Charge structure in volcanic plumes: a comparison of plume properties predicted by an integral plume model to observations of volcanic lightning during the 2010 eruption of Eyjafjallajökull, Iceland

Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0700-7) contains supplementary material, which is available to authorized users

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

A Tale of Two Shares: Why Two-Share Threshold Implementation Seems Worthwhile-and Why it is Not

In this work, we explore the possibilities for practical Threshold Implementation (TI) with only two shares in order for a smaller design that needs less randomness but is still first-order leakage resistant. We present the first two-share Threshold Implementations of two lightweight block ciphers---Simon and Present. The implementation results show that two-share TI gains in compactness while loses in throughput compared with three-share schemes. Moreover, the leakage analyses show that two-share TI retains perfect first-order resistance but is shadowed by a strong second-order leakage, making it less worthwhile

Circuits of cancer drivers revealed by convergent misregulation of transcription factor targets across tumor types

BACKGROUND: Large tumor genome sequencing projects have now uncovered a few hundred genes involved in the onset of tumorigenesis, or drivers, in some two dozen malignancies. One of the main challenges emerging from this catalog of drivers is how to make sense of their heterogeneity in most cancer types. This is key not only to understand how carcinogenesis appears and develops in these malignancies to be able to early diagnose them, but also to open up the possibility to employ therapeutic strategies targeting a driver protein to counteract the alteration of another connected driver. METHODS: Here, I focus on driver transcription factors and their connection to tumorigensis in several tumor types through the alteration of the expression of their targets. First, I explore their involvement in tumorigenesis as mutational drivers in 28 different tumor types. Then, I collect a list of downstream targets of the all driver transcription factors (TFs), and identify which of them exhibit a differential expression upon alterations of driver transcription factors. RESULTS: /nI identify the subset of targets of each TF most likely mediating the tumorigenic effect of their driver alterations in each tumor type, and explore their overlap. Furthermore, I am able to identify other driver genes that cause tumorigenesis through the alteration of very similar sets of targets./nCONCLUSIONS: I thus uncover these circuits of connected drivers which cause tumorigenesis through the perturbation of overlapping cellular pathways in a pan-cancer manner across 15 malignancies. The systematic detection of these circuits may be key to propose novel therapeutic strategies indirectly targeting driver alterations in tumors.AG-P is supported by a Ramon y Cajal scholarship funded by the Spanish Ministry of Econom