Statistical tests for intra-tumour clonal co-occurrence and exclusivity

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.     

Viral Population Estimation Using Pyrosequencing

The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate-based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug-resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an expectation–maximization (EM) algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.     

Modeling Mutual Exclusivity of Cancer Mutations

In large collections of tumor samples, it has been observed that sets of genes that are commonly involved in the same cancer pathways tend not to occur mutated together in the same patient. Such gene sets form mutually exclusive patterns of gene alterations in cancer genomic data. Computational approaches that detect mutually exclusive gene sets, rank and test candidate alteration patterns by rewarding the number of samples the pattern covers and by punishing its impurity, i.e., additional alterations that violate strict mutual exclusivity. However, the extant approaches do not account for possible observation errors. In practice, false negatives and especially false positives can severely bias evaluation and ranking of alteration patterns. To address these limitations, we develop a fully probabilistic, generative model of mutual exclusivity, explicitly taking coverage, impurity, as well as error rates into account, and devise efficient algorithms for parameter estimation and pattern ranking. Based on this model, we derive a statistical test of mutual exclusivity by comparing its likelihood to the null model that assumes independent gene alterations. Using extensive simulations, the new test is shown to be more powerful than a permutation test applied previously. When applied to detect mutual exclusivity patterns in glioblastoma and in pan-cancer data from twelve tumor types, we identify several significant patterns that are biologically relevant, most of which would not be detected by previous approaches. Our statistical modeling framework of mutual exclusivity provides increased flexibility and power to detect cancer pathways from genomic alteration data in the presence of noise. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.     

Signal and sensitivity enhancement through optical interference coating for DNA and protein microarray applications.

Optical inteference (OI) coated slides with unique optical properties were utilized in microarray analyses, demonstrating their enhanced detection sensitivity over traditional microarray substrates. The OI coating is comprised of a proprietary multilayered, dielectric, thin-film interference coating located beneath the functional coating (aminosilane or epoxysilane). It is designed to enhance the fluorescence in the Cy3 and Cy5 channel by increasing the light absorption of the dyes by about 6-fold and by redirecting emitted fluorescence into the detector during scanning, resulting in a theoretical limit of about 12-fold signal amplification. Two-color DNA microarray experiments conducted on the OI slides showed over 8-fold signal amplification, conservation of gene expression ratios, and increased signal-to-noise ratio when compared to control slides, indicating enhanced detection sensitivity. Protein microarray assays also exhibited over 8-fold signal amplification at three different target concentrations, demonstrating the versatility of the OI slides for different microarray applications. Further, the DNA and protein assays performed on the OI slides exhibited excellent detection sensitivity even at the low target amounts essential for diagnostic applications. The OI slides are compatible with commonly used protocols, printers, scanners and other microarray equipment. Therefore, the OI slides offer an attractive alternative to traditional microarray substrates, where enhanced detection sensitivity is desired.     

Building Inventory and Refurbishment Scenario Database Development for Switzerland

Material usage and the related embodied environmental impact have grown in significance in the built environment. Therefore, cities and governments need to develop strategies to reduce both the consumption of resources during usage phase as well as the embodied impact of the current building stock. This article proposes a new component‐based building inventory database as a basis to develop such strategies using building stock modeling. The developed database clusters the building stock according to building typology (single‐family houses, multifamily houses, and office buildings), age, and the main construction systems of the different building components. Based on the component makeup, it lists the necessary material input and waste output for different refurbishment options for each building component. The advantages of the proposed database structure are shown based on two applications for the developed database for Switzerland. The component‐based database allows optimization of refurbishment strategies not only from an energetic perspective, but also with respect to materials, both on the input (sourcing of materials) and the output (waste streams) level. The database structure makes it possible to continuously extend the data set by adding new refurbishment options or add data such as component‐specific lifetimes, costs, or labor intensities of the refurbishment options. In combination with an aligned economic model, this would give an even more holistic view, impact, and feasibility of different refurbishment scenarios both in environmental and economic terms.     

Hotspots of Malaria Transmission in the Peruvian Amazon: Rapid Assessment through a Parasitological and Serological Survey

Background

With low and markedly seasonal malaria transmission, increasingly sensitive tools for better stratifying the risk of infection and targeting control interventions are needed. A cross-sectional survey to characterize the current malaria transmission patterns, identify hotspots, and detect recent changes using parasitological and serological measures was conducted in three sites of the Peruvian Amazon.

Material and Methods

After full census of the study population, 651 participants were interviewed, clinically examined and had a blood sample taken for the detection of malaria parasites (microscopy and PCR) and antibodies against P. vivax (PvMSP1₁₉, PvAMA1) and P. falciparum (PfGLURP, PfAMA1) antigens by ELISA. Risk factors for malaria infection (positive PCR) and malaria exposure (seropositivity) were assessed by multivariate survey logistic regression models. Age-specific seroprevalence was analyzed using a reversible catalytic conversion model based on maximum likelihood for generating seroconversion rates (SCR, λ). SaTScan was used to detect spatial clusters of serology-positive individuals within each site.

Results

The overall parasite prevalence by PCR was low, i.e. 3.9% for P. vivax and 6.7% for P. falciparum, while the seroprevalence was substantially higher, 33.6% for P. vivax and 22.0% for P. falciparum, with major differences between study sites. Age and location (site) were significantly associated with P. vivax exposure; while location, age and outdoor occupation were associated with P. falciparum exposure. P. falciparum seroprevalence curves showed a stable transmission throughout time, while for P. vivax transmission was better described by a model with two SCRs. The spatial analysis identified well-defined clusters of P. falciparum seropositive individuals in two sites, while it detected only a very small cluster of P. vivax exposure.

Conclusion

The use of a single parasitological and serological malaria survey has proven to be an efficient and accurate method to characterize the species specific heterogeneity in malaria transmission at micro-geographical level as well as to identify recent changes in transmission.     

Chemical Composition of the Tuber Essential Oil from Helianthus tuberosus L. (Asteraceae)

Helianthus tuberosus L. (Jerusalem artichoke) is cultivated in Europe and other parts of the world as a food crop and ornamental plant. The volatile oils of the aerial parts of H. tuberosus were investigated more than 30 years ago, but no study could be found to date on the constituents of the tuber essential oil. Herein, the first characterization by GC‐FID, GC/MS, and ¹³C‐NMR analyses of a hydrodistilled essential oil of Jerusalem artichoke tubers was reported. Fresh plant material collected in Serbia (Sample A) and a commercial sample (Sample B) yielded only small amounts of oil (0.0014 and 0.0021% (w/w), resp.). In total, 195 constituents were identified, representing 88.2 and 93.6% of the oil compositions for Samples A and B, respectively. The main constituents identified were β‐bisabolene ( 1 ; 22.9–30.5%), undecanal (0–12.7%), α‐pinene (7.6–0.8%), kauran‐16‐ol ( 2 ; 6.9–9.8%), 2‐pentylfuran (0.0–5.7%), and (E)‐tetradec‐2‐enal (0.0–4.9%). Several rare compounds characteristic for Helianthus ssp. were also detected: helianthol A ( 6 ; 2.1–1.9%), dihydroeuparin ( 10 ; 0.0–2.3%), euparin ( 9 ; 0.0–0.4%), desmethoxyencecalin ( 7 ; traces – 0.2%), desmethylencecalin ( 8 ; 0.0–0.4%), and an isomer of desmethylencecalin (0.0%‐traces). The essential oils isolated from the tuber and the aerial parts share the common major component 1 .     

Spatially Restricted Immune Responses Are Required for Maintaining Root Meristematic Activity upon Detection of Bacteria

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