Scientific publications

FGFR1 and FGFR4 oncogenicity depends on n-cadherin and their co-expression may predict FGFR-targeted therapy efficacy

Mar 1, 2020 | Magazine: EBioMedicine

Álvaro Quintanal-Villalonga, Irene Ferrer, Elizabeth Guruceaga, Cristina Cirauqui, Ángela Marrugal, Laura Ojeda, Santiago García, Jon Zugazagoitia, Sandra Muñoz-Galván, Fernando Lopez-Rios, Luis Montuenga, Silvestre Vicent, Sonia Molina-Pinelo, Amancio Carnero, Luis Paz-Ares


Background: Fibroblast growth factor receptor (FGFR)1 and FGFR4 have been associated with tumorigenesis in a variety of tumour types. As a therapeutic approach, their inhibition has been attempted in different types of malignancies, including lung cancer, and was initially focused on FGFR1-amplified tumours, though with limited success.

Methods: In vitro and in vivo functional assessments of the oncogenic potential of downregulated/overexpressed genes in isogenic cell lines were performed, as well as inhibitor efficacy tests in vitro and in vivo in patient-derived xenografts (PDXs). mRNA was extracted from FFPE non-small cell lung cancer samples to determine the prognostic potential of the genes under study.

Findings: We provide in vitro and in vivo evidence showing that expression of the adhesion molecule N-cadherin is key for the oncogenic role of FGFR1/4 in non-small cell lung cancer. According to this, assessment of the expression of genes in different lung cancer patient cohorts showed that FGFR1 or FGFR4 expression alone showed no prognostic potential, and that only co-expression of FGFR1 and/or FGFR4 with N-cadherin inferred a poorer outcome. Treatment of high-FGFR1 and/or FGFR4-expressing lung cancer cell lines and patient-derived xenografts with selective FGFR inhibitors showed high efficacy, but only in models with high FGFR1/4 and N-cadherin expression.

Interpretation: Our data show that the determination of the expression of FGFR1 or FGFR4 alone is not sufficient to predict anti-FGFR therapy efficacy; complementary determination of N-cadherin expression may further optimise patient selection for this therapeutic strategy.

CITA DEL ARTÍCULO  EBioMedicine. 2020 Mar;53:102683. doi: 10.1016/j.ebiom.2020.102683. 

Our authors

Elizabet Guruceaga Martínez
Bioinformatics Research Technician Bioinformatics Platform