Minerva Endocrinologica : a journal on endocrinological pathophysiology

<div><p>Background</p><p>Originating from Primordial Germ Cells/gonocytes and developing via a precursor lesion called Carcinoma <i>In Situ</i> (CIS), Germ Cell Cancers (GCC) are the most common cancer in young men, subdivided in seminoma (SE) and non-seminoma (NS). During physiological germ cell formation/maturation, epigenetic processes guard homeostasis by regulating the accessibility of the DNA to facilitate transcription. Epigenetic deregulation through genetic and environmental parameters (i.e. genvironment) could disrupt embryonic germ cell development, resulting in delayed or blocked maturation. This potentially facilitates the formation of CIS and progression to invasive GCC. Therefore, determining the epigenetic and functional genomic landscape in GCC cell lines could provide insight into the pathophysiology and etiology of GCC and provide guidance for targeted functional experiments.</p><p>Results</p><p>This study aims at identifying epigenetic footprints in SE and EC cell lines in genome-wide profiles by studying the interaction between gene expression, DNA CpG methylation and histone modifications, and their function in the pathophysiology and etiology of GCC. Two well characterized GCC-derived cell lines were compared, one representative for SE (TCam-2) and the other for EC (NCCIT). Data were acquired using the Illumina HumanHT-12-v4 (gene expression) and HumanMethylation450 BeadChip (methylation) microarrays as well as ChIP-sequencing (activating histone modifications (H3K4me3, H3K27ac)). Results indicate known germ cell markers not only to be differentiating between SE and NS at the expression level, but also in the epigenetic landscape.</p><p>Conclusion</p><p>The overall similarity between TCam-2/NCCIT support an erased embryonic germ cell arrested in early gonadal development as common cell of origin although the exact developmental stage from which the tumor cells are derived might differ. Indeed, subtle difference in the (integrated) epigenetic and expression profiles indicate TCam-2 to exhibit a more germ cell-like profile, whereas NCCIT shows a more pluripotent phenotype. The results provide insight into the functional genome in GCC cell lines.</p></div

Overlap between top differentiating genes (methylation/histone modification/gene expression).

<p>(Hypo) methylation, (high) gene expression and histone marker (enrichment) should be interpreted relative to the other cell line. Criteria for selection are described in the main text. Briefly, significant differential methylation of regions with sufficient probe density was identified by DMRforPairs (frequently, but not necessarily close to, the TSS). The difference in histone modification enrichment was assessed by significant differences in summed peak heights between the cell lines. Finally, a fold difference of 3.65 (boundary of 99% CI) was used as cutoff for differential gene expression. Gene lists are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098330#pone.0098330.s005" target="_blank">Table S1</a>, and overlap was determined based on matching gene symbol.</p

Methylation patterns of known germ cell markers (A–D) and significant DMRs for both cell lines (E, F).

<p>Dots depict individual CpGs and black boxes denote DMRs identified by DMRforPairs. Percentages below indicate average CG density in the plotted regions (calculated using the Repitools R package, gcContentCalc function (<a href="http://www.bioconductor.org/" target="_blank">http://www.bioconductor.org/</a>)). (A) <i>SOX2</i> [44%], (B) <i>SOX17</i> [46%], (C) <i>OCT3/4</i> (<i>POU5F1</i>), [52%] (D) <i>NANOG</i>, [44%] (E) <i>miR-371/2/3</i> cluster, [49%] (F) <i>GATA4</i> [53%].</p

Display of H3K4me3 and H3K27ac tracks for both NCCIT and TCam-2. (A) <i>SOX17</i>, (B) <i>SOX2</i>, (C) <i>OCT3/4</i> (<i>POU5F1</i>), (D) <i>NANOG</i>.

<p>Arrows indicate direction of transcription. Green boxes indicate markers specific for the histological subtype represented by the cell line. Black boxes  =  no difference between the cell lines; red boxes  =  not a marker for that cell type. Note the different ranges on the y-axis for H3K4me3 and H3K27ac.</p

Heat map of epigenetic markers and gene expression profiles.

