Chromosome instability is prevalent and dynamic in high-grade serous ovarian cancer patient samples
Bibliographic record
Abstract
OBJECTIVE: High-grade serous ovarian cancer (HGSOC) is the most lethal gynaecological malignancy in women with a high level of mortality, metastatic disease, disease recurrence and multi-drug resistance. Many previous studies have focused on characterising genome instability in recurrent resistant HGSOC and while this has advanced our understanding of HGSOC, our fundamental knowledge of the mechanisms driving genome instability remains limited. Chromosome instability (CIN; an increased rate of chromosome gains and losses) is a form of genome instability that is commonly associated with recurrence and multi-drug resistance in many cancer types but has just begun to be characterised in HGSOC. METHOD: To examine the relationship between CIN and HGSOC, we employed single-cell quantitative imaging microscopy approaches capable of capturing the cell-to-cell heterogeneity associated with CIN, to assess the prevalence and dynamics of CIN within individual and patient-matched HGSOC ascites and solid tumour samples. RESULTS: CIN occurs in 90.9% of ascites samples and 100% of solid tumours, while in-depth analyses identified statistically significant temporal dynamics within the serial ascites samples. In general, aneuploidy and CIN increase with disease progression and frequently decrease following chemotherapy treatments in responsive disease. Finally, our work identified higher levels of CIN in solid tumours relative to ascites samples isolated from the same individual, which identifies a novel difference existing between solid tumours and ascites samples. CONCLUSIONS: Our findings provide novel insight into the relationship between CIN and HGSOC, and uncover a previously unknown relationship existing between CIN in solid tumours and metastatic disease (ascites).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".