Targeting Axl favors an antitumorigenic microenvironment that enhances immunotherapy responses by decreasing Hif-1α levels
Bibliographic record
Abstract
Hypoxia is an important phenomenon in solid tumors that contributes to metastasis, tumor microenvironment (TME) deregulation, and resistance to therapies. The receptor tyrosine kinase AXL is an HIF target, but its roles during hypoxic stress leading to the TME deregulation are not well defined. We report here that the mammary gland–specific deletion of Axl in a HER2 + mouse model of breast cancer leads to a normalization of the blood vessels, a proinflammatory TME, and a reduction of lung metastases by dampening the hypoxic response in tumor cells. During hypoxia, interfering with AXL reduces HIF-1α levels altering the hypoxic response leading to a reduction of hypoxia-induced epithelial-to-mesenchymal transition (EMT), invasion, and production of key cytokines for macrophages behaviors. These observations suggest that inhibition of Axl generates a suitable setting to increase immunotherapy. Accordingly, combining pharmacological inhibition of Axl with anti–PD-1 in a preclinical model of HER2 + breast cancer reduces the primary tumor and metastatic burdens, suggesting a potential therapeutic approach to manage HER2 + patients whose tumors present high hypoxic features.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".