Histological and Genetic Diversity in Ovarian Mucinous Carcinomas: A Pilot Study
Bibliographic record
Abstract
Tumor heterogeneity remains an ongoing challenge in the field of cancer therapy. Intratumor heterogeneity significantly complicates the diagnosis of cancer and presents challenging clinical problems due to resistance to drug therapy. This study aimed to elucidate the genetic changes histologically (mucinous cystadenoma (MCA), mucinous borderline tumor (MBT), and mucinous ovarian carcinoma (MOC)) in a portion of mucinous ovarian tumors within the same sample. Seven tumor samples obtained from different patients were used to evaluate the genetic mutations in each component. Intratumor genetic heterogeneity was observed in all patients; among them, BRAF (V600E) and p53 (T118I, P142S, T150I, and T170M) point mutations were observed in the MBT component, while KRAS (G12D and G13D) and PIK3CA (E545K) mutations were found in the MOC component. The current findings suggest that diverse genetic alterations occur in mucinous tumors, according to tumor histology. Tumor heterogeneity and genetic diversity in mucinous ovarian tumors might be the cause of treatment failure. Knowledge of intertumor heterogeneity may lead to an increased understanding of the tumor response to treatment.
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 | 0.000 | 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".