Elicit: B-cell Biomarkers and Ocrelizumab Response

Skip to main content

B-cell Biomarkers and Ocrelizumab Response

Research reportView only

Create alertChat

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

Press enter or space to select a node.You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel.

Press enter or space to select an edge. You can then press delete to remove it or escape to cancel.

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

toof

Pageof

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

Download BIBDownload RISDownload TXT

Report

Status

Gather sources

200 sources found

Details

Screen sources

10 sources included

Details

Extract data

50 data points extracted

Details

Generate report

Save PDF

BIBLaTeX, ZoteroRISZotero, MendeleyTXTAPA BibliographyPDFPDFDOCXMicrosoft Word

Chat

Got some follow-up questions?

Sign up or sign in to chat with this report.

Back

Baseline Inflammatory Status Reveals Dichotomic Immune Mechanisms Involved In Primary-Progressive Multiple Sclerosis Pathology

J. I. Fernández-Velasco, E. Monreal, J. Kuhle, V. Meca-Lallana, J. Meca-Lallana, G. Izquierdo, C. Oreja-Guevara, F. Gascon-Gimenez, S. Sainz de la Maza, P. Walo-Delgado, Paloma Lapuente-Suanzes, A. Maceski, E. Rodríguez-Martín, E. Roldán, N. Villarrubia, A. Saiz, Y. Blanco, C. Díaz-Pérez, G. Valero-López, J. Díaz-Díaz, Y. Aladro, L. Brieva, C. Íñiguez, I. González-Suárez, L. A. Rodríguez de Antonio, J. M. García-Domínguez, J. Sabín, S. Llufriu, J. Masjuán, L. Costa-Frossard, L. Villar

Frontiers in Immunology·

2022·

3 citations

SourceDOI

Plain textPDF

Searching for PDF

Unable to find PDF from source

Back

B-Cell Depletion Biomarkers

- Specific cell types/markers: Total B cells, naïve B cells, memory B cells - Measurement units: Not specified - Baseline values: Not specified - Values at follow-up: Reduced total B cell numbers at 6 months - Depletion patterns: Low repopulation rate in Gd+ patients; rapid repopulation implied in EDA Gd+ patients - Categorizations: NEDA vs. EDA groups; Gd+ vs. Gd- groups

Clinical Response Measures

- Disability measures: EDSS scores, confirmed disability worsening - MRI outcomes: new/enlarging T2 lesions, Gd-enhancing lesions - Composite measures: NEDA defined as no disability progression or new MRI lesions after 1 year - Timepoints: baseline, 6 months, 1 year

Biomarker-Response Correlations

- Reduction in B-cell repopulation rate and sNfL levels are associated with NEDA status in Gd+ patients. - Low B-cell numbers and sNfL levels are observed in Gd- patients but are not directly correlated with treatment response. - Increase in serum IgA levels and changes in IgA/IgM and IgG/IgM ratios are associated with NEDA status in Gd- patients. - Reduction in sNfL levels is a key biomarker response correlated with clinical improvement in Gd+ patients.

Patient Characteristics

- MS subtype: PPMS - Disease duration: Median 9.2 years (range 1.3-24.1 years) - Baseline disability (EDSS): Median 5.5 (range 1.0-8.0) - Baseline disease activity: Classified by presence [Gd+, n=16] or absence [Gd-, n=53] of gadolinium-enhancing lesions - Demographics: Median age 52 years, 53% female - Sample size: 69 patients - Follow-up duration: 1 year

Key Findings

- Primary findings: Low repopulation rate of inflammatory B cells and reduction in sNfL levels correlate with clinical response in Gd+ patients. Increase in serum IgA levels and IgA to IgM ratio is significant in Gd- patients. - Clinical implications: These biomarkers may help in identifying responders to ocrelizumab and guide treatment decisions. - Recommendations: Monitoring these biomarkers could inform dosing strategies or treatment adjustments. - Limitations: Small sample size in certain subgroups; need for further research to fully understand biomarker roles. - Future research directions: Investigate antigen-presenting B cells and activated dendritic cells; further explore biomarker-guided treatment.

