Optimal Factors That Influence the Establishment of Successful Primary Human Soft-Tissue Sarcoma Cell Lines
Bibliographic record
Abstract
PURPOSE: The development of improved therapies is complicated by the limited availability of well-characterized human models, especially in rare tumors such as soft-tissue sarcoma (STS). We report the optimization of conditions and clinical factors that correlate with the successful establishment of primary STS cell lines. We focus on leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma, which are adult STS with complex genomics and poor survival rates. EXPERIMENTAL DESIGN: We initiated cell lines from 165 fresh STS specimens. Cultures were classified as (i) no/little in vitro growth or (ii) persistent growth. Tumor and clinical characteristics were analyzed to determine their correlation with cell line growth. To determine whether cell lines shared tumor-specific variations (TSV) and mutational signatures with bulk specimens, comparative and Catalogue of Somatic Mutations in Cancer mutational signature analyses were performed on a subset of cases with cell line, tumor, and blood DNAs available. RESULTS: Cell lines were established from 46 specimens (28%). Myxofibrosarcoma specimens yielded more successful cell lines (P < 0.05) than leiomyosarcoma specimens. Primary specimens from treatment-naïve patients and those who presented with metastases demonstrated higher success rates (P < 0.05) compared with treated specimens and those who had only local disease, respectively. Cell line growth was not associated with patient outcomes or specimen grade. Six of the eight cases retained TSVs, including in ATRX or TP53, whereas two did not retain TSVs. Paired samples shared clock-like mutational signatures. Xenograft mouse models were created with a subset of the cell lines. CONCLUSIONS: The development and characterization of preclinical STS models will advance our understanding of STS biology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".