CPSF6 is a Clinically Relevant Breast Cancer Vulnerability Target
Bibliographic record
Abstract
Breast cancer represents a major health challenge. The majority of breast cancer deaths are due to cancer progression/recurrence for which no efficient therapies exist. Aggressive breast cancers are characterized by loss of cellular differentiation. Defining molecular mechanisms/targets contributing to cancer aggressiveness is needed to guide the design of new screening and targeted treatments. Here, we describe a novel tumor promoting function for the Cleavage and Polyadenylation Factor-6 (CPSF6). Importantly, aggressive breast cancer cells of luminal B, HER2-overexpressing and triple negative subtypes show dependency on CPSF6 for viability and tumorigenic capacity. Mechanistically, we found CPSF6 to interact with components of the A-to-I RNA editing machinery, paraspeckles and ADAR1 enzyme, and to be required for their physical integrity. Clinically, we found CPSF6 and all core paraspeckles proteins to be overexpressed in human breast cancer cases and their expression to correlate with poor patient outcomes. Finally, we found prolactin, a key mammary differentiation factor, to suppress CPSF6/RNA editing activity. Together, this study revealed CPSF6 as a molecular target with clinical relevance for prognosis and therapy in breast cancer.
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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) | 0.002 | 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".