Objective To ascertain the role of inflammation in the response to ocrelizumab in primary-progressive multiple sclerosis (PPMS). Methods Multicenter prospective study including 69 patients with PPMS who initiated ocrelizumab treatment, classified according to baseline presence [Gd+, n=16] or absence [Gd-, n=53] of gadolinium-enhancing lesions in brain MRI. Ten Gd+ (62.5%) and 41 Gd- patients (77.4%) showed non-evidence of disease activity (NEDA) defined as no disability progression or new MRI lesions after 1 year of treatment. Blood immune cell subsets were characterized by flow cytometry, serum immunoglobulins by nephelometry, and serum neurofilament light-chains (sNfL) by SIMOA. Statistical analyses were corrected with the Bonferroni formula. Results More than 60% of patients reached NEDA after a year of treatment, regardless of their baseline characteristics. In Gd+ patients, it associated with a low repopulation rate of inflammatory B cells accompanied by a reduction of sNfL values 6 months after their first ocrelizumab dose. Patients in Gd- group also had low B cell numbers and sNfL values 6 months after initiating treatment, independent of their treatment response. In these patients, NEDA status was associated with a tolerogenic remodeling of the T and innate immune cell compartments, and with a clear increase of serum IgA levels. Conclusion Baseline inflammation influences which immunological pathways predominate in patients with PPMS. Inflammatory B cells played a pivotal role in the Gd+ group and inflammatory T and innate immune cells in Gd- patients. B cell depletion can modulate both mechanisms.

Objective: To ascertain the role of inflammation in the response to ocrelizumab in primary-progressive multiple sclerosis (PPMS).

Methods: Multicenter prospective study including 69 patients with PPMS who initiated ocrelizumab treatment, classified according to baseline presence [Gd+, n=16] or absence [Gd-, n=53] of gadolinium-enhancing lesions in brain MRI. Ten Gd+ (62.5%) and 41 Gdpatients (77.4%) showed non-evidence of disease activity (NEDA) defined as no disability progression or new MRI lesions after 1 year of treatment. Blood immune cell subsets were characterized by flow cytometry, serum immunoglobulins by nephelometry, and serum neurofilament light-chains (sNfL) by SIMOA. Statistical analyses were corrected with the Bonferroni formula.

Results: More than 60% of patients reached NEDA after a year of treatment, regardless of their baseline characteristics. In Gd+ patients, it associated with a low repopulation rate of inflammatory B cells accompanied by a reduction of sNfL values 6 months after their first ocrelizumab dose. Patients in Gd-group also had low B cell numbers and sNfL values 6 months after initiating treatment, independent of their treatment response. In these

1INTRODUCTION

Multiple sclerosis (MS) is the most common demyelinating disease of the central nervous system (1). It induces demyelination, inflammation, and axonal damage, responsible for the permanent neurological deficits suffered by patients with MS (2). Primary progressive MS (PPMS) represents about 10-15% (3) of all MS cases and is characterized by a disease progression that remains continuous since disease onset (4), with or without concomitant visible inflammation by conventional MRI. Although many therapeutic options are available for relapsing remitting MS (RRMS), this is not the case for PPMS. Most of the anti-inflammatory drugs found useful for patients with RRMS are not effective among those with PPMS (5). However, the anti CD20 antibody rituximab showed efficacy in depleting cerebrospinal fluid and peripheral blood B cells in PPMS (6) and results of the OLYMPUS clinical trial suggested that B cell depletion could be effective in those PPMS patients exhibiting signs of inflammation as demonstrated by the occurrence of contrast-enhancing lesions at baseline on MRI (7). Recently, results of the ORATORIO clinical trial with ocrelizumab, a humanized anti CD20 antibody, showed that patients with PPMS responded to this drug, independent of the presence of clinically demonstrable inflammation (8), and it was approved for the treatment of PPMS patients. Ocrelizumab not only induces B cell depletion but modulates T cell compartment toward adopting a more tolerogenic status (9).

Therefore, we aimed to explore the mechanisms associated with favorable response to ocrelizumab in inflammatory and non-inflammatory PPMS cases, toward facilitating the early identification of ocrelizumab responders and revealing new putative therapeutic targets in PPMS.

2.1Patients

This multicenter, prospective longitudinal study included 69 patients with PPMS diagnosed according to the McDonald criteria (10) who consecutively initiated ocrelizumab treatment in 13 Spanish University Hospitals. Patients were subdivided into four groups based on their inflammatory status (presence [Gd+, n=16] or absence [Gd-, n=53] of gadolinium-enhancing lesions at baseline) and their response to 1 year of ocrelizumab treatment. Non evidence of disease activity (NEDA) was defined as the absence of further Expanded Disability-Status Scale (EDSS) progression with no new MRI lesions at 1 year; in contrast, evidence of disease activity (EDA) patients as having at least one of the above-mentioned conditions. We considered patients as having an increase in the EDSS score when this was confirmed three months after the first assessment. The maximum gap between baseline MRI and treatment initiation was a month.

