B-cell Biomarkers and Ocrelizumab Response

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

Total papers: 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

Papers screened out: n = 190

Papers included for extraction: n = 10

Screening Criteria

• Ocrelizumab Treatment: Does this study involve patients treated with ocrelizumab?
• B-cell Depletion Biomarkers: Does this study measure B-cell depletion biomarkers (e.g., CD19+ B-cell counts, CD20+ B-cell counts, B-cell subsets, or immunoglobulin levels)?
• Clinical Response Outcomes: Does this study report clinical response outcomes (e.g., relapse rates, disability progression, MRI outcomes, or clinical scales)?
• Quantitative Correlation Analysis: Does this study include quantitative analysis of correlation or association between biomarkers and clinical outcomes?
• Follow-up Biomarker Measurements: Does this study include follow-up biomarker measurements (not only baseline levels)?
• Adequate Sample Size: Does this study include 10 or more patients?
• Full-text Publication: Is this a full-text publication (not just a conference abstract without full publication)?
• Ocrelizumab-specific Data: Does this study include ocrelizumab-specific data (rather than focusing solely on other anti-CD20 therapies without ocrelizumab data)?

Data Extraction

We extracted the following data columns from each paper:

B-Cell Depletion Biomarkers

• Specific cell types/markers (e.g., CD19+, CD20+, total B cells, plasmablasts)
• Measurement units (cells/μl, percentage, absolute counts)
• Baseline values and values at each follow-up timepoint
• Depletion patterns (complete vs. incomplete depletion, fast vs. slow repopulation)
• Any categorizations used (e.g., fast repopulation vs. slow repopulation groups)

Clinical Response Measures

• Relapse-related outcomes (annualized relapse rate, time to first relapse, relapse-free survival)
• Disability measures (EDSS scores, confirmed disability worsening, disability progression-free survival)
• MRI outcomes (new/enlarging T2 lesions, Gd-enhancing lesions, brain volume changes)
• Composite measures (NEDA-3, treatment response definitions)
• Timepoints when clinical responses were assessed

Biomarker-Response Correlations

• Statistical measures of association (correlation coefficients, p-values, hazard ratios, odds ratios)
• Direction and strength of relationships (positive/negative, weak/strong)
• Specific biomarker-outcome pairs that were significantly correlated
• Predictive relationships (biomarkers predicting future clinical outcomes)
• Any dose-response or threshold effects identified

Patient Characteristics

• MS subtype (RRMS, PPMS, SPMS, active PPMS)
• Disease duration and baseline disability (EDSS)
• Previous treatment history (treatment-naive vs. previously treated, specific prior DMTs)
• Baseline disease activity (recent relapses, MRI activity)
• Demographics (age, sex) if analyzed in relation to biomarker responses
• Sample size and follow-up duration

Key Findings

• Primary findings about biomarker-response relationships
• Clinical implications mentioned by authors
• Recommendations for biomarker monitoring or dosing strategies
• Limitations or caveats about the biomarker correlations identified
• Future research directions suggested regarding biomarker-guided treatment

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.

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. 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.

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. Relapse-related outcomes included annualized relapse rate (ARR), time to first relapse, and number of attacks.

MRI outcomes varied across studies. 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.

Biomarker-Clinical Response Correlations

Total B-Cell and CD19+ Depletion

Multiple studies identified plasmablasts as paradoxically important despite representing a small fraction of circulating B 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.

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.

Conclusions

Our data point at ocrelizumab as an effective treatment option in patients with RRMS and PMS, especially for those patients who initiate ocrelizumab treatment in the early phase of the disease and for treatment-naïve patients. Depletion of CD8+ cells could account for early therapeutic effects of ocrelizumab.