis was conducted with the use of SPSS 27.0 (IBM Corp, Armonk, NY, USA). A significance level of 0.05 was taken into account.

3.1.Baseline Characteristics, Safety and Treatment Withdrawal

Patients' baseline characteristics are presented in Table 1 . In total, 12 male and 10 female patients with PPMS were included in the study, of mean age 48.5 ± 1.69 years. Mean disease duration was 9.05 ± 0.92 years, whereas time from diagnosis was 5.59 ± 0.84 years. Mean EDSS was 4.91 ± 0.3. Overall, six patients exhibited cardiovascular comorbidities, whereas nine patients exhibited other (non-cardiovascular) comorbidities, with affective disorder being the most prevalent. Retrospectively, responders at 24 months vs. non-responders did not differ in terms of baseline characteristics.

Seven patients presented with adverse events during ocrelizumab treatment. L5 radiculopathy, meningioma, urinary tract infection, deep venous thrombosis, cholecystitis (surgery-laparoscopic cholecystectomy) and migraine crisis presented in separate patients. These AEs were resolved and were not linked to ocrelizumab discontinuation. In addition, two patients, one concurrently with meningioma and one concurrently with cholecystitis, presented with serious dermatitis. Dermatitis in these two patients led to ocrelizumab discontinuation; however, the AE occurred at the end of the follow-up period of 24 months.

3.2.Primary and Secondary Outcomes

Primary and secondary outcomes are presented in Table 2 . In total, 14 (63.6%) patients were classified as responders at 12 months, whereas 13 patients (59.1%) were classified as responders at 24 months. At 12 months, 14 (63.6%) patients remained free of CDA, and 20 (90.9%) patients were free of new/enlarged T2 lesions on the brain and cervical MRI. At 24 months, 13 patients (59.1%) remained free of CDA, whereas 20 (90.9%) patients were free of new/enlarged T2 lesions on the brain and cervical MRI. The mean time to optimal response (calculated for responders at 24 months) was 8.73 ± 1.41 months.

3.3.Exploratory Outcomes

For all patients, mean EDSS was 5.27 ± 0.28 and 5.57 ± 0.28 at 12 and at 24 months, respectively. For responders at 24 months, mean EDSS was 5.15 ± 0.36 and 5.15 ± 0.35 at 12 and at 24 months, respectively. For non-responders at 24 months, mean EDSS was 5.44 ± 0.47 and 6.17 ± 0.41 at 12 and at 24 months, respectively. Mean EDSS did not differ at point estimates between the two groups, although a tendency toward a difference at 24 months was evident (p = 0.695 and p = 0.096 for 12 and 24 months, respectively). However, mean EDSS change from baseline was different between the two groups at the 12-month point estimate (mean EDSS change of 0.00 ± 0.1 for responders at 24 months vs. 0.89 ± 0.3 for non-responders at 24 months, p = 0.003), as well as the 24-month point estimate (mean EDSS change of 0.00 ± 0.06 for responders at 24 months vs. 1.61 ± 0.27 for non-responders at 24 months, p < 0.001). The mean EDSS change between 12 and 24 months was 0.00 ± 0.06 for responders at 24 months vs. 0.72 ± 0.09 for non-responders at 24 months (p < 0.001) (Figure 2A ). Overall, time did not have a significant effect on EDSS, evaluated with a general linear model in a repeated-measures setting (F = 0.821, p = 0.399). However, an effect of time on EDSS was evident when group allocation based on response at 24 months was taken into account in the model as a between-subjects factor (F = 3.922, p = 0.041), and the response-at-24-months group allocation exhibited a prominent, significant effect of EDSS change over time (F = 27.268, p < 0.001).

