Abstract IA008: Rare ovarian cancers: the sequelae of specific interactions between cell contexts mutations and microenvironments
Bibliographic record
Abstract
Abstract There are many pathologically and clinically distinct types of ovarian cancer and uterine cancers. These are not flavors of the same disease nor can they be solely explained by different mutations or other genomic findings; rather each may be thought to represent distinct interactions between cells of origin, mutations, and the microenvironment in which these cancers occur. Although many know gaps remain this may explain many curious features of these cancers. Fors instance, how endometrioid and clear cell carcinomas have similar mutations yet are distinct biologic and clinical entities. Also, why they occur at similar rates from ovarian endometriomas, yet clear cell cancers are much rarer in the endometrium. As an example of the importance microenvironment, findings will be presented suggesting that expression of Cystathionine gamma-lyase (CTH or CSE), a key enzyme within the transsulfuration pathway, enables CCC precursor cells to survive in endometriotic cysts becoming a defining feature of this cancer, one that may explain its biologic properties, aggressive course and relative chemoresistance. Other cancers will be considered using this model including rare stromal cancers. Citation Format: David Huntsman. Rare ovarian cancers: the sequelae of specific interactions between cell contexts mutations and microenvironments [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr IA008.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".