B-cell depletion and remissions of malignancy along with cytokine-associated toxicity in a clinical trial of anti-CD19 chimeric-antigen-receptor–transduced T cells
Bibliographic record
Abstract
We conducted a clinical trial to assess adoptive transfer of T cells genetically modified to express an anti-CD19 chimeric Ag receptor (CAR). Our clinical protocol consisted of chemotherapy followed by an infusion of anti-CD19-CAR-transduced T cells and a course of IL-2. Six of the 8 patients treated on our protocol obtained remissions of their advanced, progressive B-cell malignancies. Four of the 8 patients treated on the protocol had long-term depletion of normal polyclonal CD19(+) B-lineage cells. Cells containing the anti-CD19 CAR gene were detected in the blood of all patients. Four of the 8 treated patients had prominent elevations in serum levels of the inflammatory cytokines IFNγ and TNF. The severity of acute toxicities experienced by the patients correlated with serum IFNγ and TNF levels. The infused anti-CD19-CAR-transduced T cells were a possible source of these inflammatory cytokines because we demonstrated peripheral blood T cells that produced TNF and IFNγ ex vivo in a CD19-specific manner after anti-CD19-CAR-transduced T-cell infusions. Anti-CD19-CAR-transduced T cells have great promise to improve the treatment of B-cell malignancies because of a potent ability to eradicate CD19(+) cells in vivo; however, reversible cytokine-associated toxicities occurred after CAR-transduced T-cell infusions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".