2.2Sample Collection

Blood samples were collected in heparinized tubes immediately before (baseline) and 6 months after (before the second dose) ocrelizumab treatment. In both cases they were obtained the day Ocrelizumab was administered, just before initiating the infusion. Samples were then sent to the Immunology Department at Ramoń y Cajal University Hospital (Madrid). Peripheral blood mononuclear cells (PBMCs) were isolated from 20 mL of heparinized whole blood as previously described (9) and cryopreserved in fetal bovine serum (HyClone Laboratories) supplemented with 10% DMSO (dimethyl sulfoxide) until further analysis. The samples collected at baseline and 6 months were analyzed simultaneously to avoid inter-assay variability. Serum samples were also collected and stored at -80°C while awaiting analysis. Total lymphocyte and monocyte counts were determined using a Coulter Counter from 10 mL of fresh EDTA-treated blood.

2.3Monoclonal Antibodies

The monoclonal antibodies used in this study are listed in Supplementary Table 1 . No differences were observed in plasmablast counts in any of the groups studied (Figure 1E ; Supplementary Table 2 ).

2.4Labelling Surface Antigens

Aliquots of 10 6 PBMCs were thawed by a 37°C thermostatic bath and washed twice in RPMI 1640 medium (Thermo Fisher Scientific). Samples were processed and stained as described (9) prior to being analyzed by flow cytometry.

2.5In Vitro Stimulation and Intracellular Cytokine Staining

Thawed aliquots to analyze intracellular cytokine production were subdivided in three polypropylene tubes. To study cytokine production by monocytes, an aliquot of 3x10 5 PBMCs was resuspended in 1 mL of RPMI 1640 medium and incubated with 1 mg/mL lipopolysaccharide (from Escherichia coli O111: B4; Merck) in presence of 2 mg/ml Brefeldin A (GolgiPlug, BD Biosciences) and 2.1 mM Monensin (Golgi Stop, BD Biosciences) during 4 hours at 37°C in 5% CO 2 atmosphere.

To study cytokine production by T and B cells (Except IL-10 producing B cells) an aliquot of 3x10 5 PBMCs was resuspended and incubated in 1 mL RPMI 1640 medium and stimulated with 50 ng/mL of Phorbol 12-myristate 13-acetate (PMA, Merck) and 750 ng/mL Ionomycin (Merck) in presence of 2 mg/ml Brefeldin A and 2.1 mM Monensin during 4 hours at 37°C in 5% CO 2 atmosphere.

To identify IL-10 producing B cells, an aliquot of 3x10 5 PBMCs was preincubated in 1 mL RPMI 1640 medium with 3 mg/mL of CpG oligonucleotide (In vivoGen) during 20h at 37°C in 5% CO2 atmosphere. After this, it was stimulated with 50 ng/ mL of Phorbol 12-myristate 13-acetate (PMA, Merck) and 750 ng/mL Ionomycin (Merck) in presence of 2 mg/ml Brefeldin A and 2.1 mM Monensin during 4 hours at 37°C in 5% CO 2 atmosphere.

After incubation, the three aliquots were stained with the twostep protocol described previously (9). PBMCs were analyzed in a FACSCanto II flow cytometer (BD Biosciences).

2.6Flow Cytometry

Cells were always analyzed within a maximum period of 1h after staining. Mean autofluorescence values were set using appropriate negative isotype controls. Data analysis was performed using FACSDiva Software V.8.0 (BD Biosciences). A minimum amount of 5x10 4 events were analyzed. We followed the strategy showed in Supplementary Figure 1 to identify the different subpopulations. We set a gate including cells with high to intermediate CD45 and low to intermediate side scatter and excluding debris and apoptotic cells. CD4 and CD8 T cells were classified as: naïve (CCR7+ CD45RO-), central memory (CM) (CCR7+ CD45RO+), effector memory (EM) (CCR7-CD45RO+), and terminally differentiated (TD) (CCR7-CD45RO-). Regulatory CD4 T cells (Treg) were defined as CD3+ CD4+ CD25 hi CD127 -/ low . CD56 NK cells were classified as: NKT cells (CD3+ CD56 dim ), CD56dim NK cells (CD3-CD56 dim ) and CD56bright NK cells (CD3-CD56 br ). B cells were classified as: naïve (CD19+ CD38 dim CD27-), memory (CD19+ CD27 dim CD38 dim ), plasmablasts (CD19 + CD27 hi CD38 hi ), transitional B cells (CD19+ CD27-CD24 hi CD38 hi ) cells or regulatory B cells (Breg) (CD19+ IL-10+) cells. PD-L1 was explored in monocytes by studying its co-expression with CD14 in PBMCs. We also explored intracellular production of IL-1b, IL-6, IL-10, IL-12 and TNFa by monocytes.IL-1b and TNFa represent innate cell activation, IL-12 induces Th1 responses, IL-6 represent innate cell activation and induces Th17 responses and finally, IL-10 is an anti-inflammatory cytokine. We also explored in CD4 and CD8 T cells the production of IFNg and TNFa, products of Th1 response; IL-17, a product of the Th17 response; GM-CSF, which induces innate cell activation; and IL-10 that has a regulatory function. Finally, we explored B cells producing IL-6, a proinflammatory cytokine that induces Th17 cells; TNFa, an inflammatory cytokine; GM-CSF, inducing innate cell activation; and IL-10 a regulatory cytokine. Representative dot plots showing cytokine production by monocytes, B and T cells are shown in Supplementary Figure 2 .