<p>Genes with quantified methylation status around their TSS (based on Illumina annotation) and valid (see Materials &amp; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098330#s2" target="_blank">Methods</a>) measurement of their expression level were included (n = 11,620). Log-2 summed peak heights per gene were used as an estimate of histone marker enrichment. Variables scaled between 0 and 1. Hierarchical clustering was performed using complete linkage. Clusters of interest were identified based on a consistent enriched state for one or more of the active histone markers and a hypomethylated state around the TSS. Number of genes in the displayed right panel (zoomed in heat maps, top→bottom): 899/892 (TCam-2) and 1,224/37/308 (NCCIT). Gene expression was allowed to vary within clusters, but clusters with almost absent expression levels (completely red) were not selected. Gene symbols indicate genes that overlap with the analysis of top differentiating genes between the cell lines (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098330#pone-0098330-g006" target="_blank">Figure 6</a>). Gene symbols are listed alphabetically. An indication of the level for each gene in each column is presented by the color/shade and a blue line (for each column: left = 0, right = 1).</p

Relation between histone modification level (summed peak heights per gene) and expression level.

<p>Top and bottom right images depict the percentage of highly (&gt;p50) expressed genes calculated for an interval of summed peaks. For example, 5% of genes with a log2(summed peak height) of ≈5.5–7.5 were highly expressed. (B) Relation between CpG methylation (TSS/no TSS) and gene expression.</p

Motif enrichment in histone modification data.

<p>All motifs were significantly enriched in target over background sequences (p&lt;0.01). Fold enrichment is indicated relative to background. (A,B) Top ranking motifs in both cell lines showed strong overlap (top 10). (C,D) Motifs that differed strongly between the cell lines with regard to their enrichments were selected. A motif was assessed favorably if its ranking was high (≤20) for one cell line and low for the other cell line (or was absent in the other list of enriched motifs). Score: The difference in ranking was assessed based on the difference in relative position in the list (|1-(r_TCam-2/n_TCam-2)–1-(r_NCCIT/n_NCCIT)|≥15%, n = nr of enriched motifs, r is the rank of a specific motif in the list of enriched motifs for either cell line).</p

Genomic Characterisation of Small Cell Lung Cancer Patient-Derived Xenografts Generated from Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration Specimens

<div><p>Patient-derived xenograft (PDX) models generated from surgical specimens are gaining popularity as preclinical models of cancer. However, establishment of PDX lines from small cell lung cancer (SCLC) patients is difficult due to very limited amount of available biopsy material. We asked whether SCLC cells obtained from endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) could generate PDX lines that maintained the phenotypic and genetic characteristics of the primary tumor. Following successful EBUS-TBNA sampling for diagnostic purposes, we obtained an extra sample for cytologic analysis and implantation into the flanks of immunodeficient mice. Animals were monitored for engraftment for up to 6 months. Histopathologic and immunohistochemical analysis, and targeted next-generation re-sequencing, were then performed in both the primary sample and the derivative PDX line. A total of 12 patients were enrolled in the study. EBUS-TBNA aspirates yielded large numbers of viable tumor cells sufficient to inject between 18,750 and 1,487,000 cells per flank, and to yield microgram quantities of high-quality DNA. Of these, samples from 10 patients generated xenografts (engraftment rate 83%) with a mean latency of 104 days (range 63–188). All but one maintained a typical SCLC phenotype that closely matched the original sample. Identical mutations that are characteristic of SCLC were identified in both the primary sample and xenograft line. EBUS-TBNA has the potential to be a powerful tool in the development of new targeting strategies for SCLC patients by providing large numbers of viable tumor cells suitable for both xenografting and complex genomic analysis.</p></div

Next-Generation Sequence Analysis of Cancer Xenograft Models

<div><p>Next-generation sequencing (NGS) studies in cancer are limited by the amount, quality and purity of tissue samples. In this situation, primary xenografts have proven useful preclinical models. However, the presence of mouse-derived stromal cells represents a technical challenge to their use in NGS studies. We examined this problem in an established primary xenograft model of small cell lung cancer (SCLC), a malignancy often diagnosed from small biopsy or needle aspirate samples. Using an <i>in silico</i> strategy that assign reads according to species-of-origin, we prospectively compared NGS data from primary xenograft models with matched cell lines and with published datasets. We show here that low-coverage whole-genome analysis demonstrated remarkable concordance between published genome data and internal controls, despite the presence of mouse genomic DNA. Exome capture sequencing revealed that this enrichment procedure was highly species-specific, with less than 4% of reads aligning to the mouse genome. Human-specific expression profiling with RNA-Seq replicated array-based gene expression experiments, whereas mouse-specific transcript profiles correlated with published datasets from human cancer stroma. We conclude that primary xenografts represent a useful platform for complex NGS analysis in cancer research for tumours with limited sample resources, or those with prominent stromal cell populations.</p></div

Summary of the results produced by the proposed strategy to isolate and identify species-specific NGS reads in human xenografts.

<p>For each read category, the proportion (%) of the total number of reads is specified.</p