FGFR2 mutations are associated with poor outcomes in endometrioid endometrial cancer: An NRG Oncology/Gynecologic Oncology Group study
Bibliographic record
Abstract
Purpose Activating FGFR2 mutations have been identified in ~ 10% of endometrioid endometrial cancers (ECs). We have previously reported that mutations in FGFR2 are associated with shorter disease free survival (DFS) in stage I/II EC patients. Here we sought to validate the prognostic importance of FGFR2 mutations in a large, multi-institutional patient cohort. Methods Tumors were collected as part of the GOG 210 clinical trial “Molecular Staging of Endometrial Cancer” where samples underwent rigorous pathological review and had more than three years of detailed clinical follow-up. DNA was extracted and four exons encompassing the FGFR2 mutation hotspots were amplified and sequenced. Results Mutations were identified in 144 of the 973 endometrioid ECs, of which 125 were classified as known activating mutations and were included in the statistical analyses. Consistent with FGFR2 having an association with more aggressive disease, FGFR2 mutations were more common in patients initially diagnosed with stage III/IV EC (29/170;17%) versus stage I/II EC (96/803; 12%; p = 0.07, Chi-square test). Additionally, incidence of progression (progressed, recurred or died from disease) was significantly more prevalent (32/125, 26%) among patients with FGFR2 mutation versus wild type (120/848, 14%; p < 0.001, Fishers exact test). Using Cox regression analysis adjusting for known prognostic factors, patients with FGFR2 mutation had significantly (p < 0.025) shorter progression-free survival (PFS; HR 1.903; 95% CI 1.177–3.076) and endometrial cancer specific survival (ECS; HR 2.013; 95% CI 1.096–3.696). Conclusion In summary, our findings suggest that clinical trials testing the efficacy of FGFR inhibitors in the adjuvant setting to prevent recurrence and death are warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".