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Mutated fibroblast growth factor receptor 1 is an oncogenic driver and therapeutic target in high-risk neuroblastoma

ABSTRACT: Fibroblast growth factor receptor 1 (FGFR1) is recurrently mutated at p.N546 in neuroblastoma. We here sought to examine whether mutant FGFR1 is an oncogenic driver, a predictive biomarker, and an actionable vulnerability in this malignancy. FGFR1 mutations at p.N546 were associated with high-risk disease and rapid tumor progression, resulting in dismal outcome of neuroblastoma patients bearing this alteration. Ectopic expression of FGFR1N546K induced constitutive down-stream signaling and interleukin-3-independent growth in Ba/F3 cells, indicating oncogene addicted proliferation. In FGFR1N546K;MYCN transgenic mice, neuroblastoma developed within the first days of life with fatal outcome within 3 weeks, reflecting the devastating clinical phenotypes of FGFR1 mutant high-risk neuroblastoma patients. Treatment with FGFR inhibitors impaired proliferation and pathway activation in FGFR1N546K-expressing Ba/F3 and patient-derived FGFR1N546K mutant neuroblastoma cells, and inhibited tumor growth in FGFR1N546K;MYCN transgenic mice and in a chemotherapy-resistant patient-derived xenograft mouse model. In addition, partial regression of FGFR1N546K mutant tumor lesions occurred upon treatment with the FGFR inhibitor futibatinib and low-intensity chemotherapy in a patient with refractory neuroblastoma. Together, our data demonstrate that FGFR1N546K is a strong oncogenic driver in neuroblastoma that is associated with failure of current standard chemotherapy, and suggest potential clinical benefit of FGFR-directed therapies in FGFR1 mutant high-risk patients.

ORGANISM(S): Mus musculus

PROVIDER: GSE313168 | GEO | 2026/01/28

REPOSITORIES: GEO

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Project description:We generated gene expression profiles from 498 primary neuroblastomas using RNA-Seq and microarrays. We sought to systematically evaluate the capability of RNA deep-sequencing (RNA-Seq)-based classification for clinical endpoint prediction in comparison to microarray-based ones. The neuroblastoma cohort was randomly divided into training and validation sets, and 360 predictive models on six clinical endpoints were generated and evaluated. While prediction performances did not differ considerably between the two technical platforms, the RNA-Seq data processing pipelines, or feature levels (i.e., gene, transcript, and exon junction levels), RNA-Seq models based on the AceView database performed best on most endpoints. Collectively, our study reveals an unprecedented complexity of the neuroblastoma transcriptome, and provides guidelines for the development of gene expression-based predictive classifiers using high-throughput technologies. Sample clinical characteristics definitions: dataset: Expression data set used for classification training (1) or validation (2) Sex: M = male; F= female age at diagnosis: the age in days at diagnosis mycn status: Amplification status of the MYCN proto-oncogene (amplified = 1, no amplification = 0; no information = N/A) high risk: Clinically considered as high-risk neuroblastoma (yes=1, no= 0) INSS stage: disease stage according to International Neuroblastoma Staging System (INSS) (1, 2, 3, 4 and 4S) class label: Maximally divergent disease courses - unfavorable (= 1): patient died despite intensive chemotherapy, favorable (=0): patient survived without chemotharapy for at least 1000 days post diagnosis; not applicable (N/A) progression: Occurrence of a tumor progression event (yes=1; no=0) death from disease: Occurrence of death from the disease (yes=1; no=0) Gene expression of 498 neuroblastoma samples was quantified by RNA sequencing as well as by microarray analyses in order to understand the neuroblastoma transcriptome and predict clinical endpoints.

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Mutated fibroblast growth factor receptor 1 is an oncogenic driver and therapeutic target in high-risk neuroblastoma

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