Genome instability (GI) and centrosomal alterations are common traits in human cancer [1, 2]. It is suspected that centrosome dysfunction may cause tumors by bringing about GI, but direct experimental proof is still lacking [3]. To explore the possible functional link between centrosome function and overgrowth, we have assayed the tumorigenic potential of a series of mutants that affect different centrosomal proteins in Drosophila. We have found that a significant number of such mutant conditions are tumorigenic in larval brain tissue, where self-renewing asymmetric division of neural stem cells is frequent, but not in symmetrically dividing epithelial cells. We have also found that mutations that increase GI without causing centrosome dysfunction are not tumorigenic in our assay. From these observations, we conclude that the tumors caused by centrosome dysfunction cannot be explained solely by the resulting genome instability. We propose that such tumors might be caused by impaired asymmetric division of neural stem cells [4]. These results show that centrosome loss, far from being innocuous, is a potentially dangerous condition in flies.

PTOV1 is a mitogenic protein that shuttles between the nucleus and the cytoplasm in a cell cycle-dependent manner. It consists of two homologous domains arranged in tandem that constitute a new class of protein modules. We show here that PTOV1 interacts with the lipid raft protein flotillin-1, with which it copurifies in detergent-insoluble floating fractions. Flotillin-1 colocalized with PTOV1 not only at the plasma membrane but, unexpectedly, also in the nucleus, as demonstrated by immunocytochemistry and subcellular fractionation of endogenous and exogenous flotillin-1. Flotillin-1 entered the nucleus concomitant with PTOV1, shortly before the initiation of the S phase. Protein levels of PTOV1 and flotillin-1 oscillated during the cell cycle, with a peak in S. Depletion of PTOV1 significantly inhibited nuclear localization of flotillin-1, whereas depletion of flotillin-1 did not affect nuclear localization of PTOV1. Depletion of either protein markedly inhibited cell proliferation under basal conditions. Overexpression of PTOV1 or flotillin-1 strongly induced proliferation, which required their localization to the nucleus, and was dependent on the reciprocal protein. These observations suggest that PTOV1 assists flotillin-1 in its translocation to the nucleus and that both proteins are required for cell proliferation.PTOV1 was identified as a novel gene and protein during a differential display screening for gene expression in prostate cancer (4). PTOV1 is overexpressed in 71% of prostate carcinomas and in 80% of samples with prostate intraepithelial neoplasia, while it its barely detectable in normal prostate epithelium (34). In an independent study, PTOV1 was also found to be one of the genes most discriminant between normal and carcinomatous prostate (44). This gene codes for a protein that consists of two highly related sequence blocks arranged in tandem that are conserved in humans, rodents, and flies (4). The PTOV domain does not resemble any other protein motif described so far. A PTOV domain is also present in another protein, PTOV2 (4), later identified as ARC92, a component of transcriptional coregulator multisubunit complexes (28). Recently, PTOV2/ARC92, renamed as ACID-1, has been identified as a critical protein of Mediator complexes for the recruitment of activators to the basal transcriptional machinery (26). We have used yeast two-hybrid screenings to search for interaction partners of PTOV1, yielding a specific interaction with the lipid raft-associated protein flotillin-1 (5). Lipid rafts are specialized membrane microdomains enriched in glycosphingolipids, cholesterol, and glycosylphosphatidylinositol-anchored proteins (36). It is now believed that lipid rafts represent a central feature of cellular organization crucial for membrane trafficking events and for specific signaling cascades (36). Ubiquitous markers of lipid rafts are proteins of the Reggie/flotillin family (36, 38). Reggie-1 and Reggie-2, whose human orthologues are flotillin-2 and flotillin-1, respectively, were originally ide...

Although germline copy-number variants (CNVs) are the genetic cause of multiple hereditary diseases, detecting them from targeted next-generation sequencing data (NGS) remains a challenge. Existing tools perform well for large CNVs but struggle with single and multi-exon alterations. The aim of this work is to evaluate CNV calling tools working on gene panel NGS data and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies. Five tools (DECoN, CoNVaDING, panelcn.MOPS, ExomeDepth, and CODEX2) were tested against four genetic diagnostics datasets (two in-house and two external) for a total of 495 samples with 231 single and multi-exon validated CNVs. The evaluation was performed using the default and sensitivity-optimized parameters. Results showed that most tools were highly sensitive and specific, but the performance was dataset dependant. When evaluating them in our diagnostics scenario, DECoN and panelcn.MOPS detected all CNVs with the exception of one mosaic CNV missed by DECoN. However, DECoN outperformed panelcn.MOPS specificity achieving values greater than 0.90 when using the optimized parameters. In our inhouse datasets, DECoN and panelcn.MOPS showed the highest performance for CNV screening before orthogonal confirmation. Benchmarking and optimization code is freely available at https://github.com/TranslationalBioinforma ticsIGTP/CNVbenchmarkeR.

We wanted to implement an NGS strategy to globally analyze hereditary cancer with diagnostic quality while retaining the same degree of understanding and control we had in pre-NGS strategies. To do this, we developed the I2HCP panel, a custom bait library covering 122 hereditary cancer genes. We improved bait design, tested different NGS platforms and created a clinically driven custom data analysis pipeline. The I2HCP panel was developed using a training set of hereditary colorectal cancer, hereditary breast and ovarian cancer and neurofibromatosis patients and reached an accuracy, analytical sensitivity and specificity greater than 99%, which was maintained in a validation set. I2HCP changed our diagnostic approach, involving clinicians and a genetic diagnostics team from panel design to reporting. The new strategy improved diagnostic sensitivity, solved uncertain clinical diagnoses and identified mutations in new genes. We assessed the genetic variation in the complete set of hereditary cancer genes, revealing a complex variation landscape that coexists with the disease-causing mutation. We developed, validated and implemented a custom NGS-based strategy for hereditary cancer diagnostics that improved our previous workflows. Additionally, the existence of a rich genetic variation in hereditary cancer genes favors the use of this panel to investigate their role in cancer risk.

Next generation sequencing panels have been developed for hereditary cancer, although there is some debate about their cost-effectiveness compared to exome sequencing. The performance of two panels is compared to exome sequencing. Twenty-four patients were selected: ten with identified mutations (control set) and fourteen suspicious of hereditary cancer but with no mutation (discovery set). TruSight Cancer (94 genes) and a custom panel (122 genes) were assessed alongside exome sequencing. Eighty-three genes were targeted by the two panels and exome sequencing. More than 99% of bases had a read depth of over 30x in the panels, whereas exome sequencing covered 94%. Variant calling with standard settings identified the 10 mutations in the control set, with the exception of MSH6 c.255dupC using TruSight Cancer. In the discovery set, 240 unique non-silent coding and canonic splice-site variants were identified in the panel genes, 7 of them putatively pathogenic (in ATM, BARD1, CHEK2, ERCC3, FANCL, FANCM, MSH2). The three approaches identified a similar number of variants in the shared genes. Exomes were more expensive than panels but provided additional data. In terms of cost and depth, panels are a suitable option for genetic diagnostics, although exomes also identify variants in non-targeted genes.