Abstract LT021: Targeting AXL favors an anti-tumorigenic tumor microenvironment that enhances immunotherapy responses by decreasing HIF-1a levels in cancer cells
Bibliographic record
Abstract
Abstract Breast cancer-related deaths are predominantly associated to complications arising from metastases. Hypoxia is an important biological process that fuels metastasis and contributes to deregulate the tumor microenvironment (TME) of solid tumors. Hypoxia also promotes resistance to therapies by a plethora of mechanisms including enhancing tumoral angiogenesis, metabolic reprogramming, epithelial-to-mesenchymal transition (EMT) and immune evasion. The expression of the receptor tyrosine kinase AXL, a hypoxia-induced gene, is associated with poor clinical prognosis and metastasis in various cancers, including breast cancer. Here, we hypothesize that genetic or pharmacological interference of AXL may alter hypoxic responses and associated modulation of the TME offering an ideal setting for immunotherapy. In a HER2 mouse model of breast cancer (MMTV-NIC), we found that the mammary gland epithelial cells specific deletion of AXL led to a reduction of lung metastases and improved the TME by reducing the hypoxic response in tumor cells. When breast cancer cells were subjected to hypoxic conditions, we found that AXL inhibition reduced the levels of the hypoxia transcription factor HIF-1a which resulted in an altered hypoxic response. Specifically, AXL deletion led to a reduction of hypoxia-induced EMT and invasion, and a reduction of the secretion of key cytokines for macrophages recruitment and polarization. We demonstrate in vivo that AXL pharmacological inhibition in the MMTV-NIC breast cancer model generated an anti-inflammatory TME that enhanced an anti-PD-1 immune checkpoint blockade response and decreased the metastatic burden. Collectively, these results suggest that targeting AXL could be a powerful approach to improve immunotherapy response by generating an anti-tumoral microenvironment therefore limiting the metastatic burden of breast cancer. Citation Format: Marie-Anne Goyette, Jean-Philippe Gratton, Jean-François Côté. Targeting AXL favors an anti-tumorigenic tumor microenvironment that enhances immunotherapy responses by decreasing HIF-1a levels in cancer cells [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT021.
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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 | 0.000 |
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".