Data from FGFR2c Mesenchymal Isoform Expression Is Associated with Poor Prognosis and Further Refines Risk Stratification within Endometrial Cancer Molecular Subtypes
Bibliographic record
Abstract
<div>AbstractPurpose:<p>The two most common molecular subtypes of endometrial cancers, mismatch repair deficient (MMRd) and p53 wild-type (p53wt) comprise the majority of endometrial cancers and have intermediate prognoses where additional risk stratification biomarkers are needed. Isoform switching of FGFR2 from FGFR2b to FGFR2c (normally expressed in mesenchymal cells), has been reported in other solid carcinomas. The objective of this study was to investigate the role of FGFR2c in risk stratification of endometrial cancer.</p>Experimental Design:<p>We have developed and optimized a BaseScope RNA ISH assay to detect FGFR2c. FGFR2c expression was determined in a preliminary screening cohort of 78 endometrial cancers and a clinically and molecularly annotated Vancouver cohort (<i>n</i> = 465). Cox regression model analyses were performed to assess the prognostic value of FGFR2c.</p>Results:<p>Univariate and multivariate analyses revealed FGFR2c expression was significantly associated with shorter disease-specific survival (DSS) and progression-free survival (PFS) in endometrioid endometrial cancer (EEC, <i>n</i> = 302). Notably, FGFR2c expression was significantly associated with shorter PFS and DSS in patients with grade 3 EECs (<i>P</i> < 0.003 and <i>P</i> < 0.002) and the European Society Medical Oncology (ESMO) high-risk group (<i>P</i> < 0.0001 and <i>P</i> < 0.002), respectively. Moreover, within the MMRd subtype, FGFR2c expression was significantly associated with shorter PFS (<i>P</i> < 0.048) and DSS (<i>P</i> < 0.001).</p>Conclusions:<p>FGFR2c expression appears an independent prognostic biomarker in patients with EEC and further discerns the outcomes within grade 3 tumors, ESMO high-risk groups, as well as within the MMRd and p53wt subtypes. FGFR2c inclusion into future molecular subtyping can further refine risk stratification of EEC.</p></div>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".