Abstract B23: <i>FGFR3</i> mutations and their relation to FGFR3 expression and clinical outcome in a large radical cystectomy cohort: Implications for anti-FGFR3 bladder cancer treatment?
Bibliographic record
Abstract
Abstract Objective: Fibroblast growth factor receptor 3 (FGFR3) is a potentially actionable target in bladder cancer (BC). FGFR3 mutations are associated with favorable prognosis in non-muscle invasive (NMI) BC and MIBC. Overexpression of FGFR3 was reported in up to 40% of FGFR3 wild-type MIBC. p53 alterations rarely coincide with FGFR3 mutations. We analyzed FGFR3 mutations and protein-expression of FGFR3 and p53 and assessed their prognostic value in a multicenter, multilaboratory setting. Methods: We included 1,000 cN0M0, chemotherapy-naive patients who underwent radical cystectomy (RC) with pelvic node dissection. Specimens were reviewed by eight uropathologists. At seven laboratories, FGFR3 mutation status was examined using PCR-SNaPshot. p53 and FGFR3 expression were determined by immunohistochemistry (IHC). FGFR3 mutation status, p53 and FGFR3 protein expression were correlated to each other, clinicopathologic parameters, and disease-specific survival (DSS). Results: FGFR3 mutations were found in 107/1,000 RCs (11%), of which 67 were S249C. Overexpression of FGFR3 was found in 279/1,000 (28%) of tumors. p53 overexpression (cut-off>10%) was found in 638/926 (69%) of available cases. Among FGFR3 mutant tumors, 73% had FGFR3 overexpression. Among FGFR3 wild-type tumors, 22% had FGFR3 overexpression. FGFR3 mutations were associated with lower pT-stage (P<0.001), lower grade (G2-WHO 1973) (P<0.001), absence of CIS (P=0.009), pN0 (P<0.001), normal p53 (P<0.001), and prolonged DSS (Plog-rank=0.001). FGFR3 overexpression was associated with lower pT-stage (P<0.001) and G2 (P<0.001) but not with absence of CIS (P=0.860), pN0 (P=0.230), normal p53, (P=0.330) nor prolonged DSS (Plog-rank=0.204). We found no significant difference in DSS for patients with FGFR3 mutant tumors comparing normal vs. overexpression of FGFR3 (Plog-rank=0.444). Furthermore, we also found no significant difference in DSS for patients with FGFR3 wild-type tumors comparing normal vs. overexpression of FGFR3 (Plog-rank=0.754). Conclusions: FGFR3 mutations identified patients with favorable BC with fewer p53 alterations at RC. FGFR3 overexpression was not associated with DSS in patients with FGFR3 wild-type tumors. Our results suggest that FGFR3 mutations (driver effect) have a distinct functional role than FGFR3 overexpression (passenger effect). Hence, patients with FGFR3 mutations may be more likely to benefit from anti-FGFR3 therapy than patients with only overexpression of FGFR3. Citation Format: Bas van Rhijn, Laura Mertens, Roman Mayr, Peter Bostrom, Mirari Marques, Geert van Leenders, Stefanie Gotz, Michiel van der Heijden, Michael Jewett, Francisco Real, Robert Stohr, Alexandre Zlotta, Markus Eckstein, Yanish Soorojebally, Max Burger, Wolfgang Otto, Francois Radvanyi, Damien Pouessel, Theo van der Kwast, Nuria Malats, Arndt Hartmann, Yves Allory, Deric van der Schoot, Ellen Zwarthoff, Tahlita Zuiverloon. FGFR3 mutations and their relation to FGFR3 expression and clinical outcome in a large radical cystectomy cohort: Implications for anti-FGFR3 bladder cancer treatment? [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr B23.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".