Targeting Melanin in Melanoma with Radionuclide Therapy
Bibliographic record
Abstract
Nearly 100,000 individuals are expected to be diagnosed with melanoma in the United States in 2022. Treatment options for late-stage metastatic disease up until the 2010s were few and offered only slight improvement to the overall survival. The introduction of B-RAF inhibitors and anti-CTLA4 and anti-PD-1/PD-L1 immunotherapies into standard of care brought measurable increases in the overall survival across all stages of melanoma. Despite the improvement in the survival statistics, patients treated with targeted therapies and immunotherapies are subject to very serious side effects, the development of drug resistance, and the high costs of treatment. This leaves room for the development of novel approaches as well as for the exploration of novel combination therapies for the treatment of metastatic melanoma. One such approach is targeting melanin pigment with radionuclide therapy. Advances in melanin-targeting radionuclide therapy of melanoma can be viewed from two spheres: (1) radioimmunotherapy (RIT) and (2) radiolabeled small molecules. The investigation of mechanisms of the action and efficacy of targeting melanin in melanoma treatment by RIT points to the involvement of the immune system such as complement dependent cytotoxicity. The combination of RIT with immunotherapy presents synergistic killing in mouse melanoma models. The field of radiolabeled small molecules is focused on radioiodinated compounds that have the ability to cross the cellular membranes to access intracellular melanin and can be applied in both therapy and imaging as theranostics. Clinical applications of targeting melanin with radionuclide therapies have produced encouraging results and clinical work is on-going. Continued work on targeting melanin with radionuclide therapy as a monotherapy, or possibly in combination with standard of care agents, has the potential to strengthen the current treatment options for melanoma patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".