LSD1 Inhibition Disrupts Super-Enhancer–Driven Oncogenic Transcriptional Programs in Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
The lysine demethylase LSD1 (also called KDM1A) plays important roles in promoting multiple malignancies including both hematologic cancers and solid tumors. LSD1 targets histone and nonhistone proteins and can function as a transcriptional corepressor or coactivator. LSD1 has been reported to act as a coactivator of androgen receptor (AR) in prostate cancer and to regulate the AR cistrome via demethylation of its pioneer factor FOXA1. A deeper understanding of the key oncogenic programs targeted by LSD1 could help stratify prostate cancer patients for treatment with LSD1 inhibitors, which are currently under clinical investigation. In this study, we performed transcriptomic profiling in an array of castration-resistant prostate cancer (CRPC) xenograft models that are sensitive to LSD1 inhibitor treatment. Impaired tumor growth by LSD1 inhibition was attributed to significantly decreased MYC signaling, and MYC was found to be a consistent target of LSD1. Moreover, LSD1 formed a network with BRD4 and FOXA1 and was enriched at super-enhancer regions exhibiting liquid-liquid phase separation. Combining LSD1 inhibitors with BET inhibitors exhibited strong synergy in disrupting the activities of multiple drivers in CRPC, thereby inducing significant growth repression of tumors. Importantly, the combination treatment showed superior effects than either inhibitor alone in disrupting a subset of newly identified CRPC-specific super-enhancers. These results provide mechanistic and therapeutic insights for cotargeting two key epigenetic factors and could be rapidly translated in the clinic for CRPC patients. SIGNIFICANCE: LSD1 drives prostate cancer progression by activating super-enhancer-mediated oncogenic programs, which can be targeted with the combination of LSD1 and BRD4 inhibitors to suppress the growth of CRPC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".