Abstract 6157: PAR2 inhibitors reduce resistance to immunotherapy against cancer
Bibliographic record
Abstract
Abstract A pan-cancer meta-analysis showed that Protease-activated receptor 2 (PAR2) is one of the genes most significantly associated with resistance to immune checkpoint blockade (ICB) and T cells dysfunction in cancer patients. PAR2 is also upregulated and associated with poor prognosis in several cancer types which makes it a promising therapeutic target in the fight against cancer. However, the potential of PAR2 inhibitors and their mechanism of action in immuno-oncology has not been determined yet. The efficacy of PAR2 knock out and a biased negative allosteric modulator (NAM) of PAR2, RD35-11, in monotherapy or in combination with an anti-PD1 was assessed in the MC38 model. Strikingly, the combination therapy significantly increased the number of complete responses compared with mice treated with immunotherapy alone. As MC38 cells do not express PAR2, this result indicated that RD35-11 promote tumor regression by changing the tumor microenvironment. To investigate this hypothesis, the effect of PAR2 inhibition alone or in combination with an anti-PD1 on tumor-infiltrating immune cells was assessed in vivo by flow cytometry, cytokine measurement, bulk and single cell RNA sequencing. Results obtained showed that a PAR2 NAM reduced the level of immune-regulatory cells (M2 macrophages) and cytokines (e.g. IL10) while promoting antigen presenting cells and pathways. Ex-vivo experiments further demonstrated that PAR2 inhibition reduces IL10 secretion by macrophages while enhancing antigen cross-presentation by dendritic cells. Taken together, our data clearly demonstrate that PAR2 NAM can alleviate resistance to immune checkpoint blockade by reducing macrophage-induced immunosuppression and promoting antigen presentation. Domain Therapeutics has identified a novel PAR2 biased NAM, DT-9045, orally available and with clear competitive advantages. IND-enabling studies are currently ongoing to bring this new hope for cancer patients to the clinic. Citation Format: Thibaut Brugat, Maleck Kadiri, Samya Aouad, David Allard, Camille Fuselier, Emma Skora, Antoine Mousson, Aurélie Janvier, Luc Baron, Christel Franchet, Anne-Laure Blayo, Stephan Schann, John Stagg. PAR2 inhibitors reduce resistance to immunotherapy against cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6157.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".