Combined effect of all-trans retinoic acid and arsenic trioxide in acute promyelocytic leukemia cells in vitro and in vivo
Bibliographic record
Abstract
All-trans retinoic acid (tRA) and arsenic trioxide (As(2)O(3)) induce non-cross-resistant complete clinical remission in patients with acute promyelocytic leukemia with t(15;17) translocation and target PML-RARalpha, the leukemogenic protein, by different pathways suggesting a possible therapeutic synergism. To evaluate this possibility, this study examined the effect of As(2)O(3) on tRA-induced differentiation and, conversely, the effect of tRA on As(2)O(3)-induced apoptosis. As(2)O(3) at subapoptotic concentrations (0.5 microM) decreased tRA-induced differentiation in NB4 cells but synergized with atRA to induce differentiation in tRA-resistant NB4 subclones MR-2 and R4 cells as measured by nitroblue tetrazolium reduction and tRA-inducible genes (TTGII, RARbeta, RIG-E). tRA cleaved PML-RARalpha into distinct fragments in NB4 but not in tRA-resistant MR-2 or R4 cells, whereas As(2)O(3) completely degraded PML-RARalpha in all 3 cell lines. As(2)O(3)-induced apoptosis was decreased by tRA pretreatment of NB4 cells but not of R4 cells and was associated with a strong induction of Bfl-1/A1 expression, a Bcl-2 protein family member. Severe combined immunodeficient mice bearing NB4 cells showed an additive survival effect after sequential treatment, but a toxic effect was observed after simultaneous treatment with tRA and As(2)O(3). These data suggest that combined As(2)O(3) and tRA treatment may be more effective than single agents in tRA-resistant patients. Although in vitro data do not always translate to in vivo response, toxicity and potential drug antagonism may be diminished by decreasing the concentration of As(2)O(3) when given at the same time with therapeutic levels of tRA.
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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".