The unique metabolome of clear cell ovarian carcinoma
Bibliographic record
Abstract
Clear cell ovarian carcinoma (CCOC) is an aggressive malignancy affecting younger women. Despite ovarian cancer subtypes having diverse molecular and clinical characteristics, the mainstay of treatment for advanced stage disease remains cytotoxic chemotherapy. Late stage CCOC is resistant to conventional chemotherapy, which means a suboptimal outcome for patients affected. Despite detailed genomic, epigenomic, transcriptomic, and proteomic characterisation, subtype-specific treatment for CCOC has shown little progress. The unique glycogen accumulation defining CCOC suggests altered metabolic pathway activity and dependency. This study presents the first metabolomic landscape of ovarian cancer subtypes, including 42 CCOC, 20 high-grade serous and 21 endometrioid ovarian carcinomas, together comprising the three most common ovarian carcinoma subtypes. We describe a distinct metabolomic landscape of CCOC compared with other ovarian cancer subtypes, including alterations in energy utilisation and cysteine metabolism. In addition, we identify CCOC-specific alterations in metabolic pathways including serine biosynthesis and ROS-associated pathways that could serve as potential therapeutic targets. Our study provides the first in-depth study into the metabolome of ovarian cancers and a rich resource to support ongoing research efforts to identify subtype-specific therapeutic targets that could improve the dismal outcome for patients with this devastating malignancy. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".