Showing papers by "University of Trento published in 2018"

Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

Abstract: Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

GW170817: Measurements of Neutron Star Radii and Equation of State.

Abstract: On 17 August 2017, the LIGO and Virgo observatories made the first direct detection of gravitational waves from the coalescence of a neutron star binary system. The detection of this gravitational-wave signal, GW170817, offers a novel opportunity to directly probe the properties of matter at the extreme conditions found in the interior of these stars. The initial, minimal-assumption analysis of the LIGO and Virgo data placed constraints on the tidal effects of the coalescing bodies, which were then translated to constraints on neutron star radii. Here, we expand upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars. Our analysis employs two methods: the use of equation-of-state-insensitive relations between various macroscopic properties of the neutron stars and the use of an efficient parametrization of the defining function pðρÞ of the equation of state itself. From the LIGO and Virgo data alone and the first method, we measure the two neutron star radii as R1 ¼ 10.8 þ2.0 −1.7 km for the heavier star and R2 ¼ 10.7 þ2.1 −1.5 km for the lighter star at the 90% credible level. If we additionally require that the equation of state supports neutron stars with masses larger than 1.97 M⊙ as required from electromagnetic observations and employ the equation-of-state parametrization, we further constrain R1 ¼ 11.9 þ1.4 −1.4 km and R2 ¼ 11.9 þ1.4 −1.4 km at the 90% credible level. Finally, we obtain constraints on pðρÞ at supranuclear densities, with pressure at twice nuclear saturation density measured at 3.5 þ2.7 −1.7 × 1034 dyn cm−2 at the 90% level.

Species-level functional profiling of metagenomes and metatranscriptomes.

Abstract: Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types.

QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

Abstract: We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

A Survey on Learning to Hash

Abstract: Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.

Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO, Advanced Virgo and KAGRA

Abstract: We present possible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron star systems, which are the most promising targets for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and 90% credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5– 20 deg2 requires at least three detectors of sensitivity within a factor of ∼2 of each other and with a broad frequency bandwidth. When all detectors, including KAGRA and the third LIGO detector in India, reach design sensitivity, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

Functional aspects of meningeal lymphatics in ageing and Alzheimer’s disease

Abstract: Ageing is a major risk factor for many neurological pathologies, but its mechanisms remain unclear. Unlike other tissues, the parenchyma of the central nervous system (CNS) lacks lymphatic vasculature and waste products are removed partly through a paravascular route. (Re)discovery and characterization of meningeal lymphatic vessels has prompted an assessment of their role in waste clearance from the CNS. Here we show that meningeal lymphatic vessels drain macromolecules from the CNS (cerebrospinal and interstitial fluids) into the cervical lymph nodes in mice. Impairment of meningeal lymphatic function slows paravascular influx of macromolecules into the brain and efflux of macromolecules from the interstitial fluid, and induces cognitive impairment in mice. Treatment of aged mice with vascular endothelial growth factor C enhances meningeal lymphatic drainage of macromolecules from the cerebrospinal fluid, improving brain perfusion and learning and memory performance. Disruption of meningeal lymphatic vessels in transgenic mouse models of Alzheimer’s disease promotes amyloid-β deposition in the meninges, which resembles human meningeal pathology, and aggravates parenchymal amyloid-β accumulation. Meningeal lymphatic dysfunction may be an aggravating factor in Alzheimer’s disease pathology and in age-associated cognitive decline. Thus, augmentation of meningeal lymphatic function might be a promising therapeutic target for preventing or delaying age-associated neurological diseases.

