Analyzing Somatic Genome Rearrangements in Human Cancers by Using Whole-Exome Sequencing.

Although exome sequencing data are generated primarily to detect single-nucleotide variants and indels, they can also be used to identify a subset of genomic rearrangements whose breakpoints are located in or near exons. Using >4,600 tumor and normal pairs across 15 cancer types, we identified over 9,000 high confidence somatic rearrangements, including a large number of gene fusions. We find that the 5' fusion partners of functional fusions are often housekeeping genes, whereas the 3' fusion partners are enriched in tyrosine kinases. We establish the oncogenic potential of ROR1-DNAJC6 and CEP85L-ROS1 fusions by showing that they can promote cell proliferation in vitro and tumor formation in vivo. Furthermore, we found that ∼4% of the samples have massively rearranged chromosomes, many of which are associated with upregulation of oncogenes such as ERBB2 and TERT. Although the sensitivity of detecting structural alterations from exomes is considerably lower than that from whole genomes, this approach will be fruitful for the multitude of exomes that have been and will be generated, both in cancer and in other diseases.

Characterization of HPV and host genome interactions in primary head and neck cancers.

Previous studies have established that a subset of head and neck tumors contains human papillomavirus (HPV) sequences and that HPV-driven head and neck cancers display distinct biological and clinical features. HPV is known to drive cancer by the actions of the E6 and E7 oncoproteins, but the molecular architecture of HPV infection and its interaction with the host genome in head and neck cancers have not been comprehensively described. We profiled a cohort of 279 head and neck cancers with next generation RNA and DNA sequencing and show that 35 (12.5%) tumors displayed evidence of high-risk HPV types 16, 33, or 35. Twenty-five cases had integration of the viral genome into one or more locations in the human genome with statistical enrichment for genic regions. Integrations had a marked impact on the human genome and were associated with alterations in DNA copy number, mRNA transcript abundance and splicing, and both inter- and intrachromosomal rearrangements. Many of these events involved genes with documented roles in cancer. Cancers with integrated vs. nonintegrated HPV displayed different patterns of DNA methylation and both human and viral gene expressions. Together, these data provide insight into the mechanisms by which HPV interacts with the human genome beyond expression of viral oncoproteins and suggest that specific integration events are an integral component of viral oncogenesis.

All-memristive neuromorphic computing with level-tuned neurons.

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

A high-bandwidth spintronic position sensor.

Position sensing with resolution down to the scale of a single atom is of key importance in nanoscale science and engineering. However, only optical-sensing methods are currently capable of non-contact sensing at such resolution over a high bandwidth. Here, we report a new non-contact, non-optical position-sensing concept based on detecting changes in a high-gradient magnetic field of a microscale magnetic dipole by means of spintronic sensors. Experimental measurements show a sensitivity of up to 40 Ω/µm, a linear range greater than 10 µm and a noise floor of 0.5 pm/√[Hz]. Also shown is the use of the sensor for position measurements for closed-loop control of a high-speed atomic force microscope with a frame rate of more than 1 frame/s.

Dynamic event-based optical identification and communication.

Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range, and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras and, for tracking, sparse neuromorphic optical flow computed with spiking neurons. The system is embedded in a simulated drone and evaluated in an asset monitoring use case. It is robust to relative movements and enables simultaneous communication with, and tracking of, multiple moving beacons. Finally, in a hardware lab prototype, we demonstrate for the first time beacon tracking performed simultaneously with state-of-the-art frequency communication in the kHz range.

Online Spatio-Temporal Learning in Deep Neural Networks.

Biological neural networks are equipped with an inherent capability to continuously adapt through online learning. This aspect remains in stark contrast to learning with error backpropagation through time (BPTT) that involves offline computation of the gradients due to the need to unroll the network through time. Here, we present an alternative online learning algorithm ic framework for deep recurrent neural networks (RNNs) and spiking neural networks (SNNs), called online spatio-temporal learning (OSTL). It is based on insights from biology and proposes the clear separation of spatial and temporal gradient components. For shallow SNNs, OSTL is gradient equivalent to BPTT enabling for the first time online training of SNNs with BPTT-equivalent gradients. In addition, the proposed formulation unveils a class of SNN architectures trainable online at low time complexity. Moreover, we extend OSTL to a generic form, applicable to a wide range of network architectures, including networks comprising long short-term memory (LSTM) and gated recurrent units (GRUs). We demonstrate the operation of our algorithm ic framework on various tasks from language modeling to speech recognition and obtain results on par with the BPTT baselines.

