RB1-deficient prostate tumor growth and metastasis are vulnerable to ferroptosis induction via the E2F/ACSL4 axis
Bibliographic record
Abstract
Inactivation of the RB1 tumor suppressor gene is common in several types of therapy-resistant cancers, including metastatic castration-resistant prostate cancer, and predicts poor clinical outcomes. Effective therapeutic strategies against RB1-deficient cancers remain elusive. Here, we showed that RB1 loss/E2F activation sensitized cancer cells to ferroptosis, a form of regulated cell death driven by iron-dependent lipid peroxidation, by upregulating expression of ACSL4 and enriching ACSL4-dependent arachidonic acid-containing phospholipids, which are key components of ferroptosis execution. ACSL4 appeared to be a direct E2F target gene and was critical to RB1 loss-induced sensitization to ferroptosis. Importantly, using cell line-derived xenografts and genetically engineered tumor models, we demonstrated that induction of ferroptosis in vivo by JKE-1674, a highly selective and stable GPX4 inhibitor, blocked RB1-deficient prostate tumor growth and metastasis and led to improved survival of the mice. Thus, our findings uncover an RB/E2F/ACSL4 molecular axis that governs ferroptosis and also suggest a promising approach for the treatment of RB1-deficient malignancies.
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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".