Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma

Synthetic lethal interactions in cancer hold the potential for successful combined therapies, which would avoid the difficulties of single molecule-targeted treatment. Identification of interactions that are specific for human tumors is an open problem in cancer research. This work aims at deciphering synthetic sick or lethal interactions directly from somatic alteration, expression and survival data of cancer patients. To this end, we look for pairs of genes and their alterations or expression levels that are "avoided" by tumors and "beneficial" for patients. Thus, candidates for synthetic sickness or lethality (SSL) interaction are identified as such gene pairs whose combination of states is under-represented in the data. Our main methodological contribution is a quantitative score that allows ranking of the candidate SSL interactions according to evidence found in patient survival. Applying this analysis to glioblastoma data, we collect 1,956 synthetic sick or lethal partners for 85 abundantly altered genes, most of which show extensive copy number variation across the patient cohort. We rediscover and interpret known interaction between TP53 and PLK1, as well as provide insight into the mechanism behind EGFR interacting with AKT2, but not AKT1 nor AKT3. Cox model analysis determines 274 of identified interactions as having significant impact on overall survival in glioblastoma, which is more informative than a standard survival predictor based on patient's age

Modeling signal transduction pathways and their transcriptional response

This thesis is concerned with revealing regulation of gene expression. The basic motivation behind our work is that gene regulation can be better resolved when analyzed in a cellular context of the upstream signaling pathway and known regulatory targets. Our source of data are perturbation experiments, which are performed on pathway components and induce changes in gene expression. In such a way, they connect the signaling pathway to its downstream target genes. This chapter starts with an introduction to the cellular con- text considered in the thesis (section 1.1) and the principles of perturbation experiments (section 1.2). We end with a concise summary of three approaches that comprise this thesis. The approaches tackle various problems in the process of revealing context-speci c regulatory networks (section 1.3). We deal with di erential expression analysis of the per- turbation data, enhanced with known transcription factor targets serving as examples of di erential genes (chapter 2), pathway model-based planning of informative perturbation experiments (chapter 3), and nally, with deregulation analysis, i.e., comparing changes in gene regulation between two di erent cell populations (chapter 4)

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

J Comput Biol

Gene expression measurements allow determining sets of up- or down-regulated, or unchanged genes in a particular experimental condition. Additional biological knowledge can suggest examples of genes from one of these sets. For instance, known target genes of a transcriptional activator are expected, but are not certain to go down after this activator is knocked out. Available differential expression analysis tools do not take such imprecise examples into account. Here we put forward a novel partially supervised mixture modeling methodology for differential expression analysis. Our approach, guided by imprecise examples, clusters expression data into differentially expressed and unchanged genes. The partially supervised methodology is implemented by two methods: a newly introduced belief-based mixture modeling, and soft-label mixture modeling, a method proved efficient in other applications. We investigate on synthetic data the input example settings favorable for each method. In our tests, both belief-based and soft-label methods prove their advantage over semi-supervised mixture modeling in correcting for erroneous examples. We also compare them to alternative differential expression analysis approaches, showing that incorporation of knowledge yields better performance. We present a broad range of knowledge sources and data to which our partially supervised methodology can be applied. First, we determine targets of Ste12 based on yeast knockout data, guided by a Ste12 DNA-binding experiment. Second, we distinguish miR-1 from miR-124 targets in human by clustering expression data under transfection experiments of both microRNAs, using their computationally predicted targets as examples. Finally, we utilize literature knowledge to improve clustering of time-course expression profiles

Pion Content of the Nucleon as seen in the NA51 Drell-Yan experiment

In a recent CERN Drell-Yan experiment the NA51 group found a strong asymmetry of $\bar u$ and $\bar d$ densities in the proton at $x\simeq0.18$. We interpret this result as a decisive confirmation of the pion-induced sea in the nucleon.Comment: 10 pages + 3 figures, Preprint KFA-IKP(TH)-1994-14 .tex file. After \enddocument a uu-encodeded Postscript file comprising the figures is appende

Slow proton production in semi-inclusive deep inelastic scattering and the pion cloud in the nucleon

Abstract: The semi-inclusive cross section for producing slow protons in charged current deep inelastic (anti-) neutrino scattering on protons and neutrons is calculated as a function of the Bjorken x and the proton momentum. The standard hadronization models based upon the colour neutralization mechanism appear to underestimate the rate of slow proton production on hydrogen. The presence of the virtual mesons (pions) in the nucleon leads to an additional mechanism for proton production, referred to as spectator process. It is found that at low proton momenta both mechanisms compete, whereas the spectator mechanism dominates at very small momenta, while the color neutralization mechanism dominates at momenta larger than 1-2 , GeV/c. The results of the calculations are compared with the CERN bubble chamber (BEBC) data. The spectator model predicts a sharp increase of the semi-inclusive cross section at small x due to the sea quarks in virtual mesons

Inclusive production of J/ψJ/\psi meson in proton-proton collisions at BNL RHIC

Inclusive cross sections for $J/\psi$ production in proton-proton collisions were calculated in the $k_t$-factorization approach for the RHIC energy. Several mechanisms were considered, including direct color-singlet mechanism, radiative decays of $\chi_c$ mesons, decays of $\psi'$, open-charm associated production of $J/\psi$ as well as weak decays of B mesons. Different unintegrated gluon distributions from the literature were used. We find that radiative $\chi_c$ decays and direct color-singlet contributions constitute the dominant mechanism of $J/\psi$ production. These process cannot be consistently treated within collinear-factorization approach. The results are compared with recent RHIC data. The new precise data at small transverse momenta impose stringent constraints on UGDFs. Some UGDFs are inconsistent with the new data. The Kwieci\'nski UGDFs give the best description of the data. In order to verify the mechanism suggested here we propose $J/\psi$ -- jet correlation measurement and an independent measurement of $\chi_c$ meson production in $\pi^+ \pi^-$ and/or $K^+ K^-$ decay channels. Finally, we address the issue of \J spin alignment.Comment: 26 pages, 20 figures, the text was slightly modified, the title was modified, more discussion was added, one figure was removed, one was adde