Peripheral Blood Gene Expression and IgG Glycosylation Profiles as Markers of Tocilizumab Treatment in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Tocilizumab, a humanized anti-interleukin-6 receptor monoclonal antibody, has recently been approved as a biological therapy for rheumatoid arthritis (RA) and other diseases. It is not known if there are characteristic changes in gene expression and immunoglobulin G glycosylation during therapy or in response to treatment. METHODS: Global gene expression profiles from peripheral blood mononuclear cells of 13 patients with RA and active disease at Week 0 (baseline) and Week 4 following treatment were obtained together with clinical measures, serum cytokine levels using ELISA, and the degree of galactosylation of the IgG N-glycan chains. Gene sets separating responders and nonresponders were tested using canonical variates analysis. This approach also revealed important gene groups and pathways that differentiate responders from nonresponders. RESULTS: Fifty-nine genes showed significant differences between baseline and Week 4 and thus correlated with treatment. Significantly, 4 genes determined responders after correction for multiple testing. Ten of the 12 genes with the most significant changes were validated using real-time quantitative polymerase chain reaction. An increase in the terminal galactose content of N-linked glycans of IgG was observed in responders versus nonresponders, as well as in treated samples versus samples obtained at baseline. CONCLUSION: As a preliminary report, gene expression changes as a result of tocilizumab therapy in RA were examined, and gene sets discriminating between responders and nonresponders were found and validated. A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA treated with tocilizumab was documented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".