The long tail of oncogenic drivers in prostate cancer

Abstract: Comprehensive genomic characterization of prostate cancer has identified recurrent alterations in genes involved in androgen signaling, DNA repair, and PI3K signaling, among others. However, larger and uniform genomic analysis may identify additional recurrently mutated genes at lower frequencies. Here we aggregate and uniformly analyze exome sequencing data from 1,013 prostate cancers. We identify and validate a new class of E26 transformation-specific (ETS)-fusion-negative tumors defined by mutations in epigenetic regulators, as well as alterations in pathways not previously implicated in prostate cancer, such as the spliceosome pathway. We find that the incidence of significantly mutated genes (SMGs) follows a long-tail distribution, with many genes mutated in less than 3% of cases. We identify a total of 97 SMGs, including 70 not previously implicated in prostate cancer, such as the ubiquitin ligase CUL3 and the transcription factor SPEN. Finally, comparing primary and metastatic prostate cancer identifies a set of genomic markers that may inform risk stratification.

DarkSide-20k: A 20 tonne two-phase LAr TPC for direct dark matter detection at LNGS

Abstract: Building on the successful experience in operating the DarkSide-50 detector, the DarkSide Collaboration is going to construct DarkSide-20k, a direct WIMP search detector using a two-phase Liquid Argon Time Projection Chamber (LAr TPC) with an active (fiducial) mass of 23 t (20 t). This paper describes a preliminary design for the experiment, in which the DarkSide-20k LAr TPC is deployed within a shield/veto with a spherical Liquid Scintillator Veto (LSV) inside a cylindrical Water Cherenkov Veto (WCV). This preliminary design provides a baseline for the experiment to achieve its physics goals, while further development work will lead to the final optimization of the detector parameters and an eventual technical design. Operation of DarkSide-50 demonstrated a major reduction in the dominant 39Ar background when using argon extracted from an underground source, before applying pulse shape analysis. Data from DarkSide-50, in combination with MC simulation and analytical modeling, shows that a rejection factor for discrimination between electron and nuclear recoils of $>3 \times 10^{9}$ is achievable. This, along with the use of the veto system and utilizing silicon photomultipliers in the LAr TPC, are the keys to unlocking the path to large LAr TPC detector masses, while maintaining an experiment in which less than $< 0.1$ events (other than $ u$ -induced nuclear recoils) is expected to occur within the WIMP search region during the planned exposure. DarkSide-20k will have ultra-low backgrounds than can be measured in situ, giving sensitivity to WIMP-nucleon cross sections of $1.2 \times 10^{-47}$ cm2 ( $1.1 \times 10^{-46}$ cm2) for WIMPs of 1 TeV/c2 (10 TeV/c2) mass, to be achieved during a 5 yr run producing an exposure of 100 t yr free from any instrumental background.

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Abstract: Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The b jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV).

Deformable GANs for Pose-Based Human Image Generation

Abstract: In this paper we address the problem of generating person images conditioned on a given pose. Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose. In order to deal with pixel-to-pixel misalignments caused by the pose differences, we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 and L2 losses in order to match the details of the generated image with the target image. We test our approach using photos of persons in different poses and we compare our method with previous work in this area showing state-of-the-art results in two benchmarks. Our method can be applied to the wider field of deformable object generation, provided that the pose of the articulated object can be extracted using a keypoint detector.

Clinical and Genomic Characterization of Treatment-Emergent Small-Cell Neuroendocrine Prostate Cancer: A Multi-institutional Prospective Study

Abstract: Purpose The prevalence and features of treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC) are not well characterized in the era of modern androgen receptor (AR)-targeting therapy. We sought to characterize the clinical and genomic features of t-SCNC in a multi-institutional prospective study. Methods Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent metastatic tumor biopsy and were followed for survival. Metastatic biopsy specimens underwent independent, blinded pathology review along with RNA/DNA sequencing. Results A total of 202 consecutive patients were enrolled. One hundred forty-eight (73%) had prior disease progression on abiraterone and/or enzalutamide. The biopsy evaluable rate was 79%. The overall incidence of t-SCNC detection was 17%. AR amplification and protein expression were present in 67% and 75%, respectively, of t-SCNC biopsy specimens. t-SCNC was detected at similar proportions in bone, node, and visceral organ biopsy specimens. Genomic alterations in the DNA repair pathway were nearly mutually exclusive with t-SCNC differentiation ( P = .035). Detection of t-SCNC was associated with shortened overall survival among patients with prior AR-targeting therapy for mCRPC (hazard ratio, 2.02; 95% CI, 1.07 to 3.82). Unsupervised hierarchical clustering of the transcriptome identified a small-cell-like cluster that further enriched for adverse survival outcomes (hazard ratio, 3.00; 95% CI, 1.25 to 7.19). A t-SCNC transcriptional signature was developed and validated in multiple external data sets with > 90% accuracy. Multiple transcriptional regulators of t-SCNC were identified, including the pancreatic neuroendocrine marker PDX1. Conclusion t-SCNC is present in nearly one fifth of patients with mCRPC and is associated with shortened survival. The near-mutual exclusivity with DNA repair alterations suggests t-SCNC may be a distinct subset of mCRPC. Transcriptional profiling facilitates the identification of t-SCNC and novel therapeutic targets.