2.7Flow Cytometry Analyses

We recorded for every leukocyte subset total cell counts per mL of blood, calculated by measuring total lymphocyte and monocyte numbers by a coulter counter, and the percentages of every subset over total mononuclear cells. To avoid bias due to B cell depletion, we also recorded the values of every T, B, NK and monocyte subset relative to total T, B, NK and monocyte cells, respectively.

2.8Immunoglobulin and sNfL Quantification

Serum levels of immunoglobulins (IgG, IgA, and IgM) were measured by nephelometry using a DimensionVista analyzer (Siemens Healthcare Diagnostics) and serum neurofilament light chain (sNfL) levels were measured using the single molecule array (Simoa) NF-light ® Assay (Quanterix).

2.9Statistical Analyses

Statistical analyses were performed using GraphPad Prism 8.0 software (GraphPad Prism Inc.). Wilcoxon matched pairs test was used to assess differences between the samples collected at baseline and after 6 months from the same patient. Mann-Whitney-U test was used to compare the subgroups of patients. P-values were adjusted using the Bonferroni correction and p-values less than 0.05 were considered statistically significant.

The association between NfL and age has been modelled using a Generalized Additive Model for Location, Scale and Shape (GAMLSS) model and age-normalized measures were obtained for each data point. Z score was used as a continuous measure for the number of standard deviations a given datapoint is above/below the mean in samples of healthy controls of the same age.

2.10Ethical Considerations

Written informed consent was obtained from every patient prior to their inclusion in the present study, which was approved by the Ethics Committee of each center participating in this study.

2.11Data Availability Statement

Any anonymized data collected for the purpose of this study will be shared with qualified investigators for 3 years from the initial publication of the study upon reasonable request to the corresponding author.

3RESULTS

Sixty-nine patients with PPMS (53% female) treated with ocrelizumab were included in this study. Age and disease duration [median (range)] were, respectively, 52.0 (33.0-71.0) and 9.2 (1.3-24.1) years and basal EDSS score was 5.5 (1.0-8.0). All patients were followed for 1 year. Fifty-one (73.9%) remained NEDA 1 year after ocrelizumab initiation. Using MRI data collected at baseline, we further classified the patients according to their inflammatory status into Gd+ (at least one Gd-enhancing lesion) and Gd-(no Gd-enhancing lesions) groups. Ten Gd+ (62.5%) and 41 Gd-(77.4%) patients were NEDA at the one-year follow-up. We found no significant differences between the four patient subgroups in terms of baseline clinical characteristics except for the MRI data (Table 1 ).

At baseline, few differences were found between Gd+ and Gdpatients (Supplementary Figure 3 ). sNfL levels were higher in Gd+ group, (p=0.019). Likewise, plasmablast numbers trended to be higher in this group of patients (p=0.029) but significance was lost after Bonferroni correction. By contrast, percentages of B naïve cells with respect to total CD19+ cells were higher (p=0.015) in Gd-patients. No differences were found between those Gd+ and Gd-patients in monocytes, T or NK cell subsets analyzed nor in intracellular cytokine production (data not shown).

We next explored differences in the PBMCs induced by ocrelizumab according to patient group after 6 months of ocrelizumab treatment by addressing its impact on the absolute numbers (Supplementary Table 2 ) and relative percentages (Supplementary Table 3 ) of each cellular subtype.

3.1B Cells

As expected, after 6 months of treatment, ocrelizumab reduced the total numbers of B cells in all groups (Supplementary Table 2 ). After applying the Bonferroni correction, these differences remained statistically significant in the Gd+ NEDA group and the Gd-EDA and NEDA groups (Figure 1A and Supplementary Table 2 ). These differences were mainly due to decreased naïve and memory B cell numbers (Figures 1B, C , Supplementary Table 2 ). However, there was no statistically significant reduction in any B cell subpopulation in EDA patients in the Gd+ group (Supplementary Table 2 ). This may be partly due to the low number of patients included in this group. However, it should be noted that this patient subgroup had significantly more total (p=0.030) and transitional (p=0.030) B cells than the NEDA patients in the same Gd+ group at the 6month follow-up (Figures 1A, D , respectively; Supplementary Table 2 ). This could also imply a more rapid B cell repopulation in this group. These differences were not observed in the Gdpatients (Supplementary Table 2 ).

