Abstract 1313: Hepatic pre-metastatic niche formation by uveal melanoma extracellular vesicles
Bibliographic record
Abstract
Abstract Background: Uveal melanoma (UM) is the most common primary intraocular tumor in adults, and half cases develop incurable liver metastases. As the liver is the first and often the only metastatic site of UM, this suggests the existence of a favorable microenvironment for UM cells. Extracellular vesicles (EVs) are released by cancer cells, which allow oncoproteins or genetic material to be transferred to distant cells in order to modify their microenvironment and promote the spread of cancer. Our hypothesis is that EVs from the ocular tumor precondition the hepatic stroma by activating hepatic stellate cells (HSteCs) to promote the colonization by metastatic cells. Our study aims to determine the role of melanomic EVs in the mechanisms governing the remodeling of the liver microenvironment, including the stromal stiffness. Methods: EVs were isolated from UM cells and choroidal melanocytes by differential centrifugation. The mechanisms of EV internalization in HSteCs were investigated by confocal microscopy using inhibitors of endocytosis pathways. The impact of melanomic EVs on the viability, energy metabolism and gene expression profile of targeted cells was studied using MTS assay, LIVE/DEAD assay, and RNA-Sequencing. In addition, activation of the mechanotransduction signaling pathways were studied by immunofluorescence and Western blotting (FAK phosphorylation and YAP nuclear translocation. HSteCs were seeded on hydrogels of varying stiffness (0.8/2.5/8 kPa; which mimic the stiffness of healthy to highly fibrotic liver), and then exposed to EVs to study their level of activation/contractility by confocal microscopy (⍺SMA+) and traction force microscopy. The preconditioning of the liver by melanomic EVs were studied by in vivo fluorescence imaging in immunodeficient mice. The liver was collected, and the EV-driven activation of HSteCs and their remodeling of the hepatic stroma were analyzed by confocal and polarization microscopy. The number of metastatic lesions and the fibrosis were studied by histological staining using Masson's Trichrome and Hematoxylin & Eosin stains. Results: Melanomic EVs increased the proliferation and energy metabolism of HSteCs, and activated pro-fibrotic signaling pathways. The increased rigidity of hydrogels, which mimic advanced pathological stages, favored the internalization of melanomic EVs by HSteCs. The preconditioning of the liver by melanomic EVs promoted the development of metastases and the production of a stiffer extracellular matrix in the animal model. Conclusions: Melanomic EVs preconditioned the liver by establishing a pre-metastatic niche through the activation of stellate cells. A better understanding of the bidirectional crosstalk between metastatic UM cells and stellate cells at the premetastatic and micro-metastatic stages in the liver will allow to identify new molecular mechanisms or biophysical features that could be therapeutically targeted. Citation Format: Kelly Coutant, Léo Piquet, Enola Bollmann, Andrew Mitchell, François Bordeleau, Solange Landreville. Hepatic pre-metastatic niche formation by uveal melanoma extracellular vesicles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1313.
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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".