PRMT7 ablation stimulates anti-tumor immunity and sensitizes melanoma to immune checkpoint blockade
Bibliographic record
Abstract
Abstract Despite the success of immune checkpoint inhibitor (ICI) therapy in different cancers, resistance and relapses are frequent. Thus, combination therapies are expected to enhance response rates and overcome resistance to ICIs. Herein, we report that combining protein arginine methyltransferase 7 (PRMT7) inhibition with ICIs triggers a strong anti-tumor T cell immunity and restrains tumor growth in vivo by increasing tumor immune cell infiltration. Consistently, TCGA database analysis showed an inverse correlation between PRMT7 expression and T cell infiltration in human melanomas. Mechanistically, we show that PRMT7 has a two-prong effect on melanoma tumor immunity. On one hand, it serves as a coactivator of IRF-1 for PD -L1 expression by upregulating promoter H4R3me2s levels in melanoma cells. Next, PRMT7 prevents repetitive element expression to avoid intracellular dsRNA accumulation or ‘viral mimicry’. PRMT7 deletion resulted in increased endogenous retroviral elements (ERVs), dsRNA, and genes implicated in interferon activation, antigen presentation and chemokine signaling. Our findings identify PRMT7 as factor used by melanoma to evade anti-tumor immunity and define the therapeutic potential of PRMT7 alone or in combination with PD-(L)1 blockade to enhance ICI efficiency.
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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".