PARP7 and aryl hydrocarbon receptor differentially regulate mammary cancer cell proliferation and STING-induced type I interferon signalling
Bibliographic record
Abstract
PURPOSE: PARP7 is a negative regulator of type I interferon (IFN-I) and aryl hydrocarbon receptor (AHR) signalling and has important roles in cell proliferation and antitumor immunity. Recently, several cancer cell lines have been reported to be sensitive to the antiproliferative effect of PARP7 inhibition by RBN2397; however, the roles of AHR and IFN-I signalling in this effect are not fully understood. METHODS: Murine mammary cancer cells were treated with AHR ligands, RBN2397 and with the stimulator of interferon genes (STING) agonist, DMXAA. The impact of ligand treatments on AHR and IFN-I signalling and cell proliferation was determined. RESULTS: RBN2397 enhanced AHR ligand signalling and STING-induced IFN-I responses in both cell lines. Py8119 but not Py230, 4T1 or EO771 cells were sensitive to the antiproliferative effects of RBN2397. In agreement with FOS-related antigen 1 (FOSL1) being required for sensitivity to RBN2397, Py8119 but not Py230 cells expressed FOSL1. However, RBN2397 insensitive 4T1 and EO771 cell lines also expressed FOSL1, suggesting that the role of FOSL1 in RBN2397-mediated growth inhibition exhibits cell line specificity. In Py8119 cells, RBN2397 induced apoptosis which was independent of AHR ligand treatment and DMXAA-induced STING activation. Although Py230 cells were resistant to the antiproliferative effects RBN2397 alone, combined treatment of DMXAA with RBN2397 reduced their proliferation, which was further reduced by AHR loss or its inhibition. CONCLUSION: These findings highlight the complexity of the interplay among PARP7, AHR and STING-induced IFN signalling in regulating cancer cell proliferation but also suggest that for some cell lines STING activation might increase their sensitivity to the anti-proliferative effects of RBN2397.
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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) | 0.004 | 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".