RB1 and p53 at the crossroad of EMT and triple-negative breast cancer
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a heterogeneous disease that includes Basal-like and Claudin-low tumors. The Claudin-low tumors are enriched for features associated with epithelial-to-mesenchymal transition (EMT) and possibly for tumor initiating cells. Primary TNBCs respond relatively well to conventional chemotherapy; however, metastatic disease is virtually incurable. Thus, there is a great interest in identifying specific therapeutic targets for TNBC. The tumor suppressor RB1 is frequently lost in Basal-like breast cancer. To test for a causative role of RB1 gene loss in BC and for its effect on specific subtypes, we deleted mouse Rb in mammary stem/bipotent progenitor cells. This led to diverse mammary tumors including TNBC, with a subset of the latter containing p53 mutations and exhibiting features of Basal-like BC or EMT. Combined mutation of Rb and p53 in mammary stem/bipotent progenitors induced EMT type tumors. Here, we review our findings and those of others, which connect Rb and p53 to EMT in TNBC. Furthermore, we discuss how by understanding this circuit and its vulnerabilities, we may identify novel therapy for TNBC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".