Harnessing Induced Essentiality: Targeting Carbonic Anhydrase IX and Angiogenesis Reduces Lung Metastasis of Triple Negative Breast Cancer Xenografts
Bibliographic record
Abstract
Triple Negative Breast Cancer (TNBC) is aggressive, metastatic and drug-resistant, limiting the spectrum of effective therapeutic options for breast cancer patients. To date, anti-angiogenic agents have had limited success in the treatment of systemic breast cancer, possibly due to the exacerbation of tumor hypoxia and increased metastasis. Hypoxia drives increased expression of downstream effectors, including Carbonic Anhydrase IX (CAIX), a critical functional component of the pro-survival machinery required by hypoxic tumor cells. Here, we used the highly metastatic, CAIX-positive MDA-MB-231 LM2-4 orthotopic model of TNBC to investigate whether combinatorial targeting of CAIX and angiogenesis impacts tumor growth and metastasis in vivo to improve efficacy. The administration of a small molecule inhibitor of CAIX, SLC-0111, significantly reduced overall metastatic burden, whereas exposure to sunitinib increased hypoxia and CAIX expression in primary tumors, and failed to inhibit metastasis. The administration of SLC-0111 significantly decreased primary tumor vascular density and permeability, and reduced metastasis to the lung and liver. Furthermore, combining sunitinib and SLC-0111 significantly reduced both primary tumor growth and sunitinib-induced metastasis to the lung. Our findings suggest that targeting angiogenesis and hypoxia effectors in combination holds promise as a novel rational strategy for the effective treatment of patients with TNBC.
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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".