± 0.47 and 6.17 ± 0.41 at 12 and at 24 months, respectively. Mean EDSS did not differ at point estimates between the two groups, although a tendency toward a difference at 24 months was evident (p = 0.695 and p = 0.096 for 12 and 24 months, respectively). However, mean EDSS change from baseline was different between the two groups at the 12-month point estimate (mean EDSS change of 0.00 ± 0.1 for responders at 24 months vs. 0.89 ± 0.3 for non-responders at 24 months, p = 0.003), as well as the 24-month point estimate (mean EDSS change of 0.00 ± 0.06 for responders at 24 months vs. 1.61 ± 0.27 for non-responders at 24 months, p < 0.001). The mean EDSS change between 12 and 24 months was 0.00 ± 0.06 for responders at 24 months vs. 0.72 ± 0.09 for non-responders at 24 months (p < 0.001) (Figure 2A ). Overall, time did not have a significant effect on EDSS, evaluated with a general linear model in a repeated-measures setting (F = 0.821, p = 0.399). However, an effect of time on EDSS was evident when group allocation based on response at 24 months was taken into account in the model as a between-subjects factor (F = 3.922, p = 0.041), and the response-at-24-months group allocation exhibited a prominent, significant effect of EDSS change over time (F = 27.268, p < 0.001). For all patients, the mean new/enlarged T2 lesions were 0.23 ± 0.16 and 0.23 ± 0.16 at 12 and at 24 months, respectively. For responders at 24 months, the mean new/enlarged T2 lesions were 0.15 ± 0.15 at 12 and 0 at 24 months. For non-responders at 24 months, the mean new/enlarged T2 lesions were 0.33 ± 0.33 and 0.56 ± 0.38 at 12 and at 24 months, respectively. The mean number of new/enlarged T2 lesions did not differ at 12 and at 24 months between the two groups (p = 0.896 and p = 0.393, respectively). Moreover, the mean change in new/enlarged T2 lesions from baseline did not differ between the two groups at the 12-month point estimate (mean change in new/enlarged T2 lesions -1 ± 0.42 for responders at 24 months vs. -0.56 ± 0.58 for non-responders at 24 months, p = 0.601), as well as at the 24-month point estimate (mean change in new/enlarged T2 lesions -1.15 ± 0.36 for responders at 24 months vs. -0.33 ± 0.37 for non-responders at 24 months, p = 0.144). The mean change in new/enlarged T2 lesions between 12 and 24 months was -0.15 At baseline, EDSS was significantly correlated with normalized brainstem volume (Pearson's r: -0.466, p = 0.038), with absolute (cm 3 ) and with normalized thalamus volume (Pearson's r: -0.524, p = 0.018 and Pearson's r: -0.534, p = 0.015, respectively) and exhibited a tendency to correlate with the overall white matter lesion burden (Pearson's r: 0.382, p = 0.096). Similarly, fatigue was correlated at baseline with absolute (cm 3 ) and with normalized thalamus volume (Pearson's r: -0.435, p = 0.055 and Pearson's r: -0.541, p = 0.014, respectively), as well as with normalized cerebellum volume (Pearson's r: -0.507, p = 0.023) and exhibited a tendency to correlate with absolute and with normalized lesion volume (Pearson's r: 0.431, p = 0.058 and Pearson's r: 0.396, p = 0.075, respectively).

For all patients, the mean new/enlarged T2 lesions were 0.23 ± 0.16 and 0.23 ± 0.16 at 12 and at 24 months, respectively. For responders at 24 months, the mean new/enlarged T2 lesions were 0.15 ± 0.15 at 12 and 0 at 24 months. For non-responders at 24 months, the mean new/enlarged T2 lesions were 0.33 ± 0.33 and 0.56 ± 0.38 at 12 and at 24 months, respectively. The mean number of new/enlarged T2 lesions did not differ at 12 and at 24 months between the two groups (p = 0.896 and p = 0.393, respectively). Moreover, the mean change in new/enlarged T2 lesions from baseline did not differ between the two groups at the 12-month point estimate (mean change in new/enlarged T2 lesions -1 ± 0.42 for responders at 24 months vs. -0.56 ± 0.58 for non-responders at 24 months, p = 0.601), as well as at the 24-month point estimate (mean change in new/enlarged T2 lesions -1.15 ± 0.36 for responders at 24 months vs. -0.33 ± 0.37 for non-responders at 24 months, p = 0.144). The mean change in new/enlarged T2 lesions between 12 and 24 months was -0.15 ± 0.15 for responders at 24 months vs. 0.22 ± 0.55 for non-responders at 24 months, p = 0.556) (Figure 2B ). Time did not have a significant effect on mean new/enlarged T2 lesions, evaluated with a general linear model in a repeated-measures setting (F = 0.324, p = 0.725). In this model, the baseline MRI activity, defined as new/enlarged T2 and Gd(+) lesions on the brain and cervical MRI, exhibited a significant effect on the mean change of new/enlarged T2 lesions over time (F = 17.224, p < 0.001). Similarly, time did not have a significant effect on the mean new/enlarged T2 lesions when group allocation based on Cells 2022, 11, 1959 9 of 21 response at 24 months was taken into account in the model as a between-subjects factor (F = 0.501, p = 0.61). In this model, the baseline MRI activity, defined as new/enlarged T2 and Gd(+) lesions on the brain and cervical MRI, exhibited a significant effect on the mean change of new/enlarged T2 lesions over time (F = 16.877, p < 0.001).

