A Cross-Reactive Small Protein Binding Domain Provides a Model to Study Off-Tumor CAR-T Cell Toxicity
Bibliographic record
Abstract
Tumor-targeted chimeric antigen receptor (CAR)-engineered T lymphocytes (CAR-T cells) have demonstrated striking clinical success, but their use has been associated with a constellation of toxicities. A better understanding of the pathogenesis of these toxicities is required to improve the safety profile of CAR-T cells. Herein, we describe a xenograft model of off-tumor CAR-T cell-associated toxicity. Human CAR-T cells targeted against HER2 using a small-protein binding domain induced acute, dose-dependent toxicities in mice. The inclusion of a CD28 or 4-1BB co-stimulatory domain in the CAR was required to produce toxicity; however, co-stimulation through CD28 was most toxic on a per-cell basis. CAR-T cell activation in the lungs and heart was associated with a systemic cytokine storm. The severity of observed toxicities was dependent upon the peripheral blood mononuclear cell (PBMC) donor used as a T cell source and paralleled the CD4 + -to-CD8 + T cell ratio in the adoptive transfer product. CD4 + CAR-T cells were determined to be the primary contributors to CAR-T cell-associated toxicity. However, donor-specific differences persisted after infusion of a purified CD4 + CAR-T cell product, indicating a role for additional variables. This work highlights the contributions of CAR-T cell-intrinsic variables to the pathogenesis of off-tumor toxicity.
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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 | 0.000 |
| 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 | 0.001 |
| 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".