Harnessing the potential of CAR-T cell in lupus treatment: From theory to practice
Bibliographic record
Abstract
Systemic Lupus Erythematosus (SLE) is a rare, heterogeneous, potentially life-threatening autoimmune disease. Presence of kidney or other major organ (brain, heart or lung) involvement are predictors of poor outcome and in a subset of patients resistant to 1st or 2nd line conventional treatment. The 10-year mortality remains around 10-15 %. Chimeric Antigen Receptors (CAR) are molecules that allow to redirect the engineered immune cells towards specific target antigens and to simultaneously boost their activation. Following breakthrough results observed in the treatment of hematological malignancies, conventional CAR T-cell therapy has recently been applied to refractory SLE patients. Compared to the use of monoclonal antibodies, anti-CD19 CAR T-cells allow to achieve deeper depletion of autoreactive B cells, notably at site of inflamed tissues and lymphoid organs (i.e. lymph node), to suppress interferon signature and to restore the immune tolerance with the reemergence of naïve B-cells with a new repertoire. All clinical data reported in SLE patients so far showed that autologous anti-CD19 CAR T-cell treatment allowed impressive short- and longer-term resolution of lupus nephritis and other severe disease-related manifestations, without major toxicities and only mild cytokine-release syndrome. These clinical effects persisted after B-cell reconstitution and were associated with normalization of double-stranded DNA antibodies and complement levels in drug-free patients until three years after the procedure. Overall, these pioneering experiences show unique clinical and immunological response to CAR T-cell therapy in SLE, and the need for extended follow-up to determine its long-term efficacy.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".