Copy number architectures define treatment-mediated selection of lethal prostate cancer clones.

Despite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient evolutionary relationships of metastases that evade treatment, we perform genome-wide copy number profiling and bespoke approaches targeting the androgen receptor (AR) on 167 metastatic regions from 11 organs harvested post-mortem from 10 men who died from prostate cancer. We identify diverse and patient-unique alterations clustering around the AR in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of AR-neutral clones. Using the genomic boundaries of pan-autosome copy number changes, we confirm a common clone of origin across metastases and diagnostic biopsies, and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations. These autosome-defined clusters are characterized by cluster-specific AR gene architectures, and in two index cases are topologically more congruent than by chance (p-values 3.07 × 10-8 and 6.4 × 10-4). Integration with anatomical sites suggests patterns of spread and points of genomic divergence. Here, we show that copy number boundaries identify treatment-selected clones with putatively distinct lethal trajectories.

An interactive web application for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens.

A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing false-positive signals while maintaining sensitivity in identifying relevant genetic dependencies. Here, we present CRISPRcleanR WebApp , a web application enabling access to CRISPRcleanR through an intuitive interface. CRISPRcleanR WebApp removes the complexity of R/python language user interactions; provides user-friendly access to a complete analytical pipeline, not requiring any data pre-processing and generating gene-level summaries of essentiality with associated statistical scores; and offers a range of interactively explorable plots while supporting a more comprehensive range of CRISPR guide RNAs' libraries than the original package. CRISPRcleanR WebApp is available at https://crisprcleanr-webapp.fht.org/.

Accumulation of copy number alterations and clinical progression across advanced prostate cancer.

BACKGROUND: Genomic copy number alterations commonly occur in prostate cancer and are one measure of genomic instability. The clinical implication of copy number change in advanced prostate cancer, which defines a wide spectrum of disease from high-risk localised to metastatic, is unknown. METHODS: We performed copy number profiling on 688 tumour regions from 300 patients, who presented with advanced prostate cancer prior to the start of long-term androgen deprivation therapy (ADT), in the control arm of the prospective randomised STAMPEDE trial. Patients were categorised into metastatic states as follows; high-risk non-metastatic with or without local lymph node involvement, or metastatic low/high volume. We followed up patients for a median of 7 years. Univariable and multivariable Cox survival models were fitted to estimate the association between the burden of copy number alteration as a continuous variable and the hazard of death or disease progression. RESULTS: The burden of copy number alterations positively associated with radiologically evident distant metastases at diagnosis (P=0.00006) and showed a non-linear relationship with clinical outcome on univariable and multivariable analysis, characterised by a sharp increase in the relative risk of progression (P=0.003) and death (P=0.045) for each unit increase, stabilising into more modest increases with higher copy number burdens. This association between copy number burden and outcome was similar in each metastatic state. Copy number loss occurred significantly more frequently than gain at the lowest copy number burden quartile (q=4.1 × 10-6). Loss of segments in chromosome 5q21-22 and gains at 8q21-24, respectively including CHD1 and cMYC occurred more frequently in cases with higher copy number alteration (for either region: Kolmogorov-Smirnov distance, 0.5; adjusted P<0.0001). Copy number alterations showed variability across tumour regions in the same prostate. This variance associated with increased risk of distant metastases (Kruskal-Wallis test P=0.037). CONCLUSIONS: Copy number alteration in advanced prostate cancer associates with increased risk of metastases at diagnosis. Accumulation of a limited number of copy number alterations associates with most of the increased risk of disease progression and death. The increased likelihood of involvement of specific segments in high copy number alteration burden cancers may suggest an order underlying the accumulation of copy number changes. TRIAL REGISTRATION: ClinicalTrials.gov NCT00268476 , registered on December 22, 2005. EudraCT  2004-000193-31 , registered on October 4, 2004.

Genome-wide plasma DNA methylation features of metastatic prostate cancer.

Tumor DNA circulates in the plasma of cancer patients admixed with DNA from noncancerous cells. The genomic landscape of plasma DNA has been characterized in metastatic castration-resistant prostate cancer (mCRPC) but the plasma methylome has not been extensively explored. Here, we performed next-generation sequencing (NGS) on plasma DNA with and without bisulfite treatment from mCRPC patients receiving either abiraterone or enzalutamide in the pre- or post-chemotherapy setting. Principal component analysis on the mCRPC plasma methylome indicated that the main contributor to methylation variance (principal component one, or PC1) was strongly correlated with genomically determined tumor fraction (r = -0.96; P < 10-8) and characterized by hypermethylation of targets of the polycomb repressor complex 2 components. Further deconvolution of the PC1 top-correlated segments revealed that these segments are comprised of methylation patterns specific to either prostate cancer or prostate normal epithelium. To extract information specific to an individual's cancer, we then focused on an orthogonal methylation signature, which revealed enrichment for androgen receptor binding sequences and hypomethylation of these segments associated with AR copy number gain. Individuals harboring this methylation pattern had a more aggressive clinical course. Plasma methylome analysis can accurately quantitate tumor fraction and identify distinct biologically relevant mCRPC phenotypes.

