FGFR1, 2 and 3 protein overexpression and molecular aberrations of FGFR3 in early stage non‐small cell lung cancer
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study aimed to determine protein expression levels of fibroblast growth factor receptors (FGFR) 1, 2 and 3 in early stage non‐small cell lung cancer (NSCLC). Additionally, a screen to define the frequency of FGFR3‐TACC3 translocation and FGFR3 amplification was performed. Archived tissues from 653 NSCLC samples (adenocarcinoma (AC), squamous cell carcinoma (SCC) and large cell carcinoma (LCC)) were analysed with immunohistochemistry (IHC) for expression of FGFR1, 2 and 3. Expression levels of FGFR1, 2 and 3 were correlated with clinicopathological features. The presence of FGFR3‐TACC3 translocation was detected by RT‐PCR and FGFR3 amplification was detected by fluorescence in situ hybridization. FGFR1, 2 and 3 proteins were highly expressed in 64 (10.6%), 76 (12.9%) and 20 (3.3%) NSCLC tumour samples, respectively. Protein expression of FGFR1 was significantly related to worse overall survival in NSCLC. Furthermore, FGFR1 protein expression was associated with light smoking and histological subtype (AC), FGFR2 protein expression with female gender, younger age, histological subtype (AC) and lower tumour stage, and FGFR3 protein was significantly overexpressed in tumours of older patients and SCC histology. The FGFR3‐TACC3 fusion was detected in 3.0% (6/200) of NSCLC samples and the FGFR3 gene was amplified in 4.7% of IHC positive NSCLC samples (2/43). FGFR1, 2 and 3 proteins are expressed in a high number of early stage NSCLC and FGFR1 protein expression may serve as a prognostic biomarker. Recurrent translocations and amplifications in FGFR3 can be found in NSCLC. This study shows that FGFR family members are frequently aberrant in NSCLC and could be interesting therapeutic targets for the treatment of NSCLC.
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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 | 0.005 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it