Abstract A067: PRMT7 deficiency in T cells enhances immune response and fosters an anti-tumor microenvironment in melanoma
Bibliographic record
Abstract
Abstract Exploration in the field of epigenetics has unveiled the significance of protein arginine methyltransferases (PRMTs) in normal development and disease. Our research identified PRMT7 as a promising target for enhancing the efficacy of cancer immunotherapy in melanoma (Srour et al., 2022, Cell Reports 38(13):110582). To investigate if PRMT7 plays a role in the tumor microenvironment especially in tumor infiltrating lymphocytes (TILs), we bred PRMT7FL/FL mice with CD4-Cre mice (CKO) to generate PRMT7 deficient T cells. PRMT7 CKO mice exhibited a substantial increase in peripheral CD8+ but not CD4+ T cells. These CD8+ T cells had an enhanced T cell effector memory-like phenotype as assessed by CD44, CD62L expression. Interestingly, PRMT7 CKO mice exhibited reduced melanoma growth following subcutaneous injection of B16.F10 cells. Ex vivo stimulation of PRMT7 CKO CD8+ T cells with αCD3 and αCD28 resulted in enhanced proliferation compared to control CD8+ T cells. To replicate PRMT7 depletion observed in the CKO model, we developed novel VHL-ligand coupled PROTAC inhibitors targeting PRMT7. Notably, PRMT7-deficient CD8+ T cells, whether derived from CKO mice or wild-type mice treated with PRMT7 PROTACs, exhibited enhanced cytotoxic activity against melanoma cells in vitro. Furthermore, our data show that adoptive cell transfer (ACT) with PRMT7-deficient OT-I T cells improves tumor control, while ACT with wild-type OT-I effectors has a smaller effect. Transcriptomic analysis of PRMT7-deficient CD8+ T cells revealed multiple alternative splicing defects, along with upregulation of genes related to the NF-κB pathway. Our findings highlight a crucial role for PRMT7 in regulating CD8+ T cell function, reinforcing the need to therapeutically target PRMT7 for adoptive T cell immunotherapies and CAR T cell treatments. Citation Format: Nivine Srour, Yan Xiong, Zhenbao Yu, Theodore Papadopoulos, Kaixiu Luo, François Santinon, Dalia Barsyte-Lovejoy, Sonia Victoria delRincon, Jin Jian, Stéphane Richard. PRMT7 deficiency in T cells enhances immune response and fosters an anti-tumor microenvironment in melanoma [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A067.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".