The Phenotypical Characterization of Dual-Nature Hybrid Cells in Uveal Melanoma
Bibliographic record
Abstract
Background: Metastasis, occurring years after primary diagnosis, represents a poor prognosis in uveal melanoma (UM)-affected individuals. The nature of cells involved in this process is under debate. Circulating hybrid cells that have combined tumor and immune cell features found in blood were predictive of metastasis and may correspond to dual-nature cells (DNC) in the primary tumor. Herein, we sought to determine the presence of DNCs in primary UM tumors, the cell types involved in their genesis, and their ability to be formed in vitro. Methods: UM lesions (n = 38) were immunolabeled with HMB45 in combination with immune-cell-specific antibodies. In parallel, we co-cultured UM cells and peripheral blood mononuclear cells (PBMCs) to analyze DNC formation. Results: HMB45+/CD45+ DNCs were present in 90% (26/29) of the tumors, HMB45+/CD8+ DNCs were present in 93% (26/28), and HMB45+/CD68+ DNCs were present in 71% (17/24). DNCs formed with CD8+ and CD68+ cells were positively correlated to the infiltration of their respective immune cells. Notably, UM cells were prone to hybridize with PBMCs in vitro. Conclusions: This phenotypical characterization of DNCs in UM demonstrates that CD8+ T-cells and macrophages are capable of DNC formation, and they are important for better understanding metastatic dissemination, thus paving the path towards novel therapeutic avenues.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".