FGFR1 mRNA and protein expression, not gene copy number, predict FGFR TKI sensitivity across all lung cancer histologies

Abstract

Purpose: FGFR1 gene copy number (GCN) is being evaluated as a biomarker for FGFR tyrosine kinase inhibitor (TKI) response in squamous cell lung cancers (SCC). The exclusive use of FGFR1 GCN for predicting FGFR TKI sensitivity assumes increased GCN is the only mechanism for biologically relevant increases in FGFR1 signaling. Herein, we tested whether FGFR1 mRNA and protein expression may serve as better biomarkers of FGFR TKI sensitivity in lung cancer.

Experimental design: Histologically diverse lung cancer cell lines were submitted to assays for ponatinib sensitivity, a potent FGFR TKI. A tissue microarray composed of resected lung tumors was submitted to FGFR1 GCN, and mRNA analyses and the results were validated with The Cancer Genome Atlas (TCGA) lung cancer data.

Results: Among 58 cell lines, 14 exhibited ponatinib sensitivity (IC50 values ≤ 50 nmol/L) that correlated with FGFR1 mRNA and protein expression, but not with FGFR1 GCN or histology. Moreover, ponatinib sensitivity associated with mRNA expression of the ligands, FGF2 and FGF9. In resected tumors, 22% of adenocarcinomas and 28% of SCCs expressed high FGFR1 mRNA. Importantly, only 46% of SCCs with increased FGFR1 GCN expressed high mRNA. Lung cancer TCGA data validated these findings and unveiled overlap of FGFR1 mRNA positivity with KRAS and PIK3CA mutations.

Conclusions: FGFR1 dependency is frequent across various lung cancer histologies, and FGFR1 mRNA may serve as a better biomarker of FGFR TKI response in lung cancer than FGFR1 GCN. The study provides important and timely insight into clinical testing of FGFR TKIs in lung cancer and other solid tumor types.

Figure 1. Ponatinib sensitivity of lung cancer cell lines of all histologies

Lung cancer cell lines were submitted to anchorage-independent or clonogenic growth assays in the presence of 0 – 300 nM ponatinib. Some cell lines failed to grow in these assays and the effect of TKI on cell proliferation was determined instead. A, Dose-response curves for lung cancer cell lines representative of the full panel are shown. B, The IC₅₀ values were calculated from the data and plotted from the lowest to highest. Green and red bars indicate the presence of mutations in PIK3CA and KRAS, respectively. C, Selected cell lines were treated for 2 hrs with ponatinib and cell extracts were immunoblotted for phospho-ERK and total ERK.

Figure 2. Association of FGFR1 GCN, protein and mRNA expression with ponatinib IC₅₀

The FGFR1:CEP8 ratio (A), FGFR1 protein from immunoblot analysis (B) and FGFR1 mRNA measured by quantitative RT-PCR (C) were determined in the panel of cell lines and plotted as shown. The data are graphically overlayed on the ponatinib IC₅₀ values from Figure 1B. The resulting datasets were submitted to ROC analysis (D) and the area under the curve (AUC) values are shown.

Figure 3. Association of FGF2 and FGF9 mRNA expression with ponatinib IC₅₀

FGF2 (A) and FGF9 (B) mRNA levels were determined by quantitative RT-PCR and plotted as shown. In (C), the sum of the FGF2 and FGF9 mRNA relative expression values are plotted.

Figure 4. Overlap of FGFR1 gene amplification and FGFR1 mRNA expression in primary lung tumors

A, FGFR1 GCN (SISH) and mRNA (ISH) staining of a representative double-positive tumor and a tumor negative for both markers is shown. B, The overlap in positivity for FGFR1 GCN (>4) and mRNA (4+) in the surgical lung cancer cohort is shown for the entire cohort as well as for the SCC/mixed histology/not otherwise specified (NOS) or the ACs and LCCs. C, Lung SCC and ACs from TCGA cohorts were queried for the overlap between FGFR1 amplification as specified in the extracted datasets and high FGFR1 mRNA (≥ mean RNAseq expression value in each dataset).