An Association Rule Mining Approach to Discover lncRNAs Expression Patterns in Cancer Datasets.

In the past few years, the role of long noncoding RNAs (lncRNAs) in tumor development and progression has been disclosed although their mechanisms of action remain to be elucidated. An important contribution to the comprehension of lncRNAs biology in cancer could be obtained through the integrated analysis of multiple expression datasets. However, the growing availability of public datasets requires new data mining techniques to integrate and describe relationship among data. In this perspective, we explored the powerness of the Association Rule Mining (ARM) approach in gene expression data analysis. By the ARM method, we performed a meta-analysis of cancer-related microarray data which allowed us to identify and characterize a set of ten lncRNAs simultaneously altered in different brain tumor datasets. The expression profiles of the ten lncRNAs appeared to be sufficient to distinguish between cancer and normal tissues. A further characterization of this lncRNAs signature through a comodulation expression analysis suggested that biological processes specific of the nervous system could be compromised.

γ-PGA Hydrolases of Phage Origin in Bacillus subtilis and Other Microbial Genomes.

Poly-γ-glutamate (γ-PGA) is an industrially interesting polymer secreted mainly by members of the class Bacilli which forms a shield able to protect bacteria from phagocytosis and phages. Few enzymes are known to degrade γ-PGA; among them is a phage-encoded γ-PGA hydrolase, PghP. The supposed role of PghP in phages is to ensure access to the surface of bacterial cells by dismantling the γ-PGA barrier. We identified four unannotated B. subtilis genes through similarity of their encoded products to PghP; in fact these genes reside in prophage elements of B. subtilis genome. The recombinant products of two of them demonstrate efficient polymer degradation, confirming that sequence similarity reflects functional homology. Genes encoding similar γ-PGA hydrolases were identified in phages specific for the order Bacillales and in numerous microbial genomes, not only belonging to that order. The distribution of the γ-PGA biosynthesis operon was also investigated with a bioinformatics approach; it was found that the list of organisms endowed with γ-PGA biosynthetic functions is larger than expected and includes several pathogenic species. Moreover in non-Bacillales bacteria the predicted γ-PGA hydrolase genes are preferentially found in species that do not have the genetic asset for polymer production. Our findings suggest that γ-PGA hydrolase genes might have spread across microbial genomes via horizontal exchanges rather than via phage infection. We hypothesize that, in natural habitats rich in γ-PGA supplied by producer organisms, the availability of hydrolases that release glutamate oligomers from γ-PGA might be a beneficial trait under positive selection.

Chronic Replication Problems Impact Cell Morphology and Adhesion of DNA Ligase I Defective Cells.

Moderate DNA damage resulting from metabolic activities or sub-lethal doses of exogenous insults may eventually lead to cancer onset. Human 46BR.1G1 cells bear a mutation in replicative DNA ligase I (LigI) which results in low levels of replication-dependent DNA damage. This replication stress elicits a constitutive phosphorylation of the ataxia telangiectasia mutated (ATM) checkpoint kinase that fails to arrest cell cycle progression or to activate apoptosis or cell senescence. Stable transfection of wild type LigI, as in 7A3 cells, prevents DNA damage and ATM activation. Here we show that parental 46BR.1G1 and 7A3 cells differ in important features such as cell morphology, adhesion and migration. Comparison of gene expression profiles in the two cell lines detects Bio-Functional categories consistent with the morphological and migration properties of LigI deficient cells. Interestingly, ATM inhibition makes 46BR.1G1 more similar to 7A3 cells for what concerns morphology, adhesion and expression of cell-cell adhesion receptors. These observations extend the influence of the DNA damage response checkpoint pathways and unveil a role for ATM kinase activity in modulating cell biology parameters relevant to cancer progression.

CorrelaGenes: a new tool for the interpretation of the human transcriptome.

BACKGROUND: The amount of gene expression data available in public repositories has grown exponentially in the last years, now requiring new data mining tools to transform them in information easily accessible to biologists. RESULTS: By exploiting expression data publicly available in the Gene Expression Omnibus (GEO) database, we developed a new bioinformatics tool aimed at the identification of genes whose expression appeared simultaneously altered in different experimental conditions, thus suggesting co-regulation or coordinated action in the same biological process. To accomplish this task, we used the 978 human GEO Curated DataSets and we manually performed the selection of 2,109 pair-wise comparisons based on their biological rationale. The lists of differentially expressed genes, obtained from the selected comparisons, were stored in a PostgreSQL database and used as data source for the CorrelaGenes tool. Our application uses a customized Association Rule Mining (ARM) algorithm to identify sets of genes showing expression profiles correlated with a gene of interest. The significance of the correlation is measured coupling the Lift, a well-known standard ARM index, and the χ(2) p value. The manually curated selection of the comparisons and the developed algorithm constitute a new approach in the field of gene expression profiling studies. Simulation performed on 100 randomly selected target genes allowed us to evaluate the efficiency of the procedure and to obtain preliminary data demonstrating the consistency of the results. CONCLUSIONS: The preliminary results of the simulation showed how CorrelaGenes could contribute to the characterization of molecular pathways and biological processes integrating data obtained from other applications and available in public repositories.