Additional file 1: of The CXCR4 antagonist plerixafor (AMD3100) promotes proliferation of Ewing sarcoma cell lines in vitro and activates receptor tyrosine kinase signaling

Figure S1. Granulocyte-colony stimulating factor and DMSO vehicle do not induce proliferation of Ewing sarcoma cell lines in vitro. Figure S2. Serum-deprivation does not alter cell line groups of CXCR4-high and -low surface expressions. (PDF 625 kb

Additional file 2: of The CXCR4 antagonist plerixafor (AMD3100) promotes proliferation of Ewing sarcoma cell lines in vitro and activates receptor tyrosine kinase signaling

Table S1. Plerixafor treatment changes receptor tyrosine kinase phosphorylation patterns in CXCR4-high and -low Ewing sarcoma cell lines. (PDF 1503 kb

Patientś characteristics and breakpoint coordinates of tumor samples and cell lines.

<p>Patientś characteristics and breakpoint coordinates of tumor samples and cell lines.</p

Genomic <em>EWS</em>-<em>FLI1</em> Fusion Sequences in Ewing Sarcoma Resemble Breakpoint Characteristics of Immature Lymphoid Malignancies

<div><p>Chromosomal translocations between the <i>EWS</i> gene and members of the <i>ETS</i> gene family are characteristic molecular features of the Ewing sarcoma. The most common translocation t(11;22)(q24;q12) fuses the <i>EWS</i> gene to <i>FLI1</i>, and is present in 85–90% of Ewing sarcomas. In the present study, a specifically designed multiplex long-range PCR assay was applied to amplify genomic <i>EWS-FLI1</i> fusion sites from as little as 100 ng template DNA. Characterization of the <i>EWS-FLI1</i> fusion sites of 42 pediatric and young adult Ewing sarcoma patients and seven cell lines revealed a clustering in the 5′ region of the <i>EWS</i>-breakpoint cluster region (BCR), in contrast to random distribution of breakpoints in the <i>FLI1</i>-BCR. No association of breakpoints with various recombination-inducing sequence motifs was identified. The occurrence of small deletions and duplications at the genomic junction is characteristic of involvement of the non-homologous end-joining (NHEJ) repair system, similar to findings at chromosomal breakpoints in pediatric leukemia and lymphoma.</p> </div

Genomic fusion site sequencing.

<p>(A) Genomic organization of the <i>EWS</i> and <i>FLI1</i> genes and corresponding breakpoint cluster regions (BCR). Nested primer sets for der22 are shown as double headed arrows. (B) Representative breakpoint sequencing workflow. Left: Gel electrophoresis of MLR-PCR products from two tumor samples in lane 1 and 2 (lane 3 negative control DNA; lane 4 ddH₂O; lane 5 positive control DNA; M = DNA ladder). Center: Gel electrophoresis of single long-range PCR products from 1^st round MLR-PCR product of sample 1 (lane 1–11; lane 12 positive control) to identify <i>FLI1</i> and <i>EWS</i> primers next to the fusion sites and to reduce amplification product size for direct sequencing. Right: Sequencing of the shortest amplification product and alignment to <i>EWS</i> and <i>FLI1</i> reference sequences.</p

Breakpoint distribution in the BCR of <i>EWS</i> and <i>FLl1</i>.

<p>(A) Vertical bars above or below the breakpoint regions indicate individual breakpoint positions of Ewing sarcoma patients. Black boxes represent exons, and gray boxes correspond to repeat elements. (B) Results of Kernel density analysis (dashed line = breakpoint density; gray line = lower limit of 95% confidence band determined by bootstrapping procedure; black line = 95% confidence interval of a density function resulting from simulations at randomly distributed pseudo-breakpoints). X-axes indicate the BCR nucleotide positions within the respective reference gene. (C) Scatterblot of gender-specific <i>EWS-FLI1</i> breakpoints. Circles represent female, and squares represent male subjects. (D) Number of microhomologies and filler nucleotides at <i>EWS-FLI1</i> (der22; black bar) and <i>FLI1-EWS</i> (der11; gray bar) fusion sites. Each bar on the x-axis represents one individual.</p

Target discovery screens using pooled shRNA libraries and next-generation sequencing: A model workflow and analytical algorithm

<div><p>In the search for novel therapeutic targets, RNA interference screening has become a valuable tool. High-throughput technologies are now broadly accessible but their assay development from baseline remains resource-intensive and challenging. Focusing on this assay development process, we here describe a target discovery screen using pooled shRNA libraries and next-generation sequencing (NGS) deconvolution in a cell line model of Ewing sarcoma. In a strategy designed for comparative and synthetic lethal studies, we screened for targets specific to the A673 Ewing sarcoma cell line. Methods, results and pitfalls are described for the entire multi-step screening procedure, from lentiviral shRNA delivery to bioinformatics analysis, illustrating a complete model workflow. We demonstrate that successful studies are feasible from the first assay performance and independent of specialized screening units. Furthermore, we show that a resource-saving screen depth of 100-fold average shRNA representation can suffice to generate reproducible target hits despite heterogeneity in the derived datasets. Because statistical analysis methods are debatable for such datasets, we created ProFED, an analysis package designed to facilitate descriptive data analysis and hit calling using an aim-oriented profile filtering approach. In its versatile design, this open-source online tool provides fast and easy analysis of shRNA and other count-based datasets to complement other analytical algorithms.</p></div

Sequencing data analysis and screen performance.

<p>(A) Representative read length histogram of a test sample (exp_A673) in technical replicates of Ion Proton NGS library preparations incorporating adapters via ligation (top panel) or PCR (middle panel), and of its replicate 1 counterpart (bottom panel). (B) Boxplot representation of shRNA read count distribution of raw data (left panel) and of TMM normalized data filtered for shRNAs with ≥ 50 alignments in ctrl_<i>a</i>/<i>b</i> (right panel). Numbers indicate screen replicates. (C) Biological reproducibility of the relative changes in shRNA abundance. Scatter plots show the correlation of log2 fold changes (FC) of experimental (exp_) relative to control samples (ctrl_) for screen replicates 1 and 2, based on TMM normalized and filtered datasets.</p

General design and workflow of the pooled shRNA screen.

<p>Collected samples of representative cell populations are depicted in blue: ctrl_<i>a</i>/<i>b</i> = unselected control populations; exp_<i>a</i>/<i>b</i> = experimental populations selected for phenotype of interest, here cell survival; Decode ctrl = virus particle control.</p

Enriched gene ontologies of A673 (condition <i>a</i>)-specific hits.

<p>Enriched gene ontologies of A673 (condition <i>a</i>)-specific hits.</p