Targeting CD73 Enhances the Antitumor Activity of Anti-PD-1 and Anti-CTLA-4 mAbs
Bibliographic record
Abstract
PURPOSE: Monoclonal antibodies (mAb) that block programmed death (PD)-1 or cytotoxic T lymphocyte antigen (CTLA-4) receptors have been associated with durable clinical responses against a variety of cancer types and hold great potential as novel cancer therapeutics. Recent evidence suggest that targeted blockade of multiple immunosuppressive pathways can induce synergistic antitumor responses. EXPERIMENTAL DESIGN: In this study, we investigated whether targeted blockade of CD73, an ectonucleotidase that catabolizes the hydrolysis of extracellular adenosine monophosphate (AMP) to adenosine, can enhance the antitumor activity of anti-CTLA-4 and anti-PD-1 mAbs against transplanted and chemically induced mouse tumors. RESULTS: Anti-CD73 mAb significantly enhanced the activity of both anti-CTLA-4 and anti-PD-1 mAbs against MC38-OVA (colon) and RM-1 (prostate) subcutaneous tumors, and established metastatic 4T1.2 breast cancer. Anti-CD73 mAb also significantly enhanced the activity of anti-PD-1 mAb against 3-methylcholanthrene (MCA)-induced fibrosarcomas. Gene-targeted mice revealed that single-agent therapies and combinatorial treatments were dependent on host IFN-γ and CD8(+) T cells, but independent of perforin. Interestingly, anti-CD73 mAb preferentially synergized with anti-PD-1 mAb. We investigated the effect of extracellular adenosine on tumor-infiltrating T cells and showed that activation of A2A adenosine receptor enhances PD-1 expression, but not CTLA-4 expression, on tumor-specific CD8+ T cells and CD4+ Foxp3+ T regulatory cells. CONCLUSIONS: Taken together, our study revealed that targeted blockade of CD73 can enhance the therapeutic activity of anti-PD-1 and anti-CTLA-4 mAbs and may thus potentiate therapeutic strategies targeting immune checkpoint inhibitors in general.
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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".