The use of leukocytes’ secretome to individually target biological therapy in autoimmune arthritis: a case report
Bibliographic record
Abstract
BACKGROUND: Biological agents have allowed remarkable improvement in controlling autoimmune arthropathies, although none of the numerous biologics readily available represent a universal treatment standard. Moreover, classical and genetic predictors are currently unsatisfactory to predict individual response to a biologic, and the best treatment selection is still based on a trial-and-error approach. Here, we report a clinical case demonstrating the usefulness of examining the leukocytes' secretome of patients. We set up and standardized a protocol that examines a patient's immune responses to establish the secretome of the blood mononuclear leukocytes and personalize the biotherapy. CASE PRESENTATION: A 24-year-old woman was diagnosed with active early rheumatoid arthritis. The initial treatment regimen (prednisone, methotrexate, hydroxychloroquine, naproxen) was inefficient, as well as the anti-TNF adalimumab. The diagnosis was revised as possible rheumatoid arthritis-like psoriatic arthritis and adalimumab was replaced by abatacept (IgG1 Fc-CTLA-4) to no avail. Five years later, abatacept was replaced by the anti-IL-12/IL-23 ustekinumab with no objective control over the symptoms. The patient was thus enrolled in a prospective study based on the quantification of cytokines secreted by peripheral blood leukocytes stimulated with well-known immune activators of pattern recognition receptors and cytokine signalling. The results of this study revealed that plasma concentrations of cytokines were similar between the patient and healthy donors. In comparison to leukocytes from healthy donors, the patient's secretome showed a unique overproduction of IL-6. The anti-IL-6 receptor tocilizumab was, therefore, administered with a rapid improvement of her active psoriatic arthritis that remained dependent on low prednisone dosage. Clinical parameters progressively returned to normal levels and her quality of life was greatly improved, despite the major delay to begin the present personalized treatment. CONCLUSIONS: An efficient way to effectively treat patients with complex autoimmune arthropathies, and avoid irreversible disability, is to know their leukocytes' secretome to identify abnormally secreted cytokines and personalize their biotherapy, as exemplified by this case report.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".