Circadian regulator REV-ERBα is a master regulator of tumor lineage plasticity and an effective therapeutic target
Bibliographic record
Abstract
Epigenetic and transcriptional dysregulation plays a fundamental role in tumor lineage plasticity (LP). However, the underlying mechanisms, especially for the initial events of LP development, are still poorly understood. Here, we report that in progression of prostate cancer from adenocarcinoma to treatment-induced neuroendocrine prostate cancer (t-NEPC), anti-androgen receptor (AR) signaling inhibitors (ARSIs) reprogram the function of circadian regulator/nuclear receptor REV-ERBα by switching its target gene programs from kinase signaling and metabolic programs to programs of LP, which includes neurogenesis, stem cell, and epithelial-mesenchymal transition as well as over fifteen LP drivers including POU3F2/BRN2, ASCL1, FOXA2, ONECUT2, and MYCN. Unexpectedly, REV-ERBα facilitates the chromatin occupancy of BRN2, ASCL1, and FOXA1 in their activation of LP programs, thus functioning as a master regulator of ARSI-induced LP driver network. Mechanistically, REV-ERBα induces chromatin accessibility and H3K27ac modification at promoters of LP genes through its recruitment of BRD4 and p300. Overexpression of REV-ERBα alone is sufficient to induce LP and neuroendocrine phenotype and confers resistance to ARSI in adenocarcinoma cells. Loss of REV-ERBα potently inhibits NEPC cell growth and abolishes the expression of LP drivers and gene programs. Pharmacological inhibition of REV-ERBα exhibits high potency in blocking the growth of NEPC tumors including patient-derived xenografts. Our findings reveal that therapy-induced LP development entails a coordinated induction of a network of LP drivers and that REV-ERBα is an unexpected master regulator of the network and a promising therapeutic target for treatment of advanced prostate cancer such as NEPC.
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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 | 0.001 |
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".