Anti-CD25 Targeted Killing of Bicistronically Transduced Cells: A Novel Safety Mechanism Against Retroviral Genotoxicity
Bibliographic record
Abstract
Gene therapy for Fabry disease, a deficiency in alpha-galactosidase A (alpha-gal A) activity, has the potential to provide a cure for the disorder with a single treatment. Despite modifications to existing vectors, concerns have arisen regarding the risk of genotoxicity associated with the use of retroviruses. To address safety concerns, we propose that expression of a cell surface protein, human CD25 (huCD25) in a bicistronic format, with any therapeutic gene such as alpha-gal A can provide a target that can be used to kill transduced cells selectively should transformative events occur. We show that an anti-CD25 antibody and immunotoxin can specifically target and eliminate transduced leukemia cells expressing CD25. In a murine leukemia model, antibody treatment reduced tumor burden 32-fold and increased survival compared with untreated mice. Furthermore, after a bone marrow transplant of therapeutically transduced cells into Fabry mice, antibody treatment reduced the number of retrovirally transduced huCD25-expressing cells in the peripheral blood. A systemic loss of transduced cells with functional consequences was also evident in the liver and spleen. This proof-of-principle study demonstrates that a targeted antibody can reduce tumor burden and selectively clear bicistronically transduced hematopoietic cells that express a target antigen, thus acting as a built-in safety mechanism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".