Oncogenic and drug-sensitive RET mutations in human epithelial ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Epithelial ovarian cancer (EOC) is a highly lethal malignancy. Improvement in genetic characterization of EOC patients is required to propose new potential targets, since surgical resection coupled to chemotherapy, presents several limits such as cancer recurrence and drug resistance. Targeted therapies have more efficacy and less toxicity than standard treatments. One of the most relevant cancer-specific actionable targets are protein tyrosine kinases (PTKs) whose role in EOC need to be better investigated. METHODS: EOC genomic datasets are retrieved and analyzed. The biological and clinical significance of RET genomic aberrations in ovarian cancer context are investigated by a series of in vitro and in vivo experiments. RESULTS: Epithelial ovarian cancer sequencing projects identify recurrent genomic RET missense mutations in 1.98% of patients, ranking as the top-five hit among the 100 receptor tyrosine kinases-encoding genes. RET mutants R693H and A750T show oncogenic transformation properties in NIH3T3 cells. Introduction of the RET mutants into human EOC cells increases RET signaling, cell viability, anchorage-independent cell growth and tumor xenograft growth in nude mice, demonstrating that they are activating mutations. RET mutants significantly enhance the activation of RET and its downstream MAPK and AKT signaling pathway in ovarian cancer cells. Vandetanib, a clinical approved RET inhibitor, inhibits the cell viability and decreases the activation of RET-MAPK signaling pathways in EOC cells expressing oncogenic RET mutants. CONCLUSIONS: The discovery of RET pathogenic variants in the EOC patients, suggests a previously underestimated role for RET in EOC tumorigenesis. The identification of the gain-of-function RET mutations in EOC highlights the potential use of RET in targeted therapy to treat ovarian cancer patients.
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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) | 0.001 | 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 | 0.001 |
| 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 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".