244. T CELL SIGNATURES PREDICTING RESPONSE TO BIOLOGICS IN RHEUMATOID ARTHRITIS: TOWARDS DEVELOPMENT OF A 34 PARAMETER MASS CYTOMETRY PANEL
Bibliographic record
Abstract
Background: Treating the inflammation of rheumatoid arthritis (RA) early leads to improved patient outcomes. However, 30% will fail to respond to the first biologic drug tried and currently there is no way to predict which drug will be effective. This project aims to identify different RA immunophenotypes in order to predict treatment response to biologic drugs. Methods: RA patients with active disease (DAS28 >5.1) who were due to commence treatment with a biologic drug were included. PBMCs were stimulated with PMA and ionomycin or αCD3/αCD28 beads and analysed using 16-parameter flow cytometry for: a) T and B cell subsets; and b) Intracellular cytokine quantification (IFNγ, TNFα, IL-13, IL-10, IL-17A, GM-CSF). Results: 7 RA patients and 9 healthy control (HC) samples were tested. There were large differences in the proportions of both IFNγ+ T cells (range 8% to 65% in RA and 18% to 49% in controls) and IL-17A+ T cells (range 1.2% to 12%) within and between RA and HC. The proportion of IFNγ+ T cells (Th1) did not correlate with that of IL-17A+ T cells (Th17), leading to the generation of 4 immunophenotypes: Th1, Th17, double-hi, and low, depending on cytokine expression. 3 month follow-up data were available for 6 RA patients. 5 had started an anti-TNF and 4 had a good EULAR response, with two of those having a double-hi phenotype. One anti-TNF non-responder had the Th17 phenotype.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".