Synergy between all-trans retinoic acid and tumor necrosis factor pathways in acute leukemia cells
Bibliographic record
Abstract
The nuclear receptor ligand all-trans retinoic acid (ATRA) causes dramatic terminal differentiation of acute promyelocytic leukemia (APL) cells in vitro and in patients, but it is less active in other malignancies. However, downstream mediators of the effects of ATRA are not well understood. We used a cDNA microarray to search for ATRA-regulated genes in the APL cell line NB4 and found that ATRA regulated several members of the tumor necrosis factor (TNF) pathway. Here we show that TNF can synergize with ATRA to induce differentiation, showing monocytic characteristics more typical of differentiation mediated by TNF than by ATRA. ATRA and TNF can also induce differentiation of the non-APL cell line U937. Underlying this response was an increase in TNF-induced nuclear factor-kappaB (NF-kappaB) DNA binding within 2 hours in the presence of ATRA and activation of NF-kappaB DNA binding and transcriptional activity in response to ATRA alone within 48 hours of ATRA treatment. Furthermore, we found a synergistic induction of the NF-kappaB target genes BCL-3, Dif-2, and TNF receptor 2 (TNFR2) in response to the combination of TNF and ATRA. These genes have been previously shown to play a role in TNF signaling, and amplification of such genes may represent a mechanism whereby TNF and ATRA can act synergistically. We propose that ATRA can prime cancer cells for differentiation triggered by TNF and suggest that targeting the TNF pathway in combination with ATRA may represent a novel route to treat leukemias.
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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) | 0.001 | 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 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".