Cognitive function

The mean scores in cognitive testing and self-reported fatigue for baseline, as well as for 12 and 24 months, are exhibited in Table 3 . The mean values for SDMT, GVLT, BVMT-R and MFIS did not differ at baseline between responders at 24 months vs. non-responders at 24 months. A tendency toward increased mean SDMT values at baseline, at 12 and at 24 months, was evident for responders vs. non-responders (47.11 ± 3.21 vs. 38.62 ± 3.14, p = 0.096 at baseline; 45 ± 3.37 vs. 36.38 ± 2.6, p = 0.06 at 12 months and 46.67 ± 3.18 vs. 38.08 ± 2.79, p = 0.071 at 24 months). At point estimates, the mean change from baseline was comparable for SDMT, GVLT, BVMT-R and MFIS at 12 and at 24 months. Time did not have a significant effect on the mean SDMT scores evaluated with a general linear model in a repeated-measures setting, neither when all patients were considered together (F = 0.426, p = 0.538) nor when group allocation based on response at 24 months was taken into account in the model as a between-subjects factor (F = 0.401, p = 0.598). Similar trends were identified for the mean GVLT scores (F = 0.556, p = 0.525 and F = 0.539, p = 0.534, respectively), mean BVMT-R scores (F = 0.525, p = 0.596 and F = 0.486, p = 0.62, respectively) and mean MFIS scores (F = 2.119, p = 0.137 and F = 2.056, p = 0.146, respectively).

MRI volumetry-association with cognitive performance MRI volumetric and lesion load analysis at baseline, at 12 and at 24 months, for all patients, as well as for responders at 24 months vs. non-responders at 24 months, are presented in Supplementary Table S1 . For two patients, one responder and one nonresponder at 24 months, MRI analysis was not performed due to moving artifacts. At point estimates, the mean MRI volumetry and lesion analysis parameters did not differ between responders at 24 months vs. non-responders at 24 months, with the exception of cerebellar volumetric measurements (Supplementary Table S1 ). Although the mean % change in cerebellar volume and gray matter cerebellar volume at point estimates did not differ between responders and non-responders at 24 months, (Supplementary Table S2 and Figure 3 ), time, overall, exhibited a tendency toward a significant effect on the mean cerebellar volume, both for absolute and normalized values, evaluated with a general linear model in a repeated-measures setting for all patients (F = 3.684, p = 0.065 and F = 3.372, p = 0.083 for absolute and for normalized values, respectively), as well as when group allocation based on response at 24 months was taken into account in the model as a between-subjects factor (F = 3.729, p = 0.065 and F = 3.285, p = 0.088 for absolute and for normalized values, respectively). This effect was more prominent for mean gray matter cerebellar volume for all patients (F = 4.342, p = 0.23 and F = 4.279, p = 0.024 for absolute and for normalized values, respectively), as well as when group allocation based on response at 24 months was taken into account in the model as a between-subjects factor (F = 5.082, p = 0.014 and F = 4.862, p = 0.016 for absolute and for normalized values, respectively) (Supplementary Table S1 ). In the overall cohort, hierarchical linear models (HLM) with repeated-measures data in a mixed setting elucidated several volumetry and lesion analysis parameters that exhibited an association of variability with cognitive and EDSS scores' variability over time (Supplementary Table S3 ). Overall, lateral ventricles' volume variability exhibited an association with the variability of SDMT, GVLT, BVLT-R and MFIS scores over time. Moreover, lesion volumetry parameters differentially exhibited association with SDMT, GVLT, BVLT-R and/or MFIS scores. However, a single lesion volumetry parameter universally exhibiting an association with cognitive scores was not identified. Cerebellum and cere- In the overall cohort, hierarchical linear models (HLM) with repeated-measures data in a mixed setting elucidated several volumetry and lesion analysis parameters that exhibited an association of variability with cognitive and EDSS scores' variability over time (Supplementary Table S3 ). Overall, lateral ventricles' volume variability exhibited an asso-ciation with the variability of SDMT, GVLT, BVLT-R and MFIS scores over time. Moreover, lesion volumetry parameters differentially exhibited association with SDMT, GVLT, BVLT-R and/or MFIS scores. However, a single lesion volumetry parameter universally exhibiting an association with cognitive scores was not identified. Cerebellum and cerebellum gray matter volumetric parameters exhibited an association with SDMT scores. Thalamus volumetric parameters exhibited an association of variability with GVLT and BVLT-R scores over time.

