Mutational heterogeneity in non-serous ovarian cancers
Bibliographic record
Abstract
Epithelial ovarian cancer is a leading cause of death in gynecological cancers. While several systematic studies have revealed the mutation landscape of serous epithelial ovarian cancer, other non-serous subtypes of the disease have not been explored as extensively. Here we conduct exome sequencing of nine non-serous epithelial ovarian tumors (six endometrioid and three mucinous) and their corresponding normal DNA as well as a tumor-only granulosa cell sample. We integrated the exome data with targeted gene sequencing for 1,321 genes selected for their involvement in cancer from additional 28 non-serous ovarian tumors and compared our results to TCGA ovarian serous cystadenocarcinoma and uterine corpus endometrial carcinomas. Prevalence of TP53 mutations in non-serous was much lower than in serous epithelial OC, whereas the prevalence of PIK3CA, PIK3R1, PTEN, CTNNB1, ARID1A, and KRAS was higher. We confirmed the high prevalence of FOXL2 and KRAS mutations in granulosa cell tumors and in mucinous tumors, respectively. We also identified POLE proofreading domain mutations in three endometrioid ovarian tumors. These results highlight mutational differences between serous and non-serous ovarian cancers, and further distinguish different non-serous subtypes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".