Am-Bivalent Copper: Mechanistically Distinct Immune Effects Driving Innovation in Cancer Nanomedicine
Bibliographic record
Abstract
Copper (Cu) is an essential mineral required by all living cells, where it supports critical enzymatic and signaling functions. In cancer, this balance is often disrupted, creating vulnerabilities that can be therapeutically exploited. Changes in Cu availability have been shown to influence key immunoregulatory pathways, including those involved in inflammation, cell death, and immune evasion. Notably, Cu can drive expression of programmed death-ligand 1 (PD-L1), contributing to immunosuppression, while also promoting immunogenic cell death, which stimulates adaptive immune responses. These dual effects highlight the complexity and therapeutic potential of Cu-based interventions, particularly in the context of immune modulation and toxicity. This review argues that Cu-based nanomedicines can selectively deliver high concentrations of bioactive Cu to tumor cells, inducing cell death and triggering adaptive immune responses. We summarize current knowledge on Cu’s roles in cancer and immunity, emphasizing recent insights into how these intersect through Cu-mediated modulation of anti-cancer immune pathways. Finally, we explore the clinical potential of Cu-based nanomedicines to convert immunologically “cold” tumors into “hot” ones, thereby improving responses to immunotherapy. Realizing this potential will depend on the thoughtful integration of Cu delivery approaches with existing immunotherapeutic strategies.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".