Expression and Function of ETS Genes in Prostate Cancer

__Abstract__ Prostate cancer is a heterogeneous disease that is very common in elderly men in developed countries. Understanding the molecular and biological processes that contribute to tumor development and progressive growth is a challenging task. The fusion of the genes ERG and TMPRSS2 is the most frequent genomic alteration in prostate cancer. ERG is an oncogene that belongs to the family of ETS transcription factors. At lower frequency other members of this gene family are rearranged and overexpressed in prostate cancer. TMPRSS2 is an androgen-regulated gene that is preferentially expressed in the prostate. Most other ETS fusion partners are similarly regulated and prostate specific.

Circulating cell-free DNA: Translating prostate cancer genomics into clinical care.

Only in the past decade tremendous advances have been made in understanding prostate cancer genomics and consequently in applying new treatment strategies. As options regarding treatments are increasing so are the challenges in selecting the right treatment option for each patient and not the least, understanding the optimal time-point and sequence of applying available treatments. Critically, without reliable methods that enable sequential monitoring of evolving genotypes in individual patients, we will never reach effective personalised driven treatment approaches. This review focuses on the clinical implications of prostate cancer genomics and the potential of cfDNA in facilitating treatment management

Density of Phonon States in Superconducting FeSe as a Function of Temperature and Pressure

The temperature and pressure dependence of the partial density of phonon states of iron atoms in superconducting Fe1.01Se was studied by 57Fe nuclear inelastic scattering (NIS). The high energy resolution allows for a detailed observation of spectral properties. A sharpening of the optical phonon modes and shift of all spectral features towards higher energies by ~4% with decreasing temperature from 296 K to 10 K was found. However, no detectable change at the tetragonal - orthorhombic phase transition around 100 K was observed. Application of a pressure of 6.7 GPa, connected with an increase of the superconducting temperature from 8 K to 34 K, results in an increase of the optical phonon mode energies at 296 K by ~12%, and an even more pronounced increase for the lowest-lying transversal acoustic mode. Despite these strong pressure-induced modifications of the phonon-DOS we conclude that the pronounced increase of Tc in Fe1.01Se with pressure cannot be described in the framework of classical electron-phonon coupling. This result suggests the importance of spin fluctuations to the observed superconductivity

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Background Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). Methods To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. Results Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. Conclusions AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data.

Background Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).Methods To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.Results Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.Conclusions AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BACKGROUND: Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). METHODS: To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. RESULTS: Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. CONCLUSIONS: AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve

Molecular characterization of old local grapevine varieties from South East European countries

South East European (SEE) viticulture partially relies on native grapevine varieties, previously scarcely described. In order to characterize old local grapevine varieties and assess the level of synonymy and genetic diversity from SEE countries, we described and genotyped 122 accessions from Albania, Federation of Bosnia and Herzegovina (B&H), Croatia, Macedonia, Moldova, Montenegro, Republika Srpska (Bosnia and Herzegovina) and Romania on nine most commonly used microsatellite loci. As a result of the study a total of 86 different genotypes were identified. All loci were very polymorphic and a total of 96 alleles were detected, ranging from 8 to 14 alleles per locus, with an average allele number of 10.67. Overall observed heterozygosity was 0.759 and slightly lower than expected (0.789) while gene diversity per locus varied between 0.600 (VVMD27) and 0.906 (VVMD28). Eleven cases of synonymy and three of homonymy have been recorded for samples harvested from different countries. Cultivars with identical genotypes were mostly detected between neighboring countries. No clear differentiation between countries was detected although several specific alleles were detected. The integration of the obtained genetic data with ampelographic ones is very important for accurate identification of the SEE cultivars and provides a significant tool in cultivar preservation and utilization.

Genetic assessment of the pomological classification of plum Prunus domestica L. accessions sampled across Europe

The genotyping of European fruit tree collections has helped to identify synonyms, determine parentage, reveal key specimens in the collections and provide information on the development of modern cultivars from one or several progenitors. However, studies on European plum Prunus domestica have been lagging behind, mainly because of the hexaploid chromosome number. In this co-operative study, 104 accessions conserved by 14 partners across Europe were phenotyped for 20 descriptors, and genotyped for 8 SSR loci together with 8 reference cultivars. Based on the descriptors as well as additional information supplied by the partners, as well as the scientific and horticultural literature, each accession was assigned to one of six pomological groups; (1) egg plums sensu lato (E), (2) prunes of the French d’Agen type (P/A), (3) prunes of the Central-Southeast European Zwetschen type (P/Z), (4) greengages (G), (5) mirabelles (M) and (6) bullaces, damsons and var. pomariorum (D/B). A MANOVA conducted on descriptor data revealed significant differentiation among the pomological groups as well as a geographic impact on the differentiation of local plum accessions in Europe. SSR data showed that two trios and seven pairs of genotypes had very similar allele profiles and possibly are genetically identical in spite of different accession names. An AMOVA indicated sparse genetic differentiation when accessions were grouped according to geographic origin whereas significant differences were obtained among pomological groups. A Bayesian analysis of genetic structure, as well as a discriminant analysis of principal components (DAPC), further revealed levels of similarity among and within the different pomological groups, suggesting that egg plums sensu lato (E) and greengages (G) can be referred to subsp. domestica while damsons and bullaces (D/B) but also Central-Southeast European prunes (P/Z) show more affinity to subsp. insititia. The small and possibly heterogeneous groups with mirabelles (M) and prunes of the d’Agen type (P/A) take an intermediate position suggesting a hybridogenic origin

Expression of a Neuroendocrine Gene Signature in Gastric Tumor Cells from CEA 424-SV40 Large T Antigen-Transgenic Mice Depends on SV40 Large T Antigen

A large fraction of murine tumors induced by transgenic expression of SV40 large T antigen (SV40 TAg) exhibits a neuroendocrine phenotype. It is unclear whether SV40 TAg induces the neuroendocrine phenotype by preferential transformation of progenitor cells committed to the neuroendocrine lineage or by transcriptional activation of neuroendocrine genes. To address this question we analyzed CEA424-SV40 TAg-transgenic mice that develop spontaneous tumors in the antral stomach region. Immunohistology revealed expression of the neuroendocrine marker chromogranin A in tumor cells. By ELISA an 18-fold higher level of serotonin could be detected in the blood of tumor-bearing mice in comparison to nontransgenic littermates. Transcriptome analyses of antral tumors combined with gene set enrichment analysis showed significant enrichment of genes considered relevant for human neuroendocrine tumor biology. This neuroendocrine gene signature was also expressed in 424GC, a cell line derived from a CEA424-SV40 TAg tumor, indicating that the tumor cells exhibit a similar neuroendocrine phenotype also in vitro. Treatment of 424GC cells with SV40 TAg-specific siRNA downregulated expression of the neuroendocrine gene signature. SV40 TAg thus appears to directly induce a neuroendocrine gene signature in gastric carcinomas of CEA424-SV40 TAg-transgenic mice. This might explain the high incidence of neuroendocrine tumors in other murine SV40 TAg tumor models. Since the oncogenic effect of SV40 TAg is caused by inactivation of the tumor suppressor proteins p53 and RB1 and loss of function of these proteins is commonly observed in human neuroendocrine tumors, a similar mechanism might cause neuroendocrine phenotypes in human tumors