When taking into account clinically meaningful change in SDMT as a ≥4-point increase (improvement) or a ≥4-point reduction (deterioration), there was no difference in the frequency of patients who exhibited either improved or deteriorated SDMT scores between responders vs. non-responders at 12 months (SDMT improved vs. non-improved 2/11 for responders vs. 1/8 for non-responders, p = 0.774 at 12 months; SDMT improved vs. non-improved 4/9 for responders vs. 2/7 for non-responders, p = 0.658 at 24 months; SDMT deteriorated vs. non-deteriorated 6/7 for responders vs. 2/7 for non-responders, p = 0.251 at 12 months; SDMT deteriorated vs. non-deteriorated 5/8 for responders vs. 2/7 for nonresponders, p = 0.421 at 24 months). Cerebellar volume loss at 12 months was greater for patients with clinically meaningful deterioration in SDMT at 24 months, both with respect to absolute volume loss (SDMT-deteriorated vs. SDMT non-deteriorated -8.55 ± 2.87 vs.

-1.2 ± 0.99, p = 0.05) and (%) volume loss (SDMT-deteriorated vs. SDMT non-deteriorated -6.52 ± 2.39 vs. -0.93 ± 0.8, p = 0.01). Similarly, for the gray matter cerebellar volume at 12 months, there was a tendency toward greater loss for patients with clinically meaningful deterioration in SDMT at 24 months, both with respect to absolute volume loss (SDMTdeteriorated vs. SDMT non-deteriorated -10.29 ± 6.07 vs. -0.92 ± 1.73, p = 0.06) and normalized volume loss (SDMT-deteriorated vs. SDMT non-deteriorated -9.06 ± 4.29 vs. -0.88 ± 1.83, p = 0.052). Moreover, when clinically meaningful SDMT deterioration at 12 months was taken into account, cerebellar gray matter volume loss at 12 months was greater for patients with clinically meaningful deterioration in SDMT at 12 months, both with respect to absolute volume loss (SDMT-deteriorated vs. SDMT non-deteriorated -10.79 ± 5.88 vs. -0.7 ± 1.74, p = 0.041) and normalized volume loss (SDMT-deteriorated vs. SDMT non-deteriorated -9.14 ± 3.88 vs. -0.84 ± 1.97, p = 0.048). No difference with respect to change in volumetric parameters was evident between patients with and without clinically meaningful SDMT improvement at 12 and at 24 months (data not shown).

