Novel Anaplastic Thyroid Cancer PDXs and Cell Lines: Expanding Preclinical Models of Genetic Diversity
Bibliographic record
Abstract
CONTEXT: Anaplastic thyroid cancer (ATC) is a rare, aggressive, and deadly disease. Robust preclinical thyroid cancer models are needed to adequately develop and study novel therapeutic agents. Patient-derived xenograft (PDX) models may resemble patient tumors by recapitulating key genetic alterations and gene expression patterns, making them excellent preclinical models for drug response evaluation. OBJECTIVE: We developed distinct ATC PDX models concurrently with cell lines and characterized them in vitro and in vivo. METHODS: Fresh thyroid tumor from patients with a preoperative diagnosis of ATC was surgically collected and divided for concurrent cell line and PDX model development. Cell lines were created by generating single cells through enzymatic digestion. PDX models were developed following direct subcutaneous implantation of fresh tumor on the flank of immune compromised/athymic mice. RESULTS: Six ATC PDX models and 4 cell lines were developed with distinct genetic profiles. Mutational characterization showed one BRAF/TP53/CDKN2A, one BRAF/CDKN2A, one BRAF/TP53, one TP53 only, one TERT-promoter/HRAS, and one TERT-promoter/KRAS/TP53/NF2/NFE2L2 mutated phenotype. Hematoxylin-eosin staining comparing the PDX models to the original patient surgical specimens show remarkable resemblance, while immunohistochemistry stains for important biomarkers were in full concordance (cytokeratin, TTF-1, PAX8, BRAF). Short tandem repeats DNA fingerprinting analysis of all PDX models and cell lines showed strong concordance with the original tumor. PDX successful establishment rate was 32%. CONCLUSION: We have developed and characterized 6 novel ATC PDX models with 4 matching cell lines. Each PDX model harbors a distinct genetic profile, making them excellent tools for preclinical therapeutic trials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".