Targeting <scp>PRAME</scp> directly or via <scp>EZH2</scp> inhibition overcomes retinoid resistance and represents a novel therapy for keratinocyte carcinoma
Bibliographic record
Abstract
Retinoids have demonstrated efficacy as preventative/treatment agents for keratinocyte carcinomas (KCs): basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (SCC). However, retinoid resistance mechanisms limit the efficacy of these compounds. A subset of KCs expresses Preferentially Expressed Antigen in Melanoma (PRAME): a retinoid signaling corepressor. PRAME is proposed to repress retinoid signaling by guiding enhancer of zeste homolog 2 (EZH2) to retinoic acid response elements (RARE) in promoters. We investigated the effects of PRAME on KC pathogenesis and retinoid response. High-PRAME expression in tumors was negatively correlated with epidermal differentiation gene signatures. PRAME overexpression downregulated epidermal differentiation gene signatures and impaired differentiation in 3D culture. PRAME overexpression attenuated retinoid-induced RARE activation, growth suppression, and differentiation responses. Conversely, low-PRAME tumors and PRAME-depleted KC cells demonstrated enriched epidermal differentiation gene signatures. PRAME downregulation restored retinoid-induced RARE activation, growth suppression, keratinization in SCC, and cell death signaling in BCC. Furthermore, combined retinoid and EZH2 inhibitor treatment augmented RARE activation and suppressed PRAME-expressing KC cell growth. Hence, PRAME confers retinoid resistance in KC, which may be overcome by EZH2 inhibition.
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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) | 0.001 | 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".