Long-term exposure to BAY2416964 reduces proliferation, migration and recapitulates transcriptional changes induced by AHR loss in PyMT-induced mammary tumor cells
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AHR) is a ligand activated transcription factor which in certain cancer types drives pro-survival processes that facilitate tumorigenesis, malignant cell migration, invasion, and metastasis. Much of AHR’s pro-tumorigenic action is due to its activation by the oncometabolite, kynurenine. Because of this AHR antagonists are being actively investigated as new anti-tumor therapy. In this study we compared the effects of treatment with the AHR antagonists, BAY2416964 and GNF351, to that of AHR knockout in PyMT murine mammary cancer cells. BAY2416964 and GNF351 effectively inhibited kynurenine-dependent increases in Cyp1a1 and Cyp1b1 mRNA levels. CRISPR/Cas9-generated PyMT AhrKO cells exhibited reduced cell proliferation compared with controls, but treatment with 1 μM BAY2416964 for 96 h had no effect on the proliferation of wildtype cells. To further examine the differences between AHR knockout and short term BAY2416964, we generated long-term BAY2416964 (LT-BAY) cells by exposing wildtype cells to 1 μM BAY2416964 for at least 6 weeks. Similar to AhrKO cells, LT-BAY cells exhibited reduced cell proliferation and migration compared with wildtype cells. No differentially expressed genes (DEGs) were identified in wildtype cells exposed to 1 μM BAY2416964 for 24 h; however, 46.4% of DEGs overlapped between AhrKO and LT-BAY cells including gene regulated cell proliferation. Our data reveal long-term pharmacological inhibition of AHR by BAY2416964 closely resembles AHR loss in a mouse model of breast cancer.
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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".