An exact mapping from ReLU networks to spiking neural networks.

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an exact mapping from a network with Rectified Linear Units (ReLUs) to an SNN that fires exactly one spike per neuron. For our constructive proof, we assume that an arbitrary multi-layer ReLU network with or without convolutional layers, batch normalization and max pooling layers was trained to high performance on some training set. Furthermore, we assume that we have access to a representative example of input data used during training and to the exact parameters (weights and biases) of the trained ReLU network. The mapping from deep ReLU networks to SNNs causes zero percent drop in accuracy on CIFAR10, CIFAR100 and the ImageNet-like data sets Places365 and PASS. More generally our work shows that an arbitrary deep ReLU network can be replaced by an energy-efficient single-spike neural network without any loss of performance.

On the visual analytic intelligence of neural networks.

Visual oddity task was conceived to study universal ethnic-independent analytic intelligence of humans from a perspective of comprehension of spatial concepts. Advancements in artificial intelligence led to important breakthroughs, yet excelling at such abstract tasks remains challenging. Current approaches typically resort to non-biologically-plausible architectures with ever-growing models consuming substantially more energy than the brain. Motivated by the brain's efficiency and reasoning capabilities, we present a biologically inspired system that receives inputs from synthetic eye movements - reminiscent of saccades, and processes them with neuronal units incorporating dynamics of neocortical neurons. We introduce a procedurally generated visual oddity dataset to train an architecture extending conventional relational networks and our proposed system. We demonstrate that both approaches are capable of abstract problem-solving at high accuracy, and we uncover that both share the same essential underlying mechanism of reasoning in seemingly unrelated aspects of their architectures. Finally, we show that the biologically inspired network achieves superior accuracy, learns faster and requires fewer parameters than the conventional network.

High-speed multiresolution scanning probe microscopy based on Lissajous scan trajectories.

A novel scan trajectory for high-speed scanning probe microscopy is presented in which the probe follows a two-dimensional Lissajous pattern. The Lissajous pattern is generated by actuating the scanner with two single-tone harmonic waveforms of constant frequency and amplitude. Owing to the extremely narrow frequency spectrum, high imaging speeds can be achieved without exciting the unwanted resonant modes of the scanner and without increasing the sensitivity of the feedback loop to the measurement noise. The trajectory also enables rapid multiresolution imaging, providing a preview of the scanned area in a fraction of the overall scan time. We present a procedure for tuning the spatial and the temporal resolution of Lissajous trajectories and show experimental results obtained on a custom-built atomic force microscope (AFM). Real-time AFM imaging with a frame rate of 1 frame s⁻¹ is demonstrated.

Introducing principles of synaptic integration in the optimization of deep neural networks.

Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength. However, the complex interplay of stimulation-dependent plasticity with local learning signals is disregarded by most of the artificial neural network training algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial and spiking neural networks that incorporates key principles of synaptic plasticity observed in cortical dendrites: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES implements a weight-distribution-dependent modulation of the error signal at each node of the network. We show that this biologically inspired mechanism leads to a substantial improvement of the performance of artificial and spiking networks with feedforward, convolutional, and recurrent architectures, it mitigates catastrophic forgetting, and it is optimally suited for dedicated hardware implementations. Overall, our work indicates that reconciling neurophysiology insights with machine intelligence is key to boosting the performance of neural networks.

A functional genomic approach to actionable gene fusions for precision oncology.