We next evaluated intracellular cytokine production in B cells. Again, after applying the Bonferroni correction, a drastic reduction in IL-10, IL-6, GM-CSF, and TNFa B cell numbers (Figures 2A-D , respectively, Supplementary Table 2 ) was observed in the Gd+ NEDA group and in both Gd-EDA and NEDA patients. However, this was not observed in Gd+ EDA patients, who at 6 months of follow-up showed increased numbers of B cells producing TNFa than NEDA patients of the same group (p=0.04, Figure 2D and Supplementary Table 2 ). At 6 months, B cell numbers were very low and thus establishing percentages respective to total B cells with such a small quantity of cells could result highly imprecise. Consequently, we decided not to analyze differences relative to total B cells percentages, contrary to what we did with the rest of the leukocyte populations.

3.2.1Total Cell Counts

We next studied the effect of ocrelizumab in the different T cell subpopulations after 6 months of ocrelizumab treatment. We only found significant differences in the CD20+ T cell subset. It decreased in all groups but after Bonferroni correction differences only remained significant in the Gd+ NEDA group and in both Gd-EDA and NEDA groups (Figure 1F and Supplementary Table 2 ). Results were similar when studied separately CD4+ and CD8+ subsets (Supplementary Figure 4 ).

3.2.2Percentages Relative to CD4+ and CD8+ Subsets

No differences were observed in the Gd+ group (Supplementary Table 3 ). However, ocrelizumab treatment modified the T cell compartment in NEDA patients of the Gd-group. We found an increase in the proportion of naïve CD4+ T cells (p=0.004, Supplementary Table 3 ) accompanied by decreases in the percentages of TD (p=0.002, Supplementary Table 3 ) and EM (p=0.041, Supplementary Table 3 ) CD4+ T subsets related to the total CD4+ T cell population. Accordingly, we found increases in the naïve/EM (p=0.004, Figure 3A ) and naïve/TD (p<0.0001, Figure 3A ) ratios. We also found an increase in naïve CD8+ T cell percentages relative to total T CD8+ cells (p=0.031, Supplementary Table 3 ). Although, in this case, it was not associated with significant decreases in effector and TD subpopulations, again, NEDA patients showed an increase in the naïve/EM (p=0.011, Figure 3B ) and naïve/TD (p=0.005, Figure 3B ) ratios.

Finally, we studied the relative changes in intracellular cytokine production by CD4+ and CD8+ T cells. No differences were found in cytokine production from CD4+ T cells. However, in the Gd-group, the NEDA patients experienced a decrease in the proportion of CD8+ T cells producing IFNg (p=0.004, Figure 3C and Supplementary Table 3 ) relative to the total CD8+ T cell population.

By contrast, no changes were observed in EDA Gd-group in any T cell subset after six months of ocrelizumab treatment. This suggests that the remodeling of the T cell compartment is important for the response to ocrelizumab in these patients.

3.3Innate Immune Cells

An increase in the total monocyte counts was found in Gd-NEDA patients 6 months after ocrelizumab treatment with the differences being higher in the NEDA group (p=0.034, Supplementary Table 2 ). We next explored if this increase was associated with the total numbers of monocytes producing IL-1, IL-6, IL-10, IL-12, and TNF-alpha or expressing PD-L1. The only significant differences were found in PD-L1+ monocytes. Gd-NEDA patients experienced an increase of the numbers of this subset after ocrelizumab treatment (p=0.013, Supplementary Table 2 ). No significant changes were observed in the proportions of any subset relative to total monocytes. We observed a decrease in the number of CD56bright NK cells (p=0.023, Supplementary Table 2 ) in the Gd-NEDA group after treatment with no variations in the relative proportions of the NK subsets analyzed (Supplementary Table 3 ).

3.4Serum NfL Levels

Baseline sNfL levels were higher in the Gd+ EDA group compared with the Gd-EDA (p=0.014) and Gd-NEDA (p=0.007) groups (Figure 4 ). They also trended toward higher values than those observed in the Gd+ NEDA patients (p=0.07). Six months after ocrelizumab treatment, sNfL levels were only significantly lower than basal values in the Gd+ NEDA group (Figure 4 ). They did not decrease significantly in the Gd+ EDA patients. The Gd-NEDA and EDA groups, whose baseline sNfL values were not elevated, did not vary significantly from each other upon treatment. When explored the z score normalized by patient age, we observed that in most Gd+ EDA patients, baseline sNfL values were higher than 2 z score levels (median values=2.411) whilst they were mostly between 0 and 2 z score values in most patients from the other three groups (Table 2 ).

