Influencing tumor-associated macrophages in malignant melanoma with monoclonal antibodies
Bibliographic record
Abstract
The application of monoclonal antibodies (mAbs) for the treatment of melanoma has significantly improved the clinical management of this malignancy over the last decade. Currently approved mAbs for melanoma enhance T cell effector immune responses by blocking immune checkpoint molecules PD-L1/PD-1 and CTLA-4. However, more than half of patients do not benefit from treatment. Targeting the prominent myeloid compartment within the tumor microenvironment, and in particular the ever-abundant tumor-associated macrophages (TAMs), may be a promising strategy to complement existing therapies and enhance treatment success. TAMs are a highly diverse and plastic subset of cells whose pro-tumor properties can support melanoma growth, angiogenesis and invasion. Understanding of their diversity, plasticity and multifaceted roles in cancer forms the basis for new promising TAM-centered treatment strategies. There are multiple mechanisms by which macrophages can be targeted with antibodies in a therapeutic setting, including by depletion, inhibition of specific pro-tumor properties, differential polarization to pro-inflammatory states and enhancement of antitumor immune functions. Here, we discuss TAMs in melanoma, their interactions with checkpoint inhibitor antibodies and emerging mAbs targeting different aspects of TAM biology and their potential to be translated to the clinic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".