Immune cell phenotype B-cell immunophenotype is exhibited in Figure 4 and in Supplementary Table S4 . Overall, ocrelizumab infusion resulted in a striking peripheral blood (PB) reduction in the mean absolute numbers and relative % frequency of CD19+ cells, as well as in the relative % frequency of transitional B-cells, marginal-zone-like B-cells, class-switched memory B-cells and naïve B-cells. Total white blood cells, CD3+ cells and neutrophils remained largely unaffected. An increase in the relative % frequency of plasmablasts was evident over time. Transitional B-cells, class-switched B-cells and naïve B-cells re-emerged in subsequent follow-up evaluations, as was also evident for CD19+ cells. At point estimates, white blood cells, lymphocytes, CD19+ cells (absolute numbers and relative % frequency of lymphocytes), transitional B-cells, marginal-zone-like B-cells, class-switched memory B-cells, non-switched memory B-cells, naïve B-cells, as well as CD3+ cells and neutrophils did not differ between responders at 24 months vs. non-responders at 24 months. However, responders at 24 months exhibited significantly reduced plasmablasts ((%) of CD19+ cells) compared to non-responders at 24 months, at the 6-month point estimate (7.69 ± 4.4 vs. 22.66 ± 7.19, respectively, p = 0.043) and a similar tendency at the 12-month point estimate (17.05 ± 6.46 vs. 39.54 ± 10.62, respectively, p = 0.076). The emergence of plasmablasts in the non-responder population appeared to be independent of the prior-to-treatment lymphocyte cell counts and the effectiveness of lymphocyte depletion, as evidenced by increased PB absolute lymphocyte cell counts (cells/µL) in the responders before treatment initiation compared to the non-responders (2065.38 ± 99.12 vs. 1647.78 ± 110.25, respectively, p = 0.011) and by a tendency toward increased absolute lymphocyte cell counts (cells/µL) in responders at the 6-month point estimate compared to non-responders (1565.38 ± 104.65 vs. 1316.67 ± 129.91, respectively, p = 0.071).

Serum cytokine analysis

Overall, responders at 24 months did not exhibit differences in their serum cytokine levels and/or % mean change from baseline in their cytokine levels at point estimates when compared to non-responders at 24 months (Figure 5 and Supplementary Table S5 ). An exception to this finding was IL-6 levels, for which a tendency toward a difference in

Overall, responders at 24 months did not exhibit differences in their serum cytokine levels and/or % mean change from baseline in their cytokine levels at point estimates when compared to non-responders at 24 months (Figure 5 and Supplementary Table S5 ). An exception to this finding was IL-6 levels, for which a tendency toward a difference in the % mean change from baseline was evident at the 12-month point estimate between responders at 24 months vs. non-responders at 24 months. Similarly, time exhibited a tendency toward a significant effect on mean IL-6 scores evaluated with a general linear model in a repeatedmeasures setting when group allocation based on response at 24 months was taken into account in the model as a between-subjects factor (F = 2.868, p = 0.072). More specifically, non-responders at 24 months exhibited IL-6 reduction at 6-and 12-month point estimates compared to baseline, whereas for responders at 24 months, IL-6 levels were comparable across the time points (Figure 5 and Supplementary Table S5 ). the % mean change from baseline was evident at the 12-month point estimate between responders at 24 months vs. non-responders at 24 months. Similarly, time exhibited a tendency toward a significant effect on mean IL-6 scores evaluated with a general linear model in a repeated-measures setting when group allocation based on response at 24 months was taken into account in the model as a between-subjects factor (F = 2.868, p = 0.072). More specifically, non-responders at 24 months exhibited IL-6 reduction at 6-and 12-month point estimates compared to baseline, whereas for responders at 24 months, IL-6 levels were comparable across the time points (Figure 5 and Supplementary Table S5 ).

