Genomic Catastrophe (Chromothripsis and Polyploidy) Correlates With Tumor Distribution in Extrauterine High-grade Serous Carcinoma
Bibliographic record
Abstract
Most extrauterine high-grade serous carcinomas (HGSCs) are thought to develop first in the distal fallopian tube. Most models of HGSC assume origin from relatively stable, noninvasive serous tubal intraepithelial carcinomas. However, widespread tumor involvement in the absence of a serous tubal intraepithelial carcinoma could occur after catastrophic genomic events (CGEs; such as chromothripsis or polyploidy). Twenty-six HGSCs assigned to fallopian tube (n = 9, group 1) and/or ovary (n = 9, group 2), and primary peritoneal (n = 8, group 3) were assessed by microarray (Oncoscan). CGEs were identified in 15/26 (57.7%); chromothripsis-like pattern in 13/26 (50.0%) and polyploidy in 6/26 (23.1%). CGE was seen in 4/9 (44.4%), 9/9 (100%), and 2/8 (25%) cases in groups 1. 2, and 3, respectively. Overall, CGEs were seen in 9/9 (100%) cases with grossly evident ovarian parenchymal involvement versus 6/17 (35.3%) without ( P = 0.0024). Ovarian size (measured on the long axis) correlated with CGE positivity ( P = 0.016). CGEs are significantly more common in HGSCs with ovarian parenchymal involvement compared with those limited to the fallopian tube and/or extraovarian tissues. These associations suggest geographically different tumor growth patterns and support the subdivision of HGSCs according to not only the stage but also tumor distribution. They have implications for clinical and pathologic presentation, trajectory of tumor evolution, and in the case of primary peritoneal HGSCs, potentially unique precursors to tumor transitions that could inform or influence cancer prevention efforts.
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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".