FLT1 kinase is a mediator of radioresistance and survival in head and neck squamous cell carcinoma

<div><p></p><p>Head and neck squamous cell carcinoma (HNSCC) is the fifth most common malignancy worldwide, responsible for approximately half a million new cases every year. The treatment of this disease is challenging and characterised by high rates of therapy failure and toxicity, stressing the need for new innovative treatment strategies. <i>Material and methods.</i> In this study we performed a shRNAmir-based screen on HNSCC cells with the aim to identify tyrosine kinases that are mediating radiotherapy resistance. <i>Results.</i> The receptor tyrosine kinase FLT1 (VEGFR1) was identified as an important driver of cell survival and radioresistance. We show that FLT1 is phosphorylated in HNSCC cells, and document autocrine production of FLT1 ligands VEGFA and VEGFB, leading to receptor activation. Immunohistochemistry on HNSCC patient samples demonstrated FLT1 and VEGFA to be uniformly expressed. Interestingly, FLT1 was selectively overexpressed in tumour tissue as compared to non-cancerous epithelium. Remarkably, we found only membrane permeable FLT1 kinase inhibitors to be effective, which was in agreement with the intracellular localisation of FLT1. <i>Discussion and conclusion.</i> Taken together, we document expression of FLT1 in HNSCC and demonstrate this kinase to modulate radioresistance and cancer cell survival. Given the fact that FLT1 kinase is selectively upregulated in tumour tissue and that its kinase function seems expendable for normal life and development, this kinase holds great promise as a new potential therapeutic target.</p></div

JAK kinase mutations.

<p>(A) Sanger sequencing chromatograms corresponding to confirmed JAK2/JAK3 variants. (B) Domain structure of JAK2 and JAK3 proteins with indication of novel detected variants. Non-somatic variants are indicated with an asterisk. (C) Sanger sequences showing examples of TYK2 variants detect in T-ALL cell lines or in leukemia patient samples. (D) Schematic representation of TYK2 protein structure with indication of all novel TYK2 variants detected in this study. Non-somatic variants are indicated with an asterisk.</p

Mutations in the 97 genes.

<p>Coding mutations in known cancer genes (A) and candidate genes (B) are indicated with different color codes. Panel A is further subdivided into (I) genes that are known to be drivers in T-ALL, and (II) the genes that have recurrent somatic mutations in various human cancers. The cell lines are located to the left of the table, and the patient samples are located to the right. Genes are ranked according to the frequency of protein altering mutations in the patient samples.</p

Analysis of TYK2 variants in cell lines over time and in different subclones.

<p>Presence of the TYK2 R1027 and A35V variants was tested in the CCRF-CEM cell line from our group (“CCRF-CEM Cools lab”) as well as in the CCRF-CEM cell line as it is currently sold by DSMZ (“CCRF-CEM 2011 DSMZ (ACC240)) and in 5 different CCRF-CEM subclones that DSMZ collected over the years. Similarly, KARPAS-45 from the Cools lab and the KARPAS-45 lines obtained from DSMZ in 2011 and in 1994 were screened for presence of the TYK2 Q830* variant. JURKAT cells from the Cools lab as well as JURKAT provided by DSMZ in 2011 and 1992 were tested for the TYK2 C192Y variant.</p>*<p>This cell line has 4 copies of chromosome 19 containing TYK2. The height of the variant peak on the chromatogram suggests that only 1 copy of TYK2 contains the Q830* variant.</p

Performance comparison and parameter optimization.

<p>(A) Different pipelines show different sensitivity and specificity. Varying DoC and VAF thresholds in the variant calling process has an additional affect on the predictions in terms of sensitivity and specificity, respectively. Each pipeline is represented with a different symbol and the performance of each pipeline (in terms of sensitivity and specificity) is plotted under varying DoC and VAF thresholds. Note that the X-axis represents the false positive rate (1-specificity). In this ROC plot, the closer the point to the upper left point of the graph, the better the sensitivity and the specificity. Different colors of the symbols indicate the performance of the pipeline under changing VAF thresholds, and the two shaded boxes indicate the performance under changing DoC thresholds. The plot shows that (i) decreasing the DoC threshold increases the sensitivity of all pipelines as indicated with the blue dotted line; (ii) increasing the VAF threshold increases the specificity with a slight decrease in sensitivity as indicated (in the example of BLAT+VarScan pipeline) with the red dotted line; (iii) the BWA-SW+SSAHA2+Atlas-SNP2 pipeline has the best performance among all pipelines under DoC = 3 & VAF = 0.20 thresholds as indicated with the yellow arrow. The Roche pipeline is indicated with a black diamond shape since no parameter changes were performed on it, and SSAHA2+SAMTools and BWA-SW+SAMTools pipelines were colored grey since no VAF threshold changes were performed on them. (B) The Matthews correlation coefficient for each pipeline is shown for the most optimal performance of that pipeline (<b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038463#pone.0038463.s005" target="_blank">Table S1</a></b>). It is interesting to note that the optimal performance of all the pipelines, except Roche gsMapper, was observed for a DoC threshold of 3.</p

TET1 mutations in T-ALL.

<p>(A) Sanger sequencing chromatograms representing confimed TET1 variants. (B) Schematic representation of TET1 protein structure with indication of all novel TET1 variants detected in this study. Variants detected in cell lines are depicted above the TET1 protein, variants detected in leukemia patient samples are below the TET1 protein. Non-somatic variants are indicated with an asterisk.</p

SPRY4 mutations.

<p>(A) Sanger sequencing chromatograms showing confirmed SPRY4 variants. (B) Domain structure of the SPRY4 protein with indication of novel detected variants.</p