4.Discussion

Ocrelizumab has been reported to exhibit beneficial effect not only in patients with RRMS but also in patients with active PPMS [29, 30] , thus becoming the first DMT available for the latter form of the disease. Anti-CD20 treatment evidently eliminates B-cells acting as antigen-presenting cells [63]. Moreover, as B-cells produce pro-and anti-inflammatory cytokines, anti-CD20 treatment is expected to lead into alterations in the serum cytokine profile, thus interfering with T-cell activation status and ameliorating disease-mediating T-cell responses [63,64]. Notably, ocrelizumab-induced clinical benefit seems to be more prominent for patients with CNS inflammatory lesions, as indicated by the presence of MRI activity. In this respect, the individual response to ocrelizumab remains to be fully characterized, thus underlining the need for the identification of clinically relevant immunological markers. In the present study, we aimed to assess the efficacy of ocrelizumab in a cohort of patients with active progressive MS and to explore differences with respect to clinical, radiological and laboratory parameters in patients with optimal vs. sub-optimal treatment response.

In our study, baseline EDSS and self-reported fatigue, as measured with MFIS, were significantly correlated with volumetry parameters of the brainstem, thalamus and the cerebellum, whereas they exhibited a tendency to correlate with overall lesion burden parameters. Time, overall, exhibited a tendency toward a significant effect on mean cerebellar volume for all patients, as well as in relation to the group allocation based on response at 24 months. This effect was more prominent for mean gray matter cerebellar volume for all patients, as well as when group allocation based on response at 24 months was taken into account. More specifically, responders at 24 months maintained lower cerebellar volume loss and lower gray matter cerebellar volume loss over time compared to non-responders at 24 months. This result is in accordance with recently announced data on the effect of ocrelizumab treatment on cerebellar volume loss based on a sub-analysis of the ORATORIO study [65]. With regard to cognitive performance, no clinically significant difference was observed in the performance between the two patient groups. However, in all patients, a decrease in the performance in processing speed, as measured using SDMT, was correlated with greater cerebellar volume loss. The relation of SDMT performance with cerebellar pathology in MS has been demonstrated in multiple studies [66][67][68][69]. Cerebellum is interconnected with many cortical areas involved in cognition, such as the prefrontal cortex and the lateral parietal cortex [70,71]. It seems that damage in the cerebellum may lead to dysfunction of circuits involved in attention, processing speed and executive functions, among others [72,73].

The proportion of MRI efficacy, defined as absence of new/enlarged T2 lesions on the brain and cervical MRI, was >90% in 12-and 24-month point estimates for the overall cohort, thus referring to both responders and non-responders. Of note, the number of new/enlarged T2 lesions was indicated upon routine MRI evaluation, relative to the last previous MRI, and independently confirmed by the treating neurologists of the study group. However, as indicated by the lesion volume measurements derived from volumetry analysis, patients defined as non-responders exhibited an overall increase in the mean lesion volume measurements, whereas patients defined as responders exhibited an overall decrease in the mean lesion volume measurements. Although the repeated-measures general linear model mean comparison between responders and non-responders for these measurements did not reach statistical significance, we believe that this discrepancy between the routine MRI evaluation and the lesion volumetry measurements underlines the need for more thorough MRI evaluation, even with the integration of volumetric and lesion-volumetric analyses in the disease management algorithms, an especially prominent need in progressive MS (reviewed in Ref [74]). Validation and refinement of the MRI volumetry protocols in order to better adhere to clinical decision making is necessary and requires large-cohort prospective studies.

As expected, ocrelizumab infusion resulted in a striking reduction in most PB B-cell subtypes, whereas white blood cells, CD3+ cells and neutrophils remained largely unaf-fected. Additionally, consistent with existing studies, an increase in the relative % frequency of plasmablasts was evident over time [32,33]. Most B-cell subtypes at point estimates did not differ between responders at 24 months vs. non-responders at 24 months in absolute and proportional values. However, responders at 24 months exhibited significantly reduced PB plasmablasts ((%) of CD19+ cells) compared to non-responders at 24 months at the 6-month point estimate and a similar tendency at the 12-month point estimate. Increased plasmablasts both at baseline and during treatment may indicate sub-optimal response to anti-CD20 treatment in rheumatoid arthritis (RA) [57,75] and in systemic lupus erythematosus (SLE) [76]. To our knowledge, reduced plasmablast depletion efficacy has been linked for the first time to sub-optimal response to anti-CD20 treatment in active progressive MS. This finding needs to be further validated in a large-cohort study. Of note, insufficient memory B-cell depletion has been linked to poor treatment outcomes in patients with neuromyelitis optica (NMO) under rituximab [77].

