Variation in RNA expression and genomic DNA content acquired during cell culture
Bibliographic record
Abstract
Specific chromosomal abnormalities are increasingly recognised to be associated with particular tumour subtypes. These cytogenetic abnormalities define the sites of specific genes, the alteration of which is implicated in the neoplastic process. We used comparative genomic hybridisation (CGH) to examine DNA from different breast and ovarian cancer cell lines for variations in DNA sequence copy number compared with the same normal control. We also compared different sources of the MCF7 breast line by both CGH and cDNA expression arrays. Some of the differences between the subcultures were extensive and involved large regions of the chromosome. Differences between the four subcultures were observed for gains of 2q, 5p, 5q, 6q, 7p, 7q, 9q, 10p, 11q, 13q, 14q, 16q, 18p and 20p, and losses of 4q, 5p, 5q, 6q, 7q, 8p, 11p, 11q, 12q, 13q, 15q, 19p, 19q, 20p, 21q, 22q and Xp. However, few variations were found between two subcultures examined, 5 months apart, from the same initial source. The RNA arrays also demonstrated considerable variation between the three different subcultures, with only 43% of genes expressed at the same levels in all three. Moreover, the patterns of the expressed genes did not always reflect our observed CGH aberrations. These results demonstrate extensive genomic instability and variation in RNA expression during subculture and provide supportive data for evidence that cell lines do evolve in culture, thereby weakening the direct relevance of such cultures as models of human cancer. This work also reinforces the concern that comparisons of published analyses of cultures of the same name may be dangerous.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".