Fusion genes represent a class of attractive therapeutic targets. Thousands of fusion genes have been identified in patients with cancer, but the functional consequences and therapeutic implications of most of these remain largely unknown. Here, we develop a functional genomic approach that consists of efficient fusion reconstruction and sensitive cell viability and drug response assays. Applying this approach, we characterize ~100 fusion genes detected in patient samples of The Cancer Genome Atlas, revealing a notable fraction of low-frequency fusions with activating effects on tumor growth. Focusing on those in the RTK-RAS pathway, we identify a number of activating fusions that can markedly affect sensitivity to relevant drugs. Last, we propose an integrated, level-of-evidence classification system to prioritize gene fusions systematically. Our study reiterates the urgent clinical need to incorporate similar functional genomic approaches to characterize gene fusions, thereby maximizing the utility of gene fusions for precision oncology.

Impulsive control for fast nanopositioning.

In this paper we present a non-linear control scheme for high-speed nanopositioning based on impulsive control. Unlike in the case of a linear feedback controller, the controller states are altered in a discontinuous manner at specific instances in time. Using this technique, it is possible to simultaneously achieve good tracking performance, disturbance rejection and tolerance to measurement noise. Impulsive control is demonstrated experimentally on an atomic force microscope. A significant improvement in tracking performance is demonstrated.

Embryonic stem cell-related miRNAs are involved in differentiation of pluripotent cells originating from the germ line.

Cells originating from the germ cell lineage retain the remarkable property under special culture conditions to give rise to cells with embryonic stem cell (ESC) properties, such as the multipotent adult germline stem cells (maGSCs) derived from adult mouse testis. To get an insight into the mechanisms that control pluripotency and differentiation in these cells, we studied how differences observed during in vitro differentiation between ESCs and maGSCs are associated with differences at the level of microRNAs (miRNAs). In this work, we provide for a first time a connection between germ cell origin of maGSCs and their specific miRNA expression profile. We found that maGSCs express higher levels of germ cell markers characteristic for primordial germ cells (PGCs) and spermatogonia compared with ESCs. Retained expression of miR-290 cluster has been previously reported in maGSCs during differentiation and it was associated with higher Oct-4 levels. Here, we show that this property is also shared by another pluripotent cell line originating from the germ line, the embryonic germ cells. In addition, we provide proof that the specific miRNA expression profile of maGSCs has an impact on their differentiation potential. Low levels of miR-302 in maGSCs during the first 10 days of leukaemia inhibitory factor deprivation are shown to be necessary for the maintenance of high levels of early germ cell markers.

Members of the miR-290 cluster modulate in vitro differentiation of mouse embryonic stem cells.

We report the biological effects of miR-290 cluster via gain-of-function or loss-of-function experiments in mouse embryonic stem cells (ESCs) cultured under differentiation conditions. Under these conditions we found that overexpression of miR-290 cluster in ESCs cannot prevent downregulation of Oct-4, but inhibition results in earlier downregulation of Oct-4 compared with the negative control. In consistence with previous findings that report ectopic expression of Brachyury during gastrulation in Argonaute-2 KO mice due to impaired miRNA function, we show that miR-290 cluster regulates negatively differentiation of ESCs towards mesodermal and germ cell lineage. These results suggest that although incapable to maintain pluripotent state alone, miR-290 cluster inhibits ESC differentiation and it is involved in the pathways controlling mesoderm and primordial germ cell differentiation. Finally, we provide proofs that members of this cluster target Dkk-1 gene, a Wnt pathway inhibitor, and affect this pathway, which can partially explain why miR-290 cluster favours pluripotency against differentiation.

Systematic Assessment of Tumor Purity and Its Clinical Implications.

PURPOSE: The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor. MATERIALS AND METHODS: To benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell-specific mRNA and microRNA profiles. RESULTS: We present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types. CONCLUSION: The choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers.

Moving Genomics to Routine Care: An Initial Pilot in Acute Cardiovascular Disease.

