Mutation of ERBB2 Provides a Novel Alternative Mechanism for the Ubiquitous Activation of RAS-MAPK in Ovarian Serous Low Malignant Potential Tumors
Bibliographic record
Abstract
Approximately, 10% to 15% of serous ovarian tumors fall into the category designated as tumors of low malignant potential (LMP). Like their invasive counterparts, LMP tumors may be associated with extraovarian disease, for example, in the peritoneal cavity and regional lymph nodes. However, unlike typical invasive carcinomas, patients generally have a favorable prognosis. The mutational profile also differs markedly from that seen in most serous carcinomas. Typically, LMP tumors are associated with KRAS and BRAF mutations. Interrogation of expression profiles in serous LMP tumors suggested overall redundancy of RAS-MAPK pathway mutations and a distinct mechanism of oncogenesis compared with high-grade ovarian carcinomas. Our findings indicate that activating mutation of the RAS-MAPK pathway in serous LMP may be present in >70% of cases compared with approximately 12.5% in serous ovarian carcinomas. In addition to mutations of KRAS (18%) and BRAF (48%) mutations, ERBB2 mutations (6%), but not EGFR, are prevalent among serous LMP tumors. Based on the expression profile signature observed throughout our serous LMP cohort, we propose that RAS-MAPK pathway activation is a requirement of serous LMP tumor development and that other activators of this pathway are yet to be defined. Importantly, as few nonsurgical options exist for treatment of recurrent LMP tumors, therapeutic targeting of this pathway may prove beneficial, especially in younger patients where maintaining fertility is important.
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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 | 0.000 |
| 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".