A rational mouse model to detect on-target, off-tumor CAR T cell toxicity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Off-tumor targeting of human antigens is difficult to predict in preclinical animal studies and can lead to serious adverse effects in patients. To address this, we developed a mouse model with stable and tunable human Her2 (hHer2) expression on normal hepatic tissue and compared toxicity between affinity-tuned Her2 chimeric antigen receptor T cells (CARTs). In mice with hHer2-high livers, both the high-affinity (HA) and low-affinity (LA) CARTs caused lethal liver damage due to immunotoxicity. In mice with hHer2-low livers, LA-CARTs exhibited less liver damage and lower systemic levels of IFN-γ than HA-CARTs. We then compared affinity-tuned CARTs for their ability to control a hHer2-positive tumor xenograft in our model. Surprisingly, the LA-CARTs outperformed the HA-CARTs with superior antitumor efficacy in vivo. We hypothesized that this was due, in part, to T cell trafficking differences between LA and HA-CARTs and found that the LA-CARTs migrated out of the liver and infiltrated the tumor sooner than the HA-CARTs. These findings highlight the importance of T cell targeting in reducing toxicity of normal tissue and also in preventing off-tumor sequestration of CARTs, which reduces their therapeutic potency. Our model may be useful to evaluate various CARTs that have conditional expression of more than 1 single-chain variable fragment (scFv).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it