Loss of the Notch effector RBPJ promotes tumorigenesis
Bibliographic record
Abstract
Aberrant Notch activity is oncogenic in several malignancies, but it is unclear how expression or function of downstream elements in the Notch pathway affects tumor growth. Transcriptional regulation by Notch is dependent on interaction with the DNA-binding transcriptional repressor, RBPJ, and consequent derepression or activation of associated gene promoters. We show here that RBPJ is frequently depleted in human tumors. Depletion of RBPJ in human cancer cell lines xenografted into immunodeficient mice resulted in activation of canonical Notch target genes, and accelerated tumor growth secondary to reduced cell death. Global analysis of activated regions of the genome, as defined by differential acetylation of histone H4 (H4ac), revealed that the cell death pathway was significantly dysregulated in RBPJ-depleted tumors. Analysis of transcription factor binding data identified several transcriptional activators that bind promoters with differential H4ac in RBPJ-depleted cells. Functional studies demonstrated that NF-κB and MYC were essential for survival of RBPJ-depleted cells. Thus, loss of RBPJ derepresses target gene promoters, allowing Notch-independent activation by alternate transcription factors that promote tumorigenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".