Sequential Transcription Factor Targeting for Diffuse Large B-Cell Lymphomas
Bibliographic record
Abstract
Transcription factors play a central role in malignant transformation by activating or repressing waves of downstream target genes. Therapeutic targeting of transcription factors can reprogram cancer cells to lose their advantages in growth and survival. The BCL6 transcriptional repressor plays a central role in the pathogenesis of diffuse large B-cell lymphomas (DLBCL) and controls downstream checkpoints, including the p53 tumor suppressor gene. We report that a specific inhibitor of BCL6 called BPI can trigger a p53 response in DLBCL cells. This was partially due to induction of p53 activity and partially due to relief of direct repression by BCL6 of p53 target genes. BPI could thus induce a p53-like response even in the presence of mutant p53. Moreover, sequential BCL6 peptide inhibitors followed by p53 peptide or small-molecule activators provided a more powerful antilymphoma effect than either treatment alone by maximally restoring p53 target gene expression. Therefore, tandem targeting of the overlapping BCL6 and p53 transcriptional programs can correct aberrant survival pathways in DLBCL and might provide an effective therapeutic approach to lymphoma therapy.
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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) | 0.003 | 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".