Different <i>TP53</i> mutations are associated with specific chromosomal rearrangements, telomere length changes, and remodeling of the nuclear architecture of telomeres
Bibliographic record
Abstract
TP53 mutations are the most common mutations in human cancers, and TP53-R175H and TP53-R273H are the most frequent. The impact of these mutations on genomic instability after tumor initiation is still uncovered. To gain insight into this, we studied the effects of three specific TP53 mutants (TP53-V143A, TP53-R175H, and TP53-R273H) on genomic instability using four isogenic lines of LoVo cells. Multicolor fluorescence in situ hybridization (FISH), three-dimensional (3D) quantitative FISH (Q-FISH) on interphase and Q-FISH on metaphases were used to investigate genomic instability. We found that LoVo cells expressing mutant TP53-R175H displayed the highest level of chromosomal instability among the LoVo cell lines. Furthermore, we observed that mutant TP53-R175H and TP53-V143A showed more alterations in their 3D nuclear architecture of telomeres than the mutant TP53-R273H and the wild type. Moreover, we noted an association between some chromosomal abnormalities and telomere elongation in the mutant TP53-R175H. Taken together, our results indicate that the mutation TP53-R175H is more likely to cause higher levels of genomic instability than the other TP53 mutations. We proposed that the type of TP53 mutations and the genetic background of a cancer cell are major determinants of the TP53-dependent genomic instability.
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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 | 0.000 | 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) | 0.001 | 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 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".