Pre-treatment T-cell subsets associate with fingolimod treatment responsiveness in multiple sclerosis
Bibliographic record
Abstract
Abstract Biomarkers predicting fingolimod (FTY) treatment response in relapsing-remitting multiple sclerosis (RRMS) are lacking. Here, we performed extensive functional immunophenotyping using multiparametric flow cytometry to examine peripheral immune changes under FTY treatment and explore biomarkers of FTY treatment response. From among 135 RRMS patients who initiated FTY in a 2-year multicentre observational study, 36 were classified as ‘Active’ or ‘Stable’ based on clinical and/or radiological activity on-treatment. Flow cytometric analysis of immune cell subsets was performed on pre- and on-treatment peripheral blood mononuclear cells (PBMC) samples. Decreased absolute counts of B cells and most T-cell subsets were seen on-treatment. Senescent CD8 + T cells, CD56 + T cells, CD56dim natural killer cells, monocytes and dendritic cells were not reduced in number and hence relatively increased in frequency on-treatment. An unbiased multiparametric and traditional manual analysis of T-cell subsets suggested a higher pre-treatment frequency of CD4 + central memory T cells (TCM) in patients who were subsequently Active versus Stable on-treatment. Lower pre-treatment terminally differentiated effector memory (TEMRA) cell frequencies were also seen in the subsequently Active cohort. Together, our data highlight differential effects of FTY on peripheral immune cell subsets and suggest that pre-treatment T-cell subset frequencies may have value in predicting FTY treatment response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".