Data from Human Thyroid Tumor Cell Lines Derived from Different Tumor Types Present a Common Dedifferentiated Phenotype
Bibliographic record
Abstract
Abstract Cell lines are crucial to elucidate mechanisms of tumorigenesis and serve as tools for cancer treatment screenings. Therefore, careful validation of whether these models have conserved properties of in vivo tumors is highly important. Thyrocyte-derived tumors are very interesting for cancer biology studies because from one cell type, at least five histologically characterized different benign and malignant tumor types can arise. To investigate whether thyroid tumor–derived cell lines are representative in vitro models, characteristics of eight of those cell lines were investigated with microarrays, differentiation markers, and karyotyping. Our results indicate that these cell lines derived from differentiated and undifferentiated tumor types have evolved in vitro into similar phenotypes with gene expression profiles the closest to in vivo undifferentiated tumors. Accordingly, the absence of expression of most thyrocyte-specific genes, the nonresponsiveness to thyrotropin, as well as their large number of chromosomal abnormalities, suggest that these cell lines have acquired characteristics of fully dedifferentiated cells. They represent the outcome of an adaptation and evolution in vitro, which questions the reliability of these cell lines as models for differentiated tumors. However, they may represent useful models for undifferentiated cancers, and by their comparison with differentiated cells, can help to define the genes involved in the differentiation/dedifferentiation process. The use of any cell line as a model for a cancer therefore requires prior careful and thorough validation for the investigated property. [Cancer Res 2007;67(17):8113–20]
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".