In systemic rheumatoid disease, increased serum IL-6 levels prior to treatment onset have been linked to poor treatment response, and the persistence of IL-6 levels during treatment has been advocated as a biomarker of resistance to treatment [78]. Of note, the effect of IL-6 on MS is poorly characterized, and existing evidence indicates that IL-6 may be both harmful and beneficiary for MS. IL-6, together with TGFβ, is required for Th17-T-effector phenotype induction, and IL-6 may attenuate the production of T-regulatory cells [79]. Although IL-6 appears to promote EAE pathology in an experimental setting, this finding is poorly translated in MS, as IL-6 may also exhibit immunosuppressive properties [80,81]. In fact, existing evidence indicates that administration of IL-6 blocking pharmaceutical agents in the context of rheumatoid disease may precipitate CNS demyelination [82, 83] . In the present study, ocrelizumab response was linked neither to low IL-6 serum levels at baseline nor to IL-6 level reduction during treatment compared to baseline values, thus underlying the assumption that the effect of anti-CD20 cell depletion in MS may be independent of IL-6 regulation.

Limitations and Future Directions

Our study is subjected to limitations. First, low participant numbers may have accounted for insufficient statistical power to elucidate differences in the clinical outcomes and the laboratory findings between responders vs. non-responders. This is an inherent limitation of a real-world one-center study. Of note, the results elucidated in the present study need to be validated in a larger cohort.

Second, T-cell immunophenotype analysis was not conducted in the frame of the present study. T-cells are the main immune cellular elements that confer damage in the target organ, namely, the CNS, in MS. It has therefore been advocated that B-cell depletion treatment may induce beneficial effect in MS clinical outcomes via eliminating the B-cell compartment that essentially primes antigen presentation and the subsequent T-cell clonal expansion and T-effector lineage commitment in response to antigen-specific stimuli. In this respect, a study of T-cell immunophenotype alterations may accurately dissect the mechanisms by which B-cell depletion is beneficiary in a subset of patients with MS, otherwise traditionally regarded as a T-cell-mediated disease. Moreover, anti-CD20 pharmacological agents have been shown to eliminate not only CD20-bearing B-cells but also specific CD3+CD20 dim , CD8+CD20+ and other T-cell subsets [32][33][34]84] with an evidently pathogenic potential in the context of MS.

Third, regarding the volumetry analysis followed and the possible associations with cognitive measurements, it should be noted that the VolBrain algorithm provides a report value for cerebrum gray matter volume without explicitly defining cortical gray matter volume. A reduction in the rate of global and regional brain atrophy has been linked to response to ocrelizumab [74]. In this respect, a more thorough investigation and volumetry analysis protocol by a specialized neuro-radiologist, according to the SIENA protocol, would be beneficial in the frame of the present study in order to fulfill the requirement for a cortical gray matter analysis. In line with this fact, the possibility of an association between cortical gray matter changes and the changes in the cognition cannot be fully assessed. We consider this an inherent limitation of our study on the basis of the volumetry analysis tool that was used.

Of note, we considered as responders to treatment those patients who did not present progression of disability under ocrelizumab, thus signifying optimal response in the context of active progressive MS. However, for active progressive MS, a well-defined, universally applied consensus for treatment response remains to be described. In this respect, one may also consider the reduction rate of cumulative disability as an approach to assess treatment response, taking into account the fact that plateaus in the disease progression may occur during the natural course of the disease [62]. The clinically meaningful threshold for the reduction in the disability progression rate, regarded as treatment response in the context of active progressive MS, should be explored in future studies that also take into consideration the rate of disability progression before treatment initiation. Other structural biomarkers, such as the rate of retinal inner nuclear layer thinning, assessed with optical coherence tomography (OCT) techniques, are also under evaluation and may collectively contribute to an effective assessment of the neurodegenerative component in progressive MS [85].

