A combination of cellular biomarkers predicts failure to respond to rituximab in rheumatoid arthritis

Clinical Research 10 min 254

Font Size

Created On: 10/10/2019

A combination of cellular biomarkers predicts failure to respond to rituximab in rheumatoid arthritis

With the introduction of biologic disease-modifying anti-rheumatic drugs (DMARDs) the armamentarium to fight rheumatoid arthritis (RA) has been dramatically enlarged.

See All

Key take away

A combination of lymphocyte counts and plasmablast frequency identifies patients with rheumatoid arthritis who will not benefit from rituximab with high probability.

Background

With the introduction of biologic disease-modifying anti-rheumatic drugs (DMARDs) the armamentarium to fight rheumatoid arthritis (RA) has been dramatically enlarged. However, we are unable to predict which of these therapies would be optimal for a certain patient. For example, B cell depletion with the chimeric monoclonal antibody rituximab (RTX) is an effective treatment strategy for RA. However, a considerable proportion of around 30 % of patients with RA treated with RTX fail to respond, particularly after previous therapy with tumor necrosis factor (TNF)-α inhibitors. Identification of patients likely to respond to RTX treatment would result in an optimized treatment strategy reducing unnecessary socio-economic costs and potential side effects. Currently available clinical and laboratory parameters predicting the success of RTX therapy include the presence of rheumatoid factor (RF) and/or anti-citrullinated peptide antibodies (ACPA), and the absence of current glucocorticoid therapy. In addition, high serum calprotectin has been associated with good or moderate response to RTX. Furthermore, several authors have demonstrated that patients with RA who have a high frequency of plasmablasts are less likely to respond to RTX. All these factors, however, have been established to predict EULAR response. Current recommendations for the treatment of RA define remission or low disease activity (LDA) in patients with long-standing disease as the goal of treatment after 6 months, a target that is not achieved with a moderate EULAR response in many cases.

Rationale behind research:

Whether factors other than B cell subsets or their products might help us to find the optimal therapy for a particular patient is still unknown. Thus, the influence of RTX on T cells became an alternative focus of recent investigations.

Objective:

To analyze whether baseline levels of lymphocyte subsets other than those of B cells may predict clinical response to RTX
To analyze whether changes in T cell subsets correlate with clinical outcomes

Method

Study outcomes:

Complete blood count, lymphocyte analysis, and assessment of disease activity score in 28 joints (DAS28) using the erythrocyte sedimentation rate (ESR) were carried out before RTX treatment and at week 24.

Lymphocyte analysis blood cell counts in peripheral blood samples were obtained using a Beckman Coulter HMX hematology analyzer. For lymphocyte subsets determination, whole blood was stained for CD45, CD3, CD19, CD4, CD8, CD56, and CD16 using the BD Multitest IMK kit. After fixation and erythrocyte lysis according to the manufacturer’s protocol, cells were analyzed on a FACS Calibur flow cytometer (Becton Dickinson) using FACS Diva software (Becton Dickinson).

Time Points: Baseline and at week 24

Result

Outcomes:

Baseline Characteristics: Baseline total lymphocyte count (LC) was significantly higher in the non-responder group compared to patients with EULAR response. There was no significant difference in baseline LC between patients with DAS28 > 3.2 and DAS28 ≤ 3.2 at week 24. There was a higher CD3+ T-cell count in non-responders than in responders. In addition, non-responders had higher total numbers of B cells compared to responders. Importantly, at baseline none of these populations was significantly different between patients with a DAS28 > 3.2 or a DAS28 ≤ 3.2 at week 24. Also, DMARD therapy and glucocorticoid use had no influence on lymphocyte counts or lymphocyte subsets. In univariate logistic regression analysis high baseline LC, B cell, T cell, and CD4+ T cell counts were negative predictors of EULAR response, but there were no predictors of LDA.
Combining baseline LC and plasmablast frequency predicts low disease activity: The current focus in RA therapy is to achieve remission or LDA by 6 months. None of the cellular biomarkers evaluated so far solely predicted LDA. Therefore, it was investigated whether a combination of two biomarkers could be used for predicting LDA. It was hypnotized that these biomarkers would have to identify a different patient group each, in order to improve the prediction. By blotting each of the candidate biomarkers against each other, it was found that high LC and high plasmablasts, a predictor of EULAR response, each recognized a different population of patients not reaching LDA by week 24. In contrast, all patients who did reach LDA had low LC and low plasmablast frequency. This finding compelled the investigators to use a combination of both biomarkers in inclusive disjunction. The cutoffs for both values were chosen to yield at least 90 % specificity for non-response generated by ROC analysis. Patients with elevated LC >2910/μl or plasmablast frequency >2.85 % (hiLOP) at baseline had a significantly higher DAS28 at week 24 after treatment with RTX than patients who had LC and plasmablast frequency below the thresholds (loLAP) (DAS28 of 4.9 ± 0.3 and 3.7 ± 0.2, respectively; P = 0.002.
T cell numbers are reduced after treatment with RTX, but do not correlate with clinical response: It was also tested that whether a change in the absolute T cell count after therapy with RTX correlates with EULAR response. The absolute number of T lymphocytes at week 24 after RTX was reduced to 79.9 % of baseline levels. The reduction of T lymphocytes and the decrement of CD4+ T-cell levels were not associated with a EULAR response or a DAS28 ≤ 3.2. Changes within the CD8+ T lymphocyte subset were also similar in responders and non-responders.

Conclusion

This is the first nationwide population-based study known till now which has explored the relationship between osteoporosis and subsequent migraine in an Asian population. During the follow-up period, migraine developed in 2.73 % (1110) patients with osteoporosis and in 1.85 % (750) patients without osteoporosis. Patients with osteoporosis, particularly those with high CCI score, female gender, hypertension, depression, asthma, allergic rhinitis, obesity, and tobacco use disorder, had a high migraine risk. The exact mechanisms underlying the relationship between migraine and osteoporosis are likely to be elusive. However, several lines of evidence in the literature suggest that osteoporosis and migraine have a shared pathophysiology. Gallai et al. showed that individuals suffering from migraine headaches had lower plasma and saliva magnesium levels between the attacks compared to controls without migraine headaches. Both osteoporosis and migraine are associated with hypomagnesemia, which suggests an interplay between osteoporosis and migraine. Secondly, the relationship between migraine and osteoporosis might be explained at least partly by their common inflammatory mediators. Inflammatory cytokines associated with osteoporosis such as tumor necrosis factor-αand IL-6 are elevated at the onset of migraine attacks. Finally, C-reactive protein, which increases during systemic inflammation, is elevated in both osteoporosis and migraine. Thus, the inflammatory state caused by osteoporosis may increase the frequency or severity of migraine headaches by exacerbating the inflammatory response.

Clinicians should be aware that osteoporosis is a potential risk factor for migraine. Further studies are recommended to confirm this association and to explore its mechanisms.

Limitations

The biggest limitation of study was the small sample size
There was lack of detailed characterization of T cells

Clinical take-away

This study described the combination of biomarkers, i.e., high LC and plasmablast frequency at baseline, that reliably identify patients who are likely to fail to respond to RTX treatment.