Proteomic Assessment of Extracellular Vesicles from Canine Tissue Explants as a Pipeline to Identify Molecular Targets in Osteosarcoma: PSMD14/Rpn11 as a Proof of Principle
Bibliographic record
Abstract
Osteosarcoma (OS) is a highly malignant bone tumour that has seen little improvement in treatment modalities in the past 30 years. Understanding what molecules contribute to OS biology could aid in the discovery of novel therapies. Extracellular vesicles (EVs) serve as a mode of cell-to-cell communication and have the potential to uncover novel protein signatures. In our research, we developed a novel pipeline to isolate, characterize, and profile EVs from normal bone and osteosarcoma tissue explants from canine OS patients. Proteomic analysis of vesicle preparations revealed a protein signature related to protein metabolism. One molecule of interest, PSMD14/Rpn11, was explored further given its prognostic potential in human and canine OS, and its targetability with the drug capzimin. In vitro experiments demonstrated that capzimin induces apoptosis and reduces clonogenic survival, proliferation, and migration in two metastatic canine OS cell lines. Capzimin also reduces the viability of metastatic human OS cells cultured under 3D conditions that mimic the growth of OS cells at secondary sites. This unique pipeline can improve our understanding of OS biology and identify new prognostic markers and molecular targets for both canine and human OS patients.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".