Mechanistic Insights into Synergy between Melanin-Targeting Radioimmunotherapy and Immunotherapy in Experimental Melanoma
Bibliographic record
Abstract
Melanoma incidence continues to rise, and while therapeutic approaches for early stage cases are effective, metastatic melanoma continues to be associated with high mortality. Immune checkpoint blockade (ICB) has demonstrated clinical success with approved drugs in cohorts of patients with metastatic melanoma and targeted radionuclide therapy strategies showed promise in several clinical trials against various cancers including metastatic melanoma. This led our group to investigate the combination of these two treatments which could be potentially offered to patients with metastatic melanoma not responsive to ICB alone. Previously, we have demonstrated that a combination of humanized anti-melanin antibody conjugated to 213Bismuth and anti-PD-1 ICB reduced tumor growth and increased survival in the Cloudman S91 murine melanoma DBA/2 mouse model. In the current study, we sought to improve the tumoricidal effect by using the long-lived radionuclides 177Lutetium and 225Actinium. Male Cloudman S91-bearing DBA/2 mice were treated intraperitoneally with PBS (Sham), unlabeled antibody to melanin, anti-PD-1 ICB, 177Lutetium or 225Actinium RIT, or a combination of ICB and RIT. Treatment with anti-PD-1 alone or low-dose 177Lutetium RIT alone resulted in modest tumor reduction, while their combination significantly reduced tumor growth and increased survival, suggesting synergy. 225Actinium RIT, alone or in combination with ICB, showed no therapeutic benefit, suggesting that the two radionuclides with different energetic properties work in distinct ways. We did not detect an increase in tumor-infiltrating T cells in the tumor microenvironment, which suggests the involvement of alternative mechanisms that improve the effect of combination therapy beyond that observed in the single therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".