Heparins are potent inhibitors of ectonucleotide pyrophosphatase/phospho-diesterase-1 (NPP1) – a promising target for the immunotherapy of cancer
Bibliographic record
Abstract
Introduction: Heparins, naturally occurring glycosaminoglycans, are widely used for thrombosis prevention. Upon application as anticoagulants in cancer patients, heparins were found to possess additional antitumor activities. Ectonucleotidases have recently been proposed as novel targets for cancer immunotherapy. Methods and results: In the present study, we discovered that heparin and its derivatives act as potent, selective, allosteric inhibitors of the poorly investigated ectonucleotidase NPP1 (nucleotide pyrophosphatase/phosphodiesterase-1, CD203a). Structure-activity relationships indicated that NPP1 inhibition could be separated from the compounds' antithrombotic effect. Moreover, unfractionated heparin (UFH) and different low molecular weight heparins (LMWHs) inhibited extracellular adenosine production by the NPP1-expressing glioma cell line U87 at therapeutically relevant concentrations. As a consequence, heparins inhibited the ability of U87 cell supernatants to induce CD4+ T cell differentiation into immunosuppressive Treg cells. Discussion: NPP1 inhibition likely contributes to the anti-cancer effects of heparins, and their specific optimization may lead to improved therapeutics for the immunotherapy of cancer.
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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".