Fig 2: The correlation of NSCLC BM-OS with different clinicopathological characteristics. a–c AXL, GAS6, and nodal (N) stage have statistically significant correlation with BM-OS. d, e The correlation between AXL expression and NSCLC BM-OS in N 2/3 stage group (n = 43), P < 0.05. f Combined analysis of AXL and GAS6 co-expression and its correlation with NSCLC BM-OS. The association of AXL/GAS6 high co-expression with overall survival (log-rank P < 0.05) is shown here. Group I with AXLLow and GAS6Low (n = 20); Group II with AXLHigh and GAS6Low (n = 9); Group III with AXLLow and GAS6High (n = 9); Group IV, AXLHigh and GAS6High (n = 26). g–i The correlation between AXL expression and NSCLC BM-OS in the lung tissue subgroup and brain tissue subgroup. AXL expression carries a statistically significant BM-OS difference in lung tissue subgroup, and not in brain tissue subgroup

Fig 3: AXL Is Highly Expressed in Clinical High-Grade Glioma Tumors(A) Survival analysis based on the impact of the multi-gene prognostic index. Affymetrix data generated by TCGA Research Network that are available at the TCGA data portal were used. Visualization of the data was performed using the GBM-BioDP (Celiku et al., 2014).(B) AXL is more highly expressed in astrocytoma and GBM than in non-tumor tissue (Sun et al., 2006) (n = 23 non-tumor; n = 7 grade II; n = 19 grade III; n = 81 GBM). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗∗p < 0.0001 (Mann-Whitney test).(C) Representative immunohistochemistry (ICH) staining of phospho-AXL in WHO grade IV (GBM), grade III and non-tumor samples. Scale bar represents 50 μm.(D–H) Patient survival data based on AXL expression. (D) GBM dataset. The cutoff was based on the median, n = 156, p = 0.060 with log-rank test. (E) Ovarian cancer dataset through Oncomine. The cutoff was based on the median, n = 288, p < 0.0001 with log-rank test. (F) Poorly and moderately differentiated colon cancer samples. The cutoff was based on the median, n = 164, p < 0.05 with log-rank test. (G) Top and bottom 25% samples from the pancreatic ductal carcinoma dataset through Oncomine, n = 13, p < 0.01 with log-rank test. (H) Top and bottom 25% samples from the Burkitt’s lymphoma dataset through Oncomine, n = 60, p < 0.05 with log-rank test.

Fig 4: Apoptosis induction in melanoma cells following combined inhibition of AXL and BRAF. Melmet 1 and A375 cells were treated with 2 µM BRAFi, 2 µM AXLi or a combination of both, DMSO for control for 48 h, stained with Hoechst (a) or annexin-V (b) and analyzed by flow cytometry. Insert in (b) represents the % of annexin-V positive Melmet 1 cells from three independent biological experiments. Average ± SEM, n = 3. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001.

Fig 5: Knockdown of a Subset of Kinases Differentially Induces Cell Death in MES or PN GSCs(A) List of kinases that induce cell death significantly (∗, adjusted p < 0.05) in MES GSCs (red) or in PN GSCs (blue). mRNA expression FC between PN and MES GSCs is indicated on the right side of the heatmap.(B) Stacked bar charts of the top two genes inducing cell death in MES (above) or in PN (below) GSCs. Average of the shNT used for normalization and the shRNAs targeting the indicated gene is shown. Error bars represent the SD.(C) Venn diagram grouping kinases whose silencing induces a significant change of the cell-cycle phases specific to MES or to PN GSCs (see also Figures S2 and S3). Asterisks highlight 54 kinases whose silencing altered the cell cycle specifically in MES GSCs.(D) Ranking of AXL according to the cell number FC in SubG1 in MES and PN GSCs. Data are all initially normalized to the non-targeting shRNA.