Connecting the Edges: A Universal, Mobile-Centric, and Opportunistic Communications Architecture

The Internet has crossed new frontiers with access to it getting faster and cheaper. Considering that the architectural foundations of today's Internet were laid more than three decades ago, the Internet has done remarkably well until today coping with the growing demand. However, the future Internet architecture is expected to support not only the ever growing number of users and devices, but also a diverse set of new applications and services. Departing from the traditional host-centric access paradigm, where access to a desired content is mapped to its location, an information-centric model enables the association of access to a desired content with the content itself, irrespective of the location where it is being held. UMOBILE tailors the information-centric communication model to meet the requirements of opportunistic communications, integrating those connectivity approaches into a single architecture. By pushing services near the edge of the network, such an architecture can pervasively operate in any networking environment and allows for the development of innovative applications, providing access to data independent of the level of end-to-end connectivity availability

A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma.

Genomic lesions are not investigated during routine diagnostic workup for multiple myeloma (MM). Cytogenetic studies are performed to assess prognosis but with limited impact on therapeutic decisions. Recently, several recurrently mutated genes have been described, but their clinical value remains to be defined. Therefore, clinical-grade strategies to investigate the genomic landscape of myeloma samples are needed to integrate new and old prognostic markers. We developed a target-enrichment strategy followed by next-generation sequencing (NGS) to streamline simultaneous analysis of gene mutations, copy number changes and immunoglobulin heavy chain (IGH) translocations in MM in a high-throughput manner, and validated it in a panel of cell lines. We identified 548 likely oncogenic mutations in 182 genes. By integrating published data sets of NGS in MM, we retrieved a list of genes with significant relevance to myeloma and found that the mutational spectrum of primary samples and MM cell lines is partially overlapping. Gains and losses of chromosomes, chromosomal segments and gene loci were identified with accuracy comparable to conventional arrays, allowing identification of lesions with known prognostic significance. Furthermore, we identified IGH translocations with high positive and negative predictive value. Our approach could allow the identification of novel biomarkers with clinical relevance in myeloma

A targeted sequencing approach in multiple myeloma reveals a complex landscape of genomic lesions that has implications for prognosis

Background: Next-generation sequencing (NGS) studies have shown that mul- tiple myeloma is a heterogeneous disease with a complex subclonal architecture and few recurrently mutated genes. The analysis of smaller regions of interest in the genome (\u201ctargeted studies\u201d) allows interrogation of recurrent genomic events with reduces complexity of downstream analysis at a lower price. Aims: Here, we performed the largest targeted study to date in multiple myelo- ma to analyze gene mutations, deletions and amplifications, chromosomal copy number changes and immunoglobulin heavy chain locus (IGH) translo- cations and correlate results with biological and clinical features. Methods: We used Agilent SureSelect cRNA pull down baits to target: 246 genes implicated in myeloma or cancer in general in a mixed gene discovery/confirmation effort; 2538 single nucleotide polymorphisms to detect amplifications and deletions at the single-gene and chromosome level; the IGH locus to detect translocations. We sequenced unmatched DNA from CD138- purified plasma cells from 418 patients with multiple myeloma at diagnosis, with a median follow-up of 5.3 years. We sequenced at an average depth of 337x using Hiseq2000 machines (Illumina Inc.). We applied algorithms developed in- house to call genomic events, filtering out potential artifacts and germline vari- ants. We then ranker each event on its likelihood of being \u201concogenic\u201d based on clustering, recurrence and cross-reference with the COSMIC database. Results: We identified 2270 gene mutations in 412/418 patients, and of those 688 were oncogenic. 342 patients harbored at least one oncogenic mutation. 215/246 genes showed at lease one likely somatic mutation, but only 106 showed at least one oncogenic mutation. 63% of oncogenic mutations were accounted for by the top 9 driver genes previously identified (KRAS, NRAS, TP53, FAM46C, BRAF, DIS3, TRAF3, SP140, IRF4), implying our gene discov- ery effort did not identify novel mutated genes. We included deletion of tumor suppressors, amplification of oncogenes, chromosomal copy number changes and IGH translocations for a total of 76 variables, so that 413/418 patients showed at least one informative driver genomic event, (median 4/patient). We investigated pairwise associations between events and found significant corre- lations, such as TP53 mutations and del(17p), CYLD mutations and del(16), FAM46C mutations and del(1p), SF3B1 mutations and t(11;14). Hotspots muta- tions of IRF4 lysine p.123 showed an inverse correlation with a hyperdiploid karyotype and del(16) as opposed to other missense mutations scattered along the gene, which has pathogenic implications. Survival was negatively affected by the cumulative burden of lesions in an almost linear fashion, with median survival of 10.97 and 4.07 years in patients with =7 lesions respectively, and this was independent of the nature of the genomic events. Given the het- erogeneity and complex interplay of the variables we fitted a cox-proportional hazard model to predict survival. We found that mutations in TP53, amplifications of MYC, deletions of CYLD, amp(1q), del12p13.31 and del17p13 where the only significant events, all promoting shorter survival. In particular, TP53 muta- tions and deletions, often co-occurring, had an additive effect so that carriers of both showed a dismal survival of 17 months (Figure 1).Summary/Conclusions: Due to the complex genomic landscape in MM, a discovery effort still requires large studies to derive significant associations. We conclude that a targeted sequencing approach may provide prognostic models and give insights into myeloma biology

Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups

In multiple myeloma, next-generation sequencing (NGS) has expanded our knowledge of genomic lesions, and highlighted a dynamic and heterogeneous composition of the tumor. Here we used NGS to characterize the genomic landscape of 418 multiple myeloma cases at diagnosis and correlate this with prognosis and classification. Translocations and copy number abnormalities (CNAs) had a preponderant contribution over gene mutations in defining the genotype and prognosis of each case. Known and novel independent prognostic markers were identified in our cohort of proteasome inhibitor and immunomodulatory drug-treated patients with long follow-up, including events with context-specific prognostic value, such as deletions of the PRDM1 gene. Taking advantage of the comprehensive genomic annotation of each case, we used innovative statistical approaches to identify potential novel myeloma subgroups. We observed clusters of patients stratified based on the overall number of mutations and number/type of CNAs, with distinct effects on survival, suggesting that extended genotype of multiple myeloma at diagnosis may lead to improved disease classification and prognostication