Distinct Patterns of Structural and Numerical Chromosomal Instability Characterize Sporadic Ovarian Cancer
Bibliographic record
Abstract
Sporadic ovarian cancer is a particularly aggressive tumor characterized by highly abnormal karyotypes exhibiting many features of genomic instability. More complex genomic changes in tumors arise as a consequence of chromosomal instability (CIN), which can generate both numerical [(N)-CIN] and structural chromosomal instability [(S)-CIN]. In this study, molecular cytogenetic analysis was used to evaluate the relative levels of both (N)-CIN and (S)-CIN. Six tumors had a near-diploid chromosome number, two were near-tetraploid, and two were near-triploid. (N)-CIN levels increased as a function of overall tumor genomic content, with near-diploid tumors exhibiting numerical instability indices ranging from 7.0 to 21.0 and near-tetraploid and triploid tumors exhibiting instability indices ranging from 24.9 to 54.9. In contrast, the extent of (S)-CIN was generally more evident in the diploid tumors compared with the near-tetraploid tumors. To determine whether the associated chromosomal constitution and/or ploidy changes were influenced by mitotic segregation errors, centrosome analyses were performed on all 10 tumors. The near-diploid tumors, with the lowest numerical change, were observed to possess fewer cells with centrosome abnormalities (5.5% to 14.0%), whereas the near-tetraploid tumors possessed much higher levels of (N)-CIN and were characterized by a trend of elevating percentages of cells with abnormal centrosomes (16.0% to 20.5%). These observations suggest that two distinct processes governing genome stability may be disrupted in ovarian cancer: those that impact on numerical segregation and ploidy of chromosomes and those that affect the fidelity of DNA repair and lead to structural aberrations.
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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 | 0.001 | 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".