CD73-Deficient Mice Have Increased Antitumor Immunity and Are Resistant to Experimental Metastasis
Bibliographic record
Abstract
CD73 is a cell-surface enzyme that suppresses immune responses by producing extracellular adenosine. In this study, we employed CD73 gene-targeted mice to investigate the role of host-derived CD73 on antitumor immunity and tumor cell metastasis. We found that CD73 ablation significantly suppressed the growth of ovalbumin-expressing MC38 colon cancer, EG7 lymphoma, AT-3 mammary tumors, and B16F10 melanoma. The protective effect of CD73 deficiency on primary tumors was dependent on CD8(+) T cells and associated with an increased frequency of antigen-specific CD8(+) T cells in peripheral blood and tumors and increased antigen-specific IFN-γ production. Replicate studies in bone marrow chimeras established that both hematopoietic and nonhematopoietic expression of CD73 was important to promote tumor immune escape. Using adoptive reconstitution of T regulatory cell (Treg)-depleted DEREG (depletion of regulatory T cells) mice, we demonstrated that part of the protumorigenic effect of Tregs was dependent on their expression of CD73. CD73-deficient mice were also protected against pulmonary metastasis of B16F10 melanoma cells after intravenous injection. Unexpectedly, we found that the prometastatic effect of host-derived CD73 was dependent on CD73 expression on nonhematopoietic cells. CD73 expression on nonhematopoietic cells, most likely endothelial cells, was critical for promoting lung metastasis in a manner independent from immunosuppressive effects. Notably, in vivo blockade of CD73 with a selective inhibitor or anti-CD73 monoclonal antibody significantly reduced tumor growth and metastasis of CD73-negative tumors. Taken together, our findings indicate that CD73 may be targeted at multiple levels to induce anticancer effects including at the level of tumor cells, Tregs, and nonhematopoietic cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".