Exploring 4D quantum Hall physics with a 2D topological charge pump.

Abstract: The discovery of topological states of matter has greatly improved our understanding of phase transitions in physical systems Instead of being described by local order parameters, topological phases are described by global topological invariants and are therefore robust against perturbations A prominent example is the two-dimensional (2D) integer quantum Hall effect: it is characterized by the first Chern number, which manifests in the quantized Hall response that is induced by an external electric field Generalizing the quantum Hall effect to four-dimensional (4D) systems leads to the appearance of an additional quantized Hall response, but one that is nonlinear and described by a 4D topological invariant-the second Chern number Here we report the observation of a bulk response with intrinsic 4D topology and demonstrate its quantization by measuring the associated second Chern number By implementing a 2D topological charge pump using ultracold bosonic atoms in an angled optical superlattice, we realize a dynamical version of the 4D integer quantum Hall effect Using a small cloud of atoms as a local probe, we fully characterize the nonlinear response of the system via in situ imaging and site-resolved band mapping Our findings pave the way to experimentally probing higher-dimensional quantum Hall systems, in which additional strongly correlated topological phases, exotic collective excitations and boundary phenomena such as isolated Weyl fermions are predicted

Search for dark matter and other new phenomena in events with an energetic jet and large missing transverse momentum using the ATLAS detector

Abstract: Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses proton-proton collision data corresponding to an integrated luminosity of 36.1 fb−1 at a centre-of-mass energy of 13 TeV collected in 2015 and 2016 with the ATLAS detector at the Large Hadron Collider. Events are required to have at least one jet with a transverse momentum above 250 GeV and no leptons (e or μ). Several signal regions are considered with increasing requirements on the missing transverse momentum above 250 GeV. Good agreement is observed between the number of events in data and Standard Model predictions. The results are translated into exclusion limits in models with pair-produced weakly interacting dark-matter candidates, large extra spatial dimensions, and supersymmetric particles in several compressed scenarios.

Late Time Afterglow Observations Reveal a Collimated Relativistic Jet in the Ejecta of the Binary Neutron Star Merger GW170817

Abstract: The binary neutron star (BNS) merger GW170817 was the first astrophysical source detected in gravitational waves and multiwavelength electromagnetic radiation. The almost simultaneous observation of a pulse of gamma rays proved that BNS mergers are associated with at least some short gamma-ray bursts (GRBs). However, the gamma-ray pulse was faint, casting doubt on the association of BNS mergers with the luminous, highly relativistic outflows of canonical short GRBs. Here we show that structured jets with a relativistic, energetic core surrounded by slower and less energetic wings produce afterglow emission that brightens characteristically with time, as recently seen in the afterglow of GW170817. Initially, we only see the relatively slow material moving towards us. As time passes, larger and larger sections of the outflow become visible, increasing the luminosity of the afterglow. The late appearance and increasing brightness of the multiwavelength afterglow of GW170817 allow us to constrain the geometry of its ejecta and thus reveal the presence of an off-axis jet pointing about 30° away from Earth. Our results confirm a single origin for BNS mergers and short GRBs: GW170817 produced a structured outflow with a highly relativistic core and a canonical short GRB. We did not see the bright burst because it was beamed away from Earth. However, approximately one in 20 mergers detected in gravitational waves will be accompanied by a bright, canonical short GRB.

