Fibroblast growth factor receptor alterations and resistance mechanisms in the treatment of pediatric solid tumors
Bibliographic record
Abstract
Aim: The fibroblast growth factor receptor (FGFR) family receptors regulate cell proliferation, survival, and migration and are linked to cancer drug resistance. FGFR gene family alterations have been found in multiple adult cancers, for which FGFR inhibitors are in various stages of clinical development. This study aimed to delineate the FGFR alterations in pediatric tumors and provide a preclinical rationale for developing FGFR inhibitors for select pediatric patients. Methods: The prevalence of FGFR alterations in pediatric cancers was calculated from databases with available pediatric tumor data. Effects of the pan-FGFR inhibitor infigratinib (BGJ398) on pediatric cancer cell line viability and migration were evaluated by continuous live cell imaging and compared to FGFR gene expression. Effects on cell death and signaling pathway activity were evaluated by live cell imaging and Western blots. Results: Overall rates of FGFR1-4 gene alterations in pediatric cancers were rare, and the mutation profile substantially differs from that of adult tumors. Although FGFR genomic alterations are rare in pediatric neuroblastoma tumors, overexpression of FGFR1-4 is observed in tumor subsets and is associated with outcomes. Dose-dependent inhibition of cell proliferation and migration and promotion of cell death were achieved with BGJ398 treatment in neuroblastoma cell lines, accompanied by inhibition of RAS-MAPK pathway activity and induction of apoptosis. Conclusion: Adult and pediatric cancers share common mechanisms of FGFR activation but differ in overall alteration rates and relative abundance of specific aberrations. Preliminary experimental data indicate the therapeutic potential of FGFR inhibitors and suggest mechanisms of resistance in the treatment of pediatric cancers.
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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 | 0.001 | 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".