High levels of chromosomal instability facilitate the tumor growth and sphere formation
Bibliographic record
Abstract
Most cancer cells show chromosomal instability (CIN), a condition in which chromosome missegregation occurs at high rates. Growing evidence suggests that CIN is not just a consequence of, but a driving force for, oncogenic transformation, although the relationship between CIN and tumorigenesis has not been fully elucidated. Here we found that conventional two-dimensional (2D) culture of HeLa cells, a cervical cancer-derived cell line, was a heterogenous population containing cells with different CIN levels. Although cells with high-CIN levels (high-CIN cells) grew more slowly compared with cells with low-CIN levels (low-CIN cells) in 2D monolayer culture, they formed tumors in nude mice and larger spheres in three-dimensional (3D) culture, which was more representative of the in vivo environment. The duration of mitosis was longer in high-CIN cells, reflecting their higher mitotic defects. Single-cell genome sequencing revealed that high-CIN cells exhibited a higher karyotype heterogeneity compared with low-CIN cells. Intriguingly, the karyotype heterogeneity was reduced in the spheres formed by high-CIN cells, suggesting that cells with growth advantages were selected, although genomic copy number changes specific for spheres were not identified. When we examined gene expression profiles, genes related to the K-ras signaling were upregulated, while those related to the unfolded protein response were downregulated in high-CIN cells in 3D culture compared with 2D culture, suggesting the relevance of these genes for their survival. Our data suggested that, although CIN is disadvantageous in monolayer culture, it promotes the selection of cells with growth advantages under in vivo environments, which may lead to tumorigenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".