3.5Serum Immunoglobulin Values

We explored changes in serum immunoglobulin concentrations 6 months after the first dose of ocrelizumab. The levels of IgG remained stable, while significant decreases in serum IgM levels were observed in all of them (Table 3 ). Additionally, increased serum IgA levels were observed in the Gd-NEDA group (p=0.006, Table 3 ). However, the most interesting results were observed when the changes in the ratios of the different serum immunoglobulins were explored. The IgA to IgM ratio was augmented in all groups (Figure 5B ) with these changes highly relevant in Gd-NEDA group (p=4x10 -12 ). The IgG to IgM ratio also increased (Figure 5A ), being again the most prominent changes observed in the Gd-NEDA group (p=2.5x10 -1 ).

Remarkably, this group of patients experienced an increase in the IgA to IgG ratio (p=0.0011; Figure 5C ) not observed in any other group.

4DISCUSSION

Ocrelizumab selectively depletes CD20+ cells while maintaining B cell reconstitution and pre-existing humoral immunity (11). In patients with PPMS, ocrelizumab not only induces B cell depletion, but reshapes the T cell response toward a low inflammatory profile, resulting in decreased sNfL levels (9). However, the influence of baseline inflammatory status on these changes has not been fully ascertained. Inflammation is used in the classification of patients with progressive MS. In fact, current guidelines for diagnosing PPMS include two qualifiers: disease activity, defined by MRI or clinical evidence of inflammatory lesions or relapses; and disability progression, defined as a gradually worsening disability independent of relapses (12).

Inflammatory status can also play a role in DMT effectiveness. Overall subgroup analyses evaluating Rituximab in patients with PPMS (OLYMPUS trial) suggested that this drug may affect disease progression in younger patients, particularly those with inflammatory lesions (7). However, results from the ORATORIO trial with ocrelizumab showed that anti-CD20 antibodies are effective in non-inflammatory patients with PPMS (8). This agrees with our results. We explored response to ocrelizumab in a multicenter prospective cohort of 69 patients with PPMS treated with this drug. NEDA patients at 1 year of treatment represented 62% of the inflammatory group (10 out of 16) and 75% of the non-inflammatory one (41 of 53) in our study.

We first explored baseline differences in patients classified according to their inflammatory status (presence [Gd+]/absence [Gd-] of gadolinium-enhancing lesions at baseline). We studied different leukocyte subsets (B cells, T cells, NK cells and monocytes) and sNfL values. As expected, Gd+ patients showed high sNfL values. They also showed a trend to higher plasmablast numbers. The association of plasmablasts with inflammation in MS has been widely documented in the CSF (13). Probably peripheral blood is not the best compartment to study these cells in MS, but our data seem to confirm the role of plasmablasts in inflammation in MS. On the other hand, we found increased values of naïve B cells in Gdpatients. This suggests other function for B cells in noninflammatory PPMS patients, as antigen presentation. No other differences were found between Gd+ and Gd-patients.

We next studied changes associated with NEDA status in both groups of patients by analyzing T, B, NK, and monocyte cell subsets at baseline and 6 months after receiving the first dose of treatment, before receiving the second one. Patients were classified according to their inflammatory condition (Gd+ or Gd-) and to their response to treatment (NEDA or EDA).

Although B cells clearly diminished in all groups of patients 6 months after the first ocrelizumab dose, in the EDA inflammatory group, total B cell counts were higher at this point. This increase was mainly due to transitional B cells, thus indicating a higher rate of B cell repopulation for these patients, and to TNF-alpha-producing B cells, thus indicating a rapid differentiation into pro-inflammatory B cells. Another characteristic of EDA Gd+ patients was the increased levels of sNfL levels at baseline compared with the noninflammatory groups, with a trend also observed for higher values than NEDA Gd+ patients. These data show that baseline Gd enhancing lesions and especially high serum neurofilament levels associate in PPMS with a high rate of B cell repopulation and strongly suggest that these patients should benefit from early retreatment or dose adjustment. By contrast, in NEDA Gd+ patients, the low B cell counts at 6 months were associated with a significant reduction of the sNfL levels 6 months after the first ocrelizumab dose. Finally, in NEDA and EDA Gd-patients, sNfL levels did not change significantly after ocrelizumab treatment, despite the reduction in B cell counts or response to treatment. This is important, since NfL is being proposed as a biomarker for response to different drugs in MS ( 14) and this could be not the case in patients with low inflammatory activity. T cell activation and cytokine production are also affected by anti-CD20 DMTs (9,15). We first studied absolute T cell counts. Only CD20+ T cells decreased in number. This occurred in the four patient groups, with Gd+ EDA being the only one in which the reduction did not reach statistical significance, probably due to the low sample size. The CD20+ T cell subset has been proposed to play an important role in MS pathology because of its highly activated phenotype and proinflammatory and migratory properties ( 16) and a reduction in these cells, described also for alemtuzumab, fingolimod, and dimethyl fumarate (16), can be beneficial for patients with PPMS treated with ocrelizumab, independent of their baseline inflammatory status.

