Unfulfilled Promise of Endostatin in a Gene Therapy-Xenotransplant Model of Human Acute Lymphocytic Leukemia
Bibliographic record
Abstract
Retroviral transduction of hematopoietic stem cells (HSCs) offers an attractive strategy for treating malignancies that home to the marrow. This approach should therefore be of interest for evaluating the therapeutic activity of anti-angiogenic agents on hematopoietic malignancies whose growth has been associated with enhanced angiogenesis. A variety of studies have indicated endostatin to be a potent anti-angiogenic agent both in vitro and in vivo, and a human malignancy that might be sensitive to endostatin is human B-lineage acute lymphoblastic leukemia (B-ALL). The demonstrated ability of human B-ALL cells to engraft the marrow of immunodeficient mice suggested the potential of this system for testing an endostatin delivery strategy using co-transplanted non-obese diabetic-scid/scid (NOD/SCID) HSCs engineered to express endostatin. Here we show that, in spite of their mutant scid gene, NOD/SCID HSCs can be transduced with an endostatin-encoding retrovirus at efficiencies that result in a several-fold increase in endostatin serum levels in transplanted recipients. However, this did not alter the regrowth of co-transplanted human B-ALL blasts. These findings validate this gene transfer approach for investigating effects of novel therapeutics on primary human malignant cells that engraft NOD/SCID mice and question the utility of native endostatin for controlling human B-ALL in vivo.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".