Synergic Interactions Between Hepatic Stellate Cells and Uveal Melanoma in Metastatic Growth
Bibliographic record
Abstract
Uveal melanoma (UM) is a malignant intraocular tumor that spreads to the liver in half of the cases. Since hepatic cells could play a role in the therapeutic resistance of metastatic UM, the purpose of our study was to investigate the pro-invasive role of hepatic stellate cells (HSteCs) in metastatic UM at the micro- and macro-metastatic stages. We first performed an immunostaining with the alpha-smooth muscle actin (αSMA) to localize activated HSteCs in UM liver macro-metastases from four patients. Their accumulation of collagen was assessed with Masson’s Trichrome stain. Next, we inoculated metastatic UM cells alone or with human HSteCs in triple-immunodeficient mice, in order to determine if HSteCs are recruited as early as the micro-metastatic stage. The growth of metastatic foci was imaged in the liver by ex vivo fluorescence imaging. Histological analyses were performed with Masson’s Trichrome and Picrosirius Red stains, and antibodies against Melan-A and αSMA. The collagen content was measured in xenografts by quantitative polarization microscopy. In patient hepatectomy samples, activated HSteCs and their pathological matrix were localized surrounding the malignant lesions. In the mouse xenograft model, the number of hepatic metastases was increased when human HSteCs were co-inoculated. Histological analyses revealed a significant recruitment of HSteCs near the micro/macrolesions, and an increase in fibrillar collagen production. Our results show that HSteCs can provide a permissive microenvironment and might increase the therapeutic resistance of metastatic UM.
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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) | 0.002 | 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".