Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

Surveillance of Non melanoma Skin Cancer Incidence Rates in Kidney Transplant Recipients in Ireland

Background: The incidence of nonmelanomatous skin cancer (NMSC) is substantially higher among renal transplant recipients (RTRs) than in the general population. With a growing RTR population, a robust method for monitoring skin cancer rates in this population is required.
Methods: A modeling approach was used to estimate the trends in NMSC rates that adjusted for changes in the RTR population (sex and age), calendar time, the duration of posttransplant follow-up, and background population NMSC incidence rates. RTR databases in both Northern Ireland (NI) and the Republic of Ireland (ROI) were linked to their respective cancer registries for diagnosis of NMSC, mainly squamous cell carcinoma (SCC) and basal cell carcinoma (BCC).
Results: RTRs in the ROI had three times the incidence (P<0.001) of NMSC compared with NI. There was a decline (P<0.001) in NMSC 10-year cumulative incidence rate in RTRs over the period 1994–2009, which was driven by reductions in both SCC and BCC incidence rates. Nevertheless, there was an increase in the incidence of NMSC with time since transplantation. The observed graft survival was higher in ROI than NI (P<0.05) from 1994–2004. The overall patient survival of RTRs was similar in NI and ROI.
Conclusion: Appropriate modeling of incidence trends in NMSC among RTRs is a valuable surveillance exercise for assessing the impact of change in clinical practices over time on the incidence rates of skin cancer in RTRs. It can form the basis of further research into unexplained regional variations in NMSC incidence.

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.

Enrichment of pathogenic alleles in the brittle cornea gene, ZNF469, in keratoconus

Keratoconus, a common inherited ocular disorder resulting in progressive corneal thinning, is the leading indication for corneal transplantation in the developed world. Genome-wide association studies have identified common SNPs 100 kb upstream of ZNF469 strongly associated with corneal thickness. Homozygous mutations in ZNF469 and PR domain-containing protein 5 (PRDM5) genes result in brittle cornea syndrome (BCS) Types 1 and 2, respectively. BCS is an autosomal recessive generalized connective tissue disorder associated with extreme corneal thinning and a high risk of corneal rupture. Some individuals with heterozygous PRDM5 mutations demonstrate a carrier ocular phenotype, which includes a mildly reduced corneal thickness, keratoconus and blue sclera. We hypothesized that heterozygous variants in PRDM5 and ZNF469 predispose to the development of isolated keratoconus. We found a significant enrichment of potentially pathologic heterozygous alleles in ZNF469 associated with the development of keratoconus (P = 0.00102) resulting in a relative risk of 12.0. This enrichment of rare potentially pathogenic alleles in ZNF469 in 12.5% of keratoconus patients represents a significant mutational load and highlights ZNF469 as the most significant genetic factor responsible for keratoconus identified to date.

Enrichment of low-abundance proteins from bovine and porcine serum samples for proteomic studies

One of the main applications of serum proteomics is the identification of new biomarkers for animal disease or animal production. However, potential obstacles to these studies are the poor performance of affinity serum depletion methods based on human antigens when using animal samples, and loss of minor serum components bound to albumin and other proteins. In the present study, we have analyzed the efficiency and reproducibility of the ProteoMiner® beads with bovine and porcine serum samples, and compared to a traditional immunoaffinity-based albumin and IgG depletion system specific for human samples. The ProteoMiner kit is based on the use of a combinatorial peptide binding library and intends to enrich low-abundance proteins.

On transition rates in surface hopping

Trajectory surface hopping (TSH) is one of the most widely used quantum-classical algorithms for nonadiabatic molecular dynamics. Despite its empirical effectiveness and popularity, a rigorous derivation of TSH as the classical limit of a combined quantum electron-nuclear dynamics is still missing. In this work, we aim to elucidate the theoretical basis for the widely used hopping rules. Naturally, we concentrate thereby on the formal aspects of the TSH. Using a Gaussian wave packet limit, we derive the transition rates governing the hopping process at a simple avoided level crossing. