Enhanced in vivo selection of bone marrow cells by retroviral-mediated coexpression of mutant O6-methylguanine-DNA-methyltransferase and HOXB4
Bibliographic record
Abstract
To attain therapeutic levels of gene-modified hematopoietic stem cells, it may be necessary in the majority of disorders to provide an in vivo selective advantage that facilitates the expansion of their numbers. A popular strategy to achieve in vivo selection has been to employ drug selection while coexpressing a transgene that conveys chemoresistance, such as O6-methylguanine-DNA-methyltransferase (MGMT). An alternate approach is to confer an enhanced proliferative potential upon gene-modified hematopoietic stem cells through the delivery of the homeobox transcription factor HOXB4. By developing a novel tricistronic retroviral vector, we have facilitated the simultaneous coexpression of a mutant version of MGMT and HOXB4 in retrovirally transduced bone marrow. Using an in vivo competitive repopulation assay, we demonstrate that primary bone marrow cells containing this construct show enhanced reconstitution following transplant and improved selection subsequent to chemotherapeutic challenge in comparison to cells expressing either HOXB4 or MGMT alone. This selection advantage was evident even when HOXB4/MGMT-coexpressing cells were infused along with a large excess of unmodified cells. We propose that this selection cassette may facilitate the in vivo expansion of gene-modified hematopoietic stem cells at a level in excess of previous strategies.
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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) | 0.002 | 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 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".