A highly specific SpCas9 variant is identified by in vivo screening in yeast.

Abstract: Despite the utility of CRISPR-Cas9 nucleases for genome editing, the potential for off-target activity limits their application, especially for therapeutic purposes We developed a yeast-based assay to identify optimized Streptococcus pyogenes Cas9 (SpCas9) variants that enables simultaneous evaluation of on- and off-target activity We screened a library of SpCas9 variants carrying random mutations in the REC3 domain and identified mutations that increased editing accuracy while maintaining editing efficiency We combined four beneficial mutations to generate evoCas9, a variant that has fidelity exceeding both wild-type (79-fold improvement) and rationally designed Cas9 variants (fourfold average improvement), while maintaining near wild-type on-target editing efficiency (90% median residual activity) Evaluating evoCas9 on endogenous genomic loci, we demonstrated a substantially improved specificity and observed no off-target sites for four of the eight single guide RNAs (sgRNAs) tested Finally, we showed that following long-term expression (40 d), evoCas9 strongly limited the nonspecific cleavage of a difficult-to-discriminate off-target site and fully abrogated the cleavage of two additional off-target sites

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

Abstract: Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode.

ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors

Abstract: We present ASCERTAIN—a multimodal databa AS e for impli C it p ER sonali T y and A ffect recognit I o N using commercial physiological sensors. To our knowledge, ASCERTAIN is the first database to connect personality traits and emotional states via physiological responses . ASCERTAIN contains big-five personality scales and emotional self-ratings of 58 users along with their Electroencephalogram (EEG), Electrocardiogram (ECG), Galvanic Skin Response (GSR) and facial activity data, recorded using off-the-shelf sensors while viewing affective movie clips. We first examine relationships between users’ affective ratings and personality scales in the context of prior observations, and then study linear and non-linear physiological correlates of emotion and personality. Our analysis suggests that the emotion-personality relationship is better captured by non-linear rather than linear statistics. We finally attempt binary emotion and personality trait recognition using physiological features. Experimental results cumulatively confirm that personality differences are better revealed while comparing user responses to emotionally homogeneous videos, and above-chance recognition is achieved for both affective and personality dimensions.

Production and integration of the ATLAS Insertable B-Layer

Abstract: During the shutdown of the CERN Large Hadron Collider in 2013-2014, an additional pixel layer was installed between the existing Pixel detector of the ATLAS experiment and a new, smaller radius beam pipe. The motivation for this new pixel layer, the Insertable B-Layer (IBL), was to maintain or improve the robustness and performance of the ATLAS tracking system, given the higher instantaneous and integrated luminosities realised following the shutdown. Because of the extreme radiation and collision rate environment, several new radiation-tolerant sensor and electronic technologies were utilised for this layer. This paper reports on the IBL construction and integration prior to its operation in the ATLAS detector.

PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing

Abstract: Depth estimation and scene parsing are two particularly important tasks in visual scene understanding. In this paper we tackle the problem of simultaneous depth estimation and scene parsing in a joint CNN. The task can be typically treated as a deep multi-task learning problem [42]. Different from previous methods directly optimizing multiple tasks given the input training data, this paper proposes a novel multi-task guided prediction-and-distillation network (PAD-Net), which first predicts a set of intermediate auxiliary tasks ranging from low level to high level, and then the predictions from these intermediate auxiliary tasks are utilized as multi-modal input via our proposed multi-modal distillation modules for the final tasks. During the joint learning, the intermediate tasks not only act as supervision for learning more robust deep representations but also provide rich multi-modal information for improving the final tasks. Extensive experiments are conducted on two challenging datasets (i.e. NYUD-v2 and Cityscapes) for both the depth estimation and scene parsing tasks, demonstrating the effectiveness of the proposed approach.

