Genome-wide Screens Implicate Loss of Cullin Ring Ligase 3 in Persistent Proliferation and Genome Instability in TP53-Deficient Cells.

TP53 deficiency is the most common alteration in cancer; however, this alone is typically insufficient to drive tumorigenesis. To identify genes promoting tumorigenesis in combination with TP53 deficiency, we perform genome-wide CRISPR-Cas9 knockout screens coupled with proliferation and transformation assays in isogenic cell lines. Loss of several known tumor suppressors enhances cellular proliferation and transformation. Loss of neddylation pathway genes promotes uncontrolled proliferation exclusively in TP53-deficient cells. Combined loss of CUL3 and TP53 activates an oncogenic transcriptional program governed by the nuclear factor κB (NF-κB), AP-1, and transforming growth factor ß (TGF-ß) pathways. This program maintains persistent cellular proliferation, induces partial epithelial to mesenchymal transition, and increases DNA damage, genomic instability, and chromosomal rearrangements. Our findings reveal CUL3 loss as a key event stimulating persistent proliferation in TP53-deficient cells. These findings may be clinically relevant, since TP53-CUL3-deficient cells are highly sensitive to ataxia telangiectasia mutated (ATM) inhibition, exposing a vulnerability that could be exploited for cancer treatment.

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype.

Ordering of mutations in preinvasive disease stages of esophageal carcinogenesis.

Cancer genome sequencing studies have identified numerous driver genes, but the relative timing of mutations in carcinogenesis remains unclear. The gradual progression from premalignant Barrett's esophagus to esophageal adenocarcinoma (EAC) provides an ideal model to study the ordering of somatic mutations. We identified recurrently mutated genes and assessed clonal structure using whole-genome sequencing and amplicon resequencing of 112 EACs. We next screened a cohort of 109 biopsies from 2 key transition points in the development of malignancy: benign metaplastic never-dysplastic Barrett's esophagus (NDBE; n=66) and high-grade dysplasia (HGD; n=43). Unexpectedly, the majority of recurrently mutated genes in EAC were also mutated in NDBE. Only TP53 and SMAD4 mutations occurred in a stage-specific manner, confined to HGD and EAC, respectively. Finally, we applied this knowledge to identify high-risk Barrett's esophagus in a new non-endoscopic test. In conclusion, mutations in EAC driver genes generally occur exceptionally early in disease development with profound implications for diagnostic and therapeutic strategies.

Taxonomic study of the genera Halogeometricum and Halosarcina: transfer of Halosarcina limi and Halosarcina pallida to the genus Halogeometricum as Halogeometricum limi comb. nov. and Halogeometricum pallidum comb. nov., respectively.

Members of the haloarchaeal genera Halosarcina and Halogeometricum (family Halobacteriaceae) are closely related to each other and show 96.6-98 % 16S rRNA gene sequence similarity. This is higher than the accepted threshold value (95 %) to separate two genera, and a taxonomic study using a polyphasic approach of all four members of the two genera was conducted to clarify their relationships. Polar lipid profiles indicated that Halogeometricum rufum RO1-4(T), Halosarcina pallida BZ256(T) and Halosarcina limi RO1-6(T) are related more to each other than to Halogeometricum borinquense CGMCC 1.6168(T). Phylogenetic analyses using the sequences of three different genes (16S rRNA gene, rpoB' and EF-2) strongly supported the monophyly of these four species, showing that they formed a distinct clade, separate from the related genera Halopelagius, Halobellus, Haloquadratum, Haloferax and Halogranum. The results indicate that the four species should be assigned to the same genus, and it is proposed that Halosarcina pallida and Halosarcina limi be transferred to the genus Halogeometricum as Halogeometricum pallidum comb. nov. (type strain, BZ256(T) = KCTC 4017(T) = JCM 14848(T)) and Halogeometricum limi comb. nov. (type strain, RO1-6(T) = CGMCC 1.8711(T) = JCM 16054(T)).

BayesPeak--an R package for analysing ChIP-seq data.

MOTIVATION: Identification of genomic regions of interest in ChIP-seq data, commonly referred to as peak-calling, aims to find the locations of transcription factor binding sites, modified histones or nucleosomes. The BayesPeak algorithm was developed to model the data structure using Bayesian statistical techniques and was shown to be a reliable method, but did not have a full-genome implementation. RESULTS: In this note we present BayesPeak, an R package for genome-wide peak-calling that provides a flexible implementation of the BayesPeak algorithm and is compatible with downstream BioConductor packages. The BayesPeak package introduces a new method for summarizing posterior probability output, along with methods for handling overfitting and support for parallel processing. We briefly compare the package with other common peak-callers. AVAILABILITY: Available as part of BioConductor version 2.6. URL: http://bioconductor.org/packages/release/bioc/html/BayesPeak.html.

BeadDataPackR: A Tool to Facilitate the Sharing of Raw Data from Illumina BeadArray Studies.

Microarray technologies have been an increasingly important tool in cancer research in the last decade, and a number of initiatives have sought to stress the importance of the provision and sharing of raw microarray data. Illumina BeadArrays provide a particular problem in this regard, as their random construction simultaneously adds value to analysis of the raw data and obstructs the sharing of those data.We present a compression scheme for raw Illumina BeadArray data, designed to ease the burdens of sharing and storing such data, that is implemented in the BeadDataPackR BioConductor package (http://bioconductor.org/packages/release/bioc/html/BeadDataPackR.html). It offers two key advantages over off-the-peg compression tools. First it uses knowledge of the data formats to achieve greater compression than other approaches, and second it does not need to be decompressed for analysis, but rather the values held within can be directly accessed.