BACKGROUND: Whole-genome sequencing (WGS) costs are falling, yet, outside oncology, this information is seldom used in adult clinics. We piloted a rapid WGS (rWGS) workflow, focusing initially on estimating power for a feasibility study of introducing genome information into acute cardiovascular care. METHODS: A prospective implementation study was conducted to test the feasibility and clinical utility of rWGS in acute cardiovascular care. rWGS was performed on 50 adult patients with acute cardiovascular events and cardiac arrest survivors, testing for primary and secondary disease-causing variants, cardiovascular-related pharmacogenomics, and carrier status for recessive diseases. The impact of returning rWGS results on short-term clinical care of participants was investigated. The utility of polygenic risk scores to stratify coronary artery disease was also assessed. RESULTS: Pathogenic variants, typically secondary findings, were identified in 20% (95% CI, 11.7-34.3). About 60% (95% CI, 46.2-72.4) of participants were carriers for one or more recessive traits, most commonly in HFE and SERPINA1 genes. Although 64% (95% CI, 50.1-75.9) of participants carried at least one pharmacogenetic variant of cardiovascular relevance, these were actionable in only 14% (95% CI, 7-26.2). Coronary artery disease prevalence among participants at the 95th percentile of polygenic risk score was 88.2% (95% CI, 71.8-95.7). CONCLUSIONS: We demonstrated the feasibility of rWGS integration into the inpatient management of adults with acute cardiovascular events. Our pilot identified pathogenic variants in one out of 5 acute vascular patients. Integrating rWGS in clinical care will progressively increase actionability.

Global impact of somatic structural variation on the DNA methylome of human cancers.

BACKGROUND: Genomic rearrangements exert a heavy influence on the molecular landscape of cancer. New analytical approaches integrating somatic structural variants (SSVs) with altered gene features represent a framework by which we can assign global significance to a core set of genes, analogous to established methods that identify genes non-randomly targeted by somatic mutation or copy number alteration. While recent studies have defined broad patterns of association involving gene transcription and nearby SSV breakpoints, global alterations in DNA methylation in the context of SSVs remain largely unexplored. RESULTS: By data integration of whole genome sequencing, RNA sequencing, and DNA methylation arrays from more than 1400 human cancers, we identify hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of a somatic structural variant (SSV) breakpoint is recurrently associated with altered expression or DNA methylation, respectively, independently of copy number alterations. CGIs with SSV-associated increased methylation are predominantly promoter-associated, while CGIs with SSV-associated decreased methylation are enriched for gene body CGIs. Rearrangement of genomic regions normally having higher or lower methylation is often involved in SSV-associated CGI methylation alterations. Across cancers, the overall structural variation burden is associated with a global decrease in methylation, increased expression in methyltransferase genes and DNA damage response genes, and decreased immune cell infiltration. CONCLUSION: Genomic rearrangement appears to have a major role in shaping the cancer DNA methylome, to be considered alongside commonly accepted mechanisms including histone modifications and disruption of DNA methyltransferases.

A Pan-Cancer Compendium of Genes Deregulated by Somatic Genomic Rearrangement across More Than 1,400 Cases.

A systematic cataloging of genes affected by genomic rearrangement, using multiple patient cohorts and cancer types, can provide insight into cancer-relevant alterations outside of exomes. By integrative analysis of whole-genome sequencing (predominantly low pass) and gene expression data from 1,448 cancers involving 18 histopathological types in The Cancer Genome Atlas, we identified hundreds of genes for which the nearby presence (within 100 kb) of a somatic structural variant (SV) breakpoint is associated with altered expression. While genomic rearrangements are associated with widespread copy-number alteration (CNA) patterns, approximately 1,100 genes-including overexpressed cancer driver genes (e.g., TERT, ERBB2, CDK12, CDK4) and underexpressed tumor suppressors (e.g., TP53, RB1, PTEN, STK11)-show SV-associated deregulation independent of CNA. SVs associated with the disruption of topologically associated domains, enhancer hijacking, or fusion transcripts are implicated in gene upregulation. For cancer-relevant pathways, SVs considerably expand our understanding of how genes are affected beyond point mutation or CNA.

An integrated clinical program and crowdsourcing strategy for genomic sequencing and Mendelian disease gene discovery.

Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs.