The complete costs of genome sequencing: a microcosting study in cancer and rare diseases from a single center in the United Kingdom

Abstract: Purpose: The translation of genome sequencing into routine health care has been slow, partly because of concerns about affordability. The aspirational cost of sequencing a genome is $1000, but there is little evidence to support this estimate. We estimate the cost of using genome sequencing in routine clinical care in patients with cancer or rare diseases. Methods: We performed a microcosting study of Illumina-based genome sequencing in a UK National Health Service laboratory processing 399 samples/year. Cost data were collected for all steps in the sequencing pathway, including bioinformatics analysis and reporting of results. Sensitivity analysis identified key cost drivers. Results: Genome sequencing costs £6841 per cancer case (comprising matched tumor and germline samples) and £7050 per rare disease case (three samples). The consumables used during sequencing are the most expensive component of testing (68–72$1000/genome in a single laboratory. This aspirational sequencing cost will likely only be achieved if consumable costs are considerably reduced and sequencing is performed at scale

Clinically actionable mutation profiles in patients with cancer identified by whole-genome sequencing

Next-generation sequencing (NGS) efforts have established catalogs of mutations relevant to cancer development. However, the clinical utility of this information remains largely unexplored. Here, we present the results of the first eight patients recruited into a clinical whole-genome sequencing (WGS) program in the United Kingdom. We performed PCR-free WGS of fresh frozen tumors and germline DNA at 75× and 30×, respectively, using the HiSeq2500 HTv4. Subtracted tumor VCFs and paired germlines were subjected to comprehensive analysis of coding and noncoding regions, integration of germline with somatically acquired variants, and global mutation signatures and pathway analyses. Results were classified into tiers and presented to a multidisciplinary tumor board. WGS results helped to clarify an uncertain histopathological diagnosis in one case, led to informed or supported prognosis in two cases, leading to de-escalation of therapy in one, and indicated potential treatments in all eight. Overall 26 different tier 1 potentially clinically actionable findings were identified using WGS compared with six SNVs/indels using routine targeted NGS. These initial results demonstrate the potential of WGS to inform future diagnosis, prognosis, and treatment choice in cancer and justify the systematic evaluation of the clinical utility of WGS in larger cohorts of patients with cancer

Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom's 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia

A bio-inspired approach for streaming applications in wireless sensor networks based on the Lotka-Volterra competition model

In the new era of Ambient Intelligence, wireless sensor networks (WSNs) are seen to bridge the gap between physical world and the Internet, making a large amount of information accessible anywhere, anytime. Over the last few years, WSNs are being developed towards a large number of multimedia streaming applications, e.g. video surveillance, traffic control systems, health monitoring, and industrial process control. WSNs consist of small sensor devices (nodes) that are capable of working unattended, without centralized control, under dynamically changing conditions. However, these devices face important limitations in terms of energy, memory and computational power. The uncontrolled use of limited resources in conjunction with the unpredictable nature of WSNs in terms of traffic load injection, wireless link capacity fluctuations and topology modifications may lead to congestion. Congestion can cause increased packet loss and delay. This paper proposes a bio-inspired congestion control approach for WSNs streaming applications that necessitate controlled performance with graceful degradation. In the proposed approach, congestion in WSNs is prevented (or at least minimized) by regulating the rate of each traffic flow based on the Lotka-Volterra competition model. Performance evaluations reveal that the proposed approach achieves adaptability to changing traffic loads, scalability and fairness among flows, while providing graceful performance degradation as the offered load increases. © 2010 Elsevier B.V. All rights reserved. 33 17 2039 2047 "p"Cited By :2

Congestion control in autonomous decentralized networks based on the Lotka-Volterra competition model,”

Abstract. Next generation communication networks are moving towards autonomous infrastructures that are capable of working unattended under dynamically changing conditions. The new network architecture involves interactions among unsophisticated entities which may be characterized by constrained resources. From this mass of interactions collective unpredictable behavior emerges in terms of traffic load variations and link capacity fluctuations, leading to congestion. Biological processes found in nature exhibit desirable properties e.g. selfadaptability and robustness, thus providing a desirable basis for such computing environments. This study focuses on streaming applications in sensor networks and on how congestion can be prevented by regulating the rate of each traffic flow based on the Lotka-Volterra population model. Our strategy involves minimal exchange of information and computation burden and is simple to implement at the individual node. Performance evaluations reveal that our approach achieves adaptability to changing traffic loads, scalability and fairness among flows, while providing graceful performance degradation as the offered load increases

ADIVIS: A NOVEL ADAPTIVE ALGORITHM FOR VIDEO STREAMING OVER THE INTERNET

The transfer of information over the wireless networks is emerging as a promising business model. In addition, new applications require the transfer of video content in real time. However, the unpredictable nature of wireless and mobile networks in terms of bandwidth, end-to-end delay and packet loss has tremendous impact on the transmission of video streams. In this paper, we adopt Network Adaptation Techniques applied together with Content Adaptation Techniques to achieve graceful performance degradation when network load increases and network conditions deteriorate. We present a new feedback mechanism that provides video adaptation to network parameters, working together with a fuzzy-based decision algorithm. Our preliminary performance evaluations indicate that our algorithm can finely adapt the video stream bit rate to the available bandwidth while providing fairness as well as high and stable objective quality of service. I