Moreover, recent evidence regarding the effects of ocrelizumab on fluid biomarkers suggests that serum neurofilament light chain levels are also reduced as a result of the treatment, an observation with implication for a possible treatment effect on axonal damage and loss in the frame of progressive MS [74]. Additional serum biomarkers currently under study may contribute to the development and validation of a consensus guidance regarding the assessment of treatment efficacy and the optimal treatment administration frequency in patients with aPPMS under ocrelizumab.

5.Conclusions

In the present study, the efficacy of ocrelizumab in a cohort of patients with active progressive MS was assessed, and clinical, radiological and laboratory attributes were linked to treatment response. With respect to radiological biomarkers, the response to ocrelizumab was linked to lower cerebellar volume loss and lower gray matter cerebellar volume loss over time. Moreover, to our knowledge, reduced plasmablast depletion efficacy was linked for the first time to sub-optimal response to anti-CD20 treatment in active progressive MS, and this effect may be independent of IL-6 regulation. Further studies are necessary in order to enhance existing knowledge regarding the biological implications and the clinical outcomes that are associated with the effect of ocrelizumab on the immunological profile in the context of active PPMS.

Supplementary Materials:

The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cells11121959/s1, Table S1 : MRI volumetry and lesion analysis parameters at baseline, at 12 and at 24 months for all patients and for responders at 24 months vs. non-responders at 24 months; Table S2 : % mean reduction in cerebellar volume and in gray matter cerebellar volume from baseline at 12 and at 24 months for responders at 24 months vs. non-responders at 24 months; Table S3 : Association of volumetry and lesion analysis parameters' variability with cognitive and EDSS scores' variability over time (only associations with p values < 0.1 are presented); Table S4 : Immune cell phenotype assessed at baseline pre-first infusion (BLa) and 15 days after treatment with the first infusion and pre-second infusion (BLb), as well as in 6-month intervals thereafter, prior to the next scheduled infusion of ocrelizumab and % change from baseline for 6-month interval point estimates for all patients and non-responders at 24 months vs. responders at 24 months; Table S5 : Cytokine levels assessed at baseline pre-first infusion (BL), as well as in 6-and 12-month time points, prior to the next scheduled infusion of ocrelizumab and % mean cytokine change from baseline for 6-and 12-month time points for all patients and non-responders at 24 months vs. responders at 24 months. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

availability

Data Availability Statement: Anonymized data not published within this article will be made available on request from any qualified investigator. The principal author has full access to the data used in the analyses in the manuscript. The principal author takes full responsibility for the data, the analyses and interpretation, and the conduct of the research; they have full access to all of the data; they have the right to publish any and all data, separate and apart from the guidance of any sponsor.

annex

Conflicts of Interest: M.B.: Travel support and/or research grants and/or lecture fees and/or advisory services from The Hellenic Foundation for Research and Innovation (H.F.R.I.), the Ministry of Education's Education and Lifelong Learning Program, the Hellenic Neurological Society, the Hellenic Academy of Neuroimmunology, Biogen Idec, Novartis, TEVA, Bayer, Genesis Pharma, Sanofi, Specifar, Roche and Merck. C.B.: Travel support and/or research grants and/or lecture fees and/or advisory services from Novartis, Bayer, Merck, Genesis, Sanofi, Specifar, Roche, Biogen, Mylan. I.N.: conference fees and travel sponsorship from Bayer, Specifar-TEVA, Novartis, Sanofi-Genzyme, Roche, Mylan. Speaker honoraria: Merck, Sanofi-Genzyme, specifar-TEVA, Genesis Pharma, Novartis. Honoraria for participation in advisory boards: Sanofi-Genzyme, Specifar-TEVA, Roche. N.G.: Travel support and/or research grants and/or lecture fees and/or advisory services from Novartis, Bayer, Merck, Genesis, Sanofi, Specifar, Roche, Biogen, TEVA, Mylan. S.-A.S., E.K., E.G., A.I., V.T., L.S., A.F., V.N., M.S., V.G., E.K. and P.T.: no disclosures. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Acknowledgements

Acknowledgments:We would like to acknowledge Eleni Grigoriadou for the administrative management of the study.

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