Apelin inhibition prevents resistance and metastasis associated with anti‐angiogenic therapy
Bibliographic record
Abstract
Angiogenesis is a hallmark of cancer, promoting growth and metastasis. Anti‐angiogenic treatment has limited efficacy due to therapy‐induced blood vessel alterations, often followed by local hypoxia, tumor adaptation, progression, and metastasis. It is therefore paramount to overcome therapy‐induced resistance. We show that Apelin inhibition potently remodels the tumor microenvironment, reducing angiogenesis, and effectively blunting tumor growth. Functionally, targeting Apelin improves vessel function and reduces polymorphonuclear myeloid‐derived suppressor cell infiltration. Importantly, in mammary and lung cancer, Apelin prevents resistance to anti‐angiogenic receptor tyrosine kinase (RTK) inhibitor therapy, reducing growth and angiogenesis in lung and breast cancer models without increased hypoxia in the tumor microenvironment. Apelin blockage also prevents RTK inhibitor‐induced metastases, and high Apelin levels correlate with poor prognosis of anti‐angiogenic therapy patients. These data identify a druggable anti‐angiogenic drug target that reduces tumor blood vessel densities and normalizes the tumor vasculature to decrease metastases. Apelin is an angiogenic peptide implicated in embryonic and tumor angiogenesis. This study highlights Apelin targeting as a cancer therapy alone or in combination with current anti‐angiogenic therapies to reduce tumour growth and improve vessel structure and functionality, and thus survival. Apelin is an angiogenic peptide implicated in embryonic and tumor angiogenesis. This study highlights Apelin targeting as a cancer therapy alone or in combination with current anti‐angiogenic therapies to reduce tumour growth and improve vessel structure and functionality, and thus survival.
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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".