We next explored the changes in the proportions of the different T cell subsets after 6 months of treatment relative to the total CD4+ and CD8+ T cells. Variations were restricted to the Gd-NEDA group. These patients exhibited a reduction in TD and EM CD4+ T effector cells and increases in naïve CD4+ and CD8+ T cells and in the ratios of naïve/EM and naïve/TD in CD4+ and CD8+ T cells. Likewise, they experienced a decrease in the proportion of CD8+ T cells producing IFNg. These data show that response to ocrelizumab in Gd-patients is conditioned by reshaping the T cell compartment to a more tolerogenic profile. The activation of the T cell compartment by B cells may play an important role in the pathology of Gd-PPMS. Furthermore, the beneficial effects of different drugs in patients with MS with low inflammatory contribution to their disease may be reflected by changes in other biomarkers aside from sNfL, especially in those with low baseline levels of this protein.

Regarding to Gd-EDA patients, we did not find any clear explanation for the lack of changes in the T cell compartment they showed upon Ocrelizumab treatment. They had no differences on epidemiological, clinical or immune cell subsets at baseline with NEDA group. Exploring antigen presenting B cells and activated dendritic cells in both groups could help to elucidate this conundrum. Future research will demonstrate if antigen presenting B cells could be a biomarker of response to Ocrelizumab in Gd-PPMS patients, as suggested by the remodeling in the T cell compartment showed by NEDA patients.

We also explored changes in innate immune cells at baseline and after 6 months of treatment. Again, we only found significant changes in NEDA Gd-patients. They showed a significant increase in total monocyte counts. This increase was mainly due to PD-L1-expressing monocytes. This molecule is a ligand of the PD-1 receptor, which promotes self-tolerance by suppressing T cell inflammatory activity (17). Its increase has been described in response to other drugs in MS (18). Its up-regulation upon B cell depletion further demonstrates the role of inflammatory B cells in inducing inflammation in cells of the innate immune response and how this can be changed by B cell depletion (19). The up-regulation of PD-L1 expression by monocytes may contribute to the remodeling of the T cell compartment observed in these patients.

Regarding serum immunoglobulins in the four groups of patients, ocrelizumab induced a decrease in serum IgM levels, with no changes in IgG values, as previously reported for patients treated with anti-CD20 antibodies (9,20). Likewise, all groups of patients showed a decrease in the IgG/IgM and IgA/IgM ratios. Most IgM molecules present in serum are natural antibodies that react against non-protein antigens, anti-lipid specificity being the most frequent (21,22). Intrathecal synthesis of anti-lipid IgM antibodies associates with an aggressive MS course (22,23). Thus, the down regulation of the B cells producing these antibodies may have a beneficial effect in MS. Additionally, our data contain interesting results with IgA antibodies. Gd-NEDA patients showed an increase of the levels of this immunoglobulin upon ocrelizumab treatment, and raised values of the ratio IgA/IgG. IgA, produced mostly at mucosal surfaces, functions as a critical mediator of intestinal homeostasis (24) and gut-microbiota reactive IgA plasma cells can migrate to peripheral organs with potential roles in extraintestinal autoimmune diseases (25). In MS, gut microbiota-specific IgA cells are considered a systemic mediator of the disease behaving as an informative biomarker during active neuroinflammation (26). In the experimental model of the disease, migration of IgA-producing plasma cells from the intestinal mucosa to the CNS has proven to down-modulate disease activity. This was attributed to IL-10 production in these cells (27), but often, natural IgA antibodies produced by plasma cells of the gut mucosa recognize similar antigens that natural IgM antibodies present in serum (28,29). These IgA antibodies could block antigens recognized by IgM, thus avoiding complement fixation and diminishing axonal damage.

In summary this study shows that baseline inflammation could determine the immunological pathways that drive the response to Ocrelizumab in PPMS and that, regardless of baseline MRI activity, B cell depletion with ocrelizumab can modify both underlying mechanisms, and be effective in more than 60% of patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Ethics Committee of each participating hospital. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

JF-V drafted the manuscript, major role in performing experiments, acquisition and analysis of data. PW-D, PL-S, and NV collected samples and performed flow cytometry experiments. JK and AM contributed to sNfL measurement. ER-M and ER supervised flow cytometry studies. EM, VM-L, JM-L, GI, CO-G, FG-G, SS, AS, YB, CD-P, GV-L, JD-D, YA, LB, CI ́, IG-S, LR, JG-D, JS, SL, JM, and LC-F visited MS patients, contributed by sending samples or collected clinical data. LV designed and supervised the study. All authors revised the manuscript and approved the final version.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.

