Erlotinib Bound to the EGFR Kinase Domain

<div><p>Schematic representation of the wild-type EGFR tyrosine kinase domain (cyan) bound to erlotinib (orange) from the Protein Data Bank (<a href="http://www.rcsb.org/pdb/" target="_blank">http://www.rcsb.org/pdb/</a>) entry 1M17. The threonine 790 side chain is shown in green. The positions of the phosphate-binding loop (P-loop), the αC-helix, and the activation loop (conserved structural features in kinase domains) are shown for reference. Sites of common lung-cancer-associated drug-sensitive mutations (exon 19 deletion [del] and L858R) are also depicted.</p> <p>(Figure: Nikola Pavletich, Structural Biology Program, Memorial Sloan-Kettering Cancer Center)</p></div

Structural Models of EGFR Showing the T790M Resistance Mutation

<div><p>(A) Space-filling representation of the wild-type kinase active site (cyan) with the viewer looking down the vertical axis. The structure above the plane of the figure is omitted for clarity. The threonine 790 side chain is green, and erlotinib's molecular surface is shown as a yellow net.</p> <p>(B) The threonine 790 side chain is replaced by the corresponding methionine side chain from the structure of the insulin receptor tyrosine kinase (Protein Data Bank entry 1IRK). The EGFR and insulin receptor have a similar structure in this region of the active site. The methionine side chain would sterically clash with erlotinib, as shown, as well as with the related kinase inhibitor gefitinib (not shown).</p> <p>(Figure: Nikola Pavletich, Structural Biology Program, Memorial Sloan-Kettering Cancer Center)</p></div

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

Quantitative phosphoproteomics workflow and experimental design.

<p>Ba/F3 ERBB3/ERBB4 or ERBB4 cells were treated in the three replicate experiments as indicated. Upon lysis and proteolytic digestion, peptides were differentially labeled with the three isotopic variant of mTRAQ and then pooled prior to peptide separation by high pH reversed phase chromatography and IMAC phosphopeptide enrichment. Phosphopeptide fractions were then analyzed by quantitative LC-MS on a LTQ Orbitrap Velos instrument. Lower panel: Characteristic mTRAQ patterns shown for a peptide harboring a NRG1-induced phosphosite in ERBB3/ERBB4 cells, which less strongly up-regulated in the absence of ERBB3 in ERBB4-expressing Ba/F3 cells. </p

Contribution of ERBB3 to phosphosite regulation.

<p>Scatter plot of the mean ERBB3/ERBB4 ± NRG1 ratios with mean ERBB3/ERBB4 <i>versus</i> ERBB4 ratios from NRG1-treated cells. Reproducibly quantified ERBB3 phosphosites are encircled.</p

Follow-up of ART+CQ-treated, <i>Pb</i>NK65-infected C57BL/6 mice.

PbNK65-infected C57BL/6 mice were treated daily from 8 until 12 dpi with 10 mg/kg artesunate + 30 mg/kg chloroquine (ART+CQ). (A) Parasitemia was determined daily starting at 6 dpi using Giemsa-stained blood smears. (B) Clinical score was monitored daily starting at 6 dpi. (C) Body weight loss was calculated compared to 0 dpi starting at 6 dpi. (A-C) Data from three experiments. Data are represented as means ± SEM. n = 10 for CON, n = 15–18 for ART+CQ. (TIF)</p

Identification of significantly different phosphorylation sites.

<p>(A) Volcano plot of NRG1-regulated phosphorylation in Ba/F3 cells expressing ERBB3 and ERBB4. (B) Volcano plot comparison of phosphorylation sites in NRG1-treated ERBB3/ERBB4 <i>versus</i> NRG1-treated ERBB4 expressing Ba/F3 cells. In both comparisons, log₁₀-transformed, average phosphosite ratios are plotted against their standard deviations determined from mTRAQ replicate quantifications. Significantly regulated class I sites according to the Global Mean Rank test are depicted in red, all other sites in blue. The dashed grey lines indicate two-fold regulation.</p

Expression levels of <i>Vegfa</i> and <i>Mki67</i> in pulmonary nonimmune cell populations.

Non-immune cell clusters (Fig 5) from control (CON), PbNK65-infected (d8) and ART+CQ-treated (d12) mice were checked for the expression of different genes. Expression levels of vascular endothelial growth factor (Vegfa; A) and Marker of proliferation Ki-67 (Mki67; B) in the different nonimmune cell populations in all three conditions are shown. (TIF)</p

Overview of the pulmonary cell-cell communication in uninfected mice, mice with MA-ARDS and mice recovering from MA-ARDS.

Interactome analysis was performed on the original lung scRNAseq analysis of control (CON), PbNK65-infected (d8) and ART+CQ-treated (d12) mice using the MultiNicheNet package. (A-C) Circos plots showing the top 30 ligand-receptor interactions per condition compared to the other two conditions for CON (A), d8 (B) and d12 (C), determined using MultiNicheNet. Subclusters were combined into bigger clusters to make the results more robust (S5 Fig). All clusters were selected as senders and all as receivers. (A-C) n = 4 for CON, d8 and d12 (4 mice per condition were pooled using Hashtag oligos). AM, alveolar macrophages; BECs, blood endothelial cells; DCs, dendritic cells; EpCs, epithelial cells; Fibro, fibroblasts; gdT.ILC, γδ T cells/innate lymphoid cells; iMOs, inflammatory monocytes; Macros, macrophages; Meso, mesothelial cells; ncMOs, non-classical monocytes; Neutros, neutrophils; NK.cells, natural killer cells; Teff, effector T cells; Treg, regulatory T cells.</p

Pulmonary cell-cell communication between blood endothelial cells and effector T cells.

Interactome analysis was performed on the original lung scRNAseq analysis of control (CON), PbNK65-infected (d8) and ART+CQ-treated (d12) mice using the MultiNicheNet package with blood endothelial cells (BECs) as sender and effector T cells (Teff) as receiver (A,C,E) or with Teff cells as sender and BECs as receivers (B, D, F). Circos plots showing the top 30 ligand-receptor interactions per condition compared to the other two conditions for CON (A,B), d8 (C,D) and d12 (E,F) determined using MultiNicheNet. Subclusters were combined into bigger clusters to make the results more robust (S5 Fig). n = 4 for CON, d8 and d12 (4 mice per condition were pooled using Hashtag oligos. BECs, blood endothelial cells; Teff, effector T cells.</p