Mandibular undifferentiated pleomorphic sarcoma: Molecular analysis of a primary cell population
Bibliographic record
Abstract
BACKGROUND: Undifferentiated pleomorphic sarcomas are one of the most common subtypes of soft tissue sarcomas. These are aggressive mesenchymal tumors and are devoid of the major known biomarkers except vimentin. Our objective was to establish and characterize a primary cell population from a mandibular UPS specimen. METHODS: The tumor was surgically removed from the right mandible of a 24-year-old male with IRB approved signed consent. Tumor was dissected, cultured ex vivo, and a cell population, MUPS-1, were isolated from outgrowths. Gene and protein expression profiles of both the primary tumor and the derived there from cells were obtained by quantitative RT-PCR and immunohistochemistry and included markers of epithelial, endothelial, and mesenchymal differentiation. To better define potential biomarkers, MUPS-1 cells were additionally characterized by RNA sequencing analysis. RESULTS: Pathological analysis of primary tumor tissue revealed a sarcoma demonstrating multiple pathways of differentiation simultaneously with myxoid, fibrous, and osseous tissue. The isolated cells had a spindle cell-like morphology, were maintained in culture for greater than 20 passages, and formed colonies in soft agar indicating tumorigenicity. The cells, similar to the primary tumor, were strongly positive for vimentin and moderately expressed alkaline phosphatase. RNA-seq analysis revealed the tumor over-expressed several genes compared to normal tissue, including components of the Notch signaling pathway, NOTCH3 and JAG1. CONCLUSIONS: We have successfully established an undifferentiated pleomorphic sarcoma cell population, which will provide a valuable resource for studying fundamental processes and potentially serving as a platform for exploring therapeutic strategies for sarcomas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".