Targeting Hypoxia-Induced Carbonic Anhydrase IX Enhances Immune-Checkpoint Blockade Locally and Systemically
Bibliographic record
Abstract
Abstract Treatment strategies involving immune-checkpoint blockade (ICB) have significantly improved survival for a subset of patients across a broad spectrum of advanced solid cancers. Despite this, considerable room for improving response rates remains. The tumor microenvironment (TME) is a hurdle to immune function, as the altered metabolism-related acidic microenvironment of solid tumors decreases immune activity. Here, we determined that expression of the hypoxia-induced, cell-surface pH regulatory enzyme carbonic anhydrase IX (CAIX) is associated with worse overall survival in a cohort of 449 patients with melanoma. We found that targeting CAIX with the small-molecule SLC-0111 reduced glycolytic metabolism of tumor cells and extracellular acidification, resulting in increased immune cell killing. SLC-0111 treatment in combination with immune-checkpoint inhibitors led to the sensitization of tumors to ICB, which led to an enhanced Th1 response, decreased tumor growth, and reduced metastasis. We identified that increased expression of CA9 is associated with a reduced Th1 response in metastatic melanoma and basal-like breast cancer TCGA cohorts. These data suggest that targeting CAIX in the TME in combination with ICB is a potential therapeutic strategy for enhancing response and survival in patients with hypoxic solid malignancies.
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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".