842354/full#supplementary-material

Conflict of Interest: EM received research grants, travel support or honoraria for Publisher's Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2022 Fernańdez-Velasco, Monreal, Kuhle, Meca-Lallana, Meca-Lallana, Izquierdo, Oreja-Guevara, Gascoń-Gimeńez, Sainz de la Maza, Walo-Delgado, Lapuente-Suanzes, Maceski, Rodrı ́guez-Martı ́n, Roldań, Villarrubia, Saiz, Blanco, Diaz-Peŕez, Valero-Loṕez, Diaz-Diaz, Aladro, Brieva, I ́ñiguez, Gonzaĺez-Suaŕez, Rodríguez de Antonio, Garcı ́a-Domı ́nguez, Sabin, Llufriu, Masjuan, Costa-Frossard and Villar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Acknowledgements

ACKNOWLEDGMENTSAuthors acknowledge AI Peŕez Macıás and S Ortega for their excellent technical support.speaking engagements from Biogen, Merck, Novartis, Roche, and Sanofi-Genzyme; JK received speaker fees, research support, travel support, and/or served on advisory boards by ECTRIMS, Swiss MS Society, Swiss National Research Foundation (320030_189140/1), University of Basel, Bayer, Biogen, Celgene, Merck, Novartis, Roche, Sanofi; VM-L received grants and consulting or speaking fees from Almirall, Biogen, Celgene, Genzyme, Merck, Novartis, Roche and Teva; JM-L has received grants and consulting or speaking fees from Almirall, Biogen, Bristol-Myers-Squibb, Genzyme, Merck, Novartis, Roche and Teva; GI has received consultancy/advice and Conference-travel support from Bayer, Novartis, Sanofi, Merck Serono, Roche, Actelion Celgene and Teva; CO-G has received speaker and consulting fees from Biogen, Celgene, Merck KGaA (Darmstadt, Germany), Novartis, Roche, Sanofi Genzyme and Teva; FG-G has received funding for research grants, travel support and honoraria for speaking engagements from: Bayer, Biogen, Roche, Merck, Novartis, Almirall, Teva and Genzyme-Sanofi; SS received research grants, travel support, or honoraria for speaking engagements from Almirall, Bayer, Biogen, Merck, Mylan, Novartis, Roche, Sanofi-Genzyme and Teva; AS reports compensation for consulting services and speaker honoraria from Merck-Serono, Biogen-Idec, Sanofi-Aventis, Teva Pharmaceutical Industries Ltd, Novartis, Roche, and Alexion; YB received speaking honoraria from Biogen, Novartis and Genzyme; LB received funding for research projects or in the form of conference fees, mentoring, and assistance for conference attendance from: Bayer, Biogen, Roche, Merk, Novartis, Almirall, Celgen and Sanofi; IG-S received research grants, travel support and honoraria for speaking engagements from Biogen, Merck, Novartis, Roche, Sanofi-Genzyme, TEVA and Alexion; LR received travel support, and honoraria for speaking engagements from Biogen, Merck, Roche and Sanofi-Genzyme; JS received funding for research projects and conference fees, mentoring, and assistance for conference attendance from: Teva, Merck, Biogen, Roche, Novartis, and Sanofi; SL received compensation for consulting services and speaking honoraria from Merck-Serono, Biogen-Idec, Sanofi-Aventis, Teva Pharmaceutical Industries Ltd, Novartis and Roche; LC-F received speaker fees, travel support, and/or served on advisory boards by Biogen, Sanofi, Merck, Bayer, Novartis, Roche, Teva, Celgene, Ipsen, Biopas, Almirall; LV received research grants, travel support or honoraria for speaking engagements from Biogen, Merck, Novartis, Roche, Sanofi-Genzyme and Bristol-Myers.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

FUNDINGThis work was supported by Red Española de Esclerosis Muĺtiple (REEM) (RD16/0015/0001; RD16/0015/0002; RD16/0015/0003; RD16/0015/0008; RD16/0015/0013) and PI18/00572 integrated in the Plan Estatal I+D+I and co-funded by ISCIII-Subdireccioń General de Evaluación and Fondo Europeo de Desarrollo Regional (FEDER, "Una manera de hacer Europa").

Failed to load PDF:

StripeM-Inner

Paper sources

Abstract screening pilot

Abstract screening results

Extraction pilot

Extraction results

Research report

Modify setup