Stem Cell Media Culture of Melanoma Results in the Induction of a Nonrepresentative Neural Expression Profile
Bibliographic record
Abstract
The ability of cell lines to accurately represent cancer is a major concern in preclinical research. Culture of glioma cells as neurospheres in stem cell media (SCM) has been shown to better represent the genotype and phenotype of primary glioblastoma in comparison to serum cell lines. Despite the use of neurosphere-like models of many malignancies, there has been no robust analysis of whether other cancers benefit from a more representative phenotype and genotype when cultured in SCM. We analyzed the growth properties, transcriptional profile, and genotype of melanoma cells grown de novo in SCM, as while melanocytes share a common precursor with neural cells, melanoma frequently demonstrates divergent behavior in cancer stem cell assays. SCM culture of melanoma cells induced a neural lineage gene expression profile that was not representative of matched patient tissue samples and which could be induced in serum cell lines by switching them into SCM. There was no enrichment for expression of putative melanoma stem cell markers, but the SCM expression profile did overlap significantly with that of SCM cultures of glioma, suggesting that the observed phenotype is media-specific rather than melanoma-specific. Xenografts derived from either culture condition provided the best representation of melanoma in situ. Finally, SCM culture of melanoma did not prevent ongoing acquisition of DNA copy number abnormalities. In conclusion, SCM culture of melanoma does not provide a better representation of the phenotype or genotype of metastatic melanoma, and the resulting neural bias could potentially confound therapeutic target identification.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".