From simplicity to complexity in current melanoma models
Bibliographic record
Abstract
Despite the recent impressive clinical success of immunotherapy against melanoma, development of primary and adaptive resistance against immune checkpoint inhibitors remains a major issue in a large number of treated patients. This highlights the need for melanoma models that replicate the tumor's intricate dynamics in the tumor microenvironment (TME) and associated immune suppression to study possible resistance mechanisms in order to improve current and test novel therapeutics. While two-dimensional melanoma cell cultures have been widely used to perform functional genomics screens in a high-throughput fashion, they are not suitable to answer more complex scientific questions. Melanoma models have also been established in a variety of experimental (humanized) animals. However, due to differences in physiology, such models do not fully represent human melanoma development. Therefore, fully human three-dimensional in vitro models mimicking melanoma cell interactions with the TME are being developed to address this need for more physiologically relevant models. Such models include melanoma organoids, spheroids, and reconstructed human melanoma-in-skin cultures. Still, while major advances have been made to complement and replace animals, these in vitro systems have yet to fully recapitulate human tumor complexity. Lastly, technical advancements have been made in the organ-on-chip field to replicate functions and microstructures of in vivo human tissues and organs. This review summarizes advancements made in understanding and treating melanoma and specifically aims to discuss the progress made towards developing melanoma models, their applications, limitations, and the advances still needed to further facilitate the development of therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".