Anti-CD73 antibody therapy inhibits breast tumor growth and metastasis
Bibliographic record
Abstract
Extracellular adenosine is a potent immunosuppressor that accumulates during tumor growth. We performed proof-of-concept studies investigating the therapeutic potential and mechanism of action of monoclonal antibody (mAb)-based therapy against CD73, an ecto-enzyme overexpressed on breast-cancer cells that catalyzes the dephosphorylation of adenosine monophosphates into adenosine. We showed that anti-CD73 mAb therapy significantly delayed primary 4T1.2 and E0771 tumor growth in immune-competent mice and significantly inhibited the development of spontaneous 4T1.2 lung metastases. Notably, anti-CD73 mAb therapy was essentially dependent on the induction of adaptive anti-tumor immune responses. Knockdown of CD73 in 4T1.2 tumor cells confirmed the tumor-promoting effects of CD73. In addition to its immunosuppressive effect, CD73 enhanced tumor-cell chemotaxis, suggesting a role for CD73-derived adenosine in tumor metastasis. Accordingly, administration of adenosine-5'-N-ethylcarboxamide to tumor-bearing mice significantly enhanced spontaneous 4T1.2 lung metastasis. Using selective adenosine-receptor antagonists, we showed that activation of A2B adenosine receptors promoted 4T1.2 tumor-cell chemotaxis in vitro and metastasis in vivo. In conclusion, our study identified tumor-derived CD73 as a mechanism of tumor immune escape and tumor metastasis, and it also established the proof of concept that targeted therapy against CD73 can trigger adaptive anti-tumor immunity and inhibit metastasis of breast 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".