Beyond the Required LISA Free-Fall Performance: New LISA Pathfinder Results down to 20 μHz.

Abstract: In the months since the publication of the first results, the noise performance of LISA Pathfinder has improved because of reduced Brownian noise due to the continued decrease in pressure around the test masses, from a better correction of noninertial effects, and from a better calibration of the electrostatic force actuation. In addition, the availability of numerous long noise measurement runs, during which no perturbation is purposely applied to the test masses, has allowed the measurement of noise with good statistics down to 20 μ Hz . The Letter presents the measured differential acceleration noise figure, which is at ( 1.74 ± 0.01 ) fm s − 2 / √ Hz above 2 mHz and ( 6 ± 1 ) × 10 fm s − 2 / √ Hz at 20 μ Hz , and discusses the physical sources for the measured noise. This performance provides an experimental benchmark demonstrating the ability to realize the low-frequency science potential of the LISA mission, recently selected by the European Space Agency.

Group Consistent Similarity Learning via Deep CRF for Person Re-identification

Abstract: Person re-identification benefits greatly from deep neural networks (DNN) to learn accurate similarity metrics and robust feature embeddings. However, most of the current methods impose only local constraints for similarity learning. In this paper, we incorporate constraints on large image groups by combining the CRF with deep neural networks. The proposed method aims to learn the "local similarity" metrics for image pairs while taking into account the dependencies from all the images in a group, forming "group similarities". Our method involves multiple images to model the relationships among the local and global similarities in a unified CRF during training, while combines multi-scale local similarities as the predicted similarity in testing. We adopt an approximate inference scheme for estimating the group similarity, enabling end-to-end training. Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities. The overall results outperform those by state-of-the-arts with considerable margins on three widely-used benchmarks.

Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation

Abstract: Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks. Following this line of research, in this paper we introduce a novel approach for monocular depth estimation. Similarly to previous works, our method employs a continuous CRF to fuse multi-scale information derived from different layers of a front-end Convolutional Neural Network (CNN). Differently from past works, our approach benefits from a structured attention model which automatically regulates the amount of information transferred between corresponding features at different scales. Importantly, the proposed attention model is seamlessly integrated into the CRF, allowing end-to-end training of the entire architecture. Our extensive experimental evaluation demonstrates the effectiveness of the proposed method which is competitive with previous methods on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2 dataset.

Large extracellular vesicles carry most of the tumour DNA circulating in prostate cancer patient plasma.

Abstract: Cancer-derived extracellular vesicles (EVs) are membrane-enclosed structures of highly variable size. EVs contain a myriad of substances (proteins, lipid, RNA, DNA) that provide a reservoir of circulating molecules, thus offering a good source of biomarkers. We demonstrate here that large EVs (L-EV) (large oncosomes) isolated from prostate cancer (PCa) cells and patient plasma are an EV population that is enriched in chromosomal DNA, including large fragments up to 2 million base pair long. While L-EVs and small EVs (S-EV) (exosomes) isolated from the same cells contained similar amounts of protein, the DNA was more abundant in L-EV, despite S-EVs being more numerous. Consistent with in vitro observations, the abundance of DNA in L-EV obtained from PCa patient plasma was variable but frequently high. Conversely, negligible amounts of DNA were present in the S-EVs from the same patients. Controlled experimental conditions, with spike-ins of L-EVs and S-EVs from cancer cells in human plasma from healthy subjects, showed that circulating DNA is almost exclusively enclosed in L-EVs. Whole genome sequencing revealed that the DNA in L-EVs reflects genetic aberrations of the cell of origin, including copy number variations of genes frequently altered in metastatic PCa (i.e. MYC, AKT1, PTK2, KLF10 and PTEN). These results demonstrate that L-EV-derived DNA reflects the genomic make-up of the tumour of origin. They also support the conclusion that L-EVs are the fraction of plasma EVs with DNA content that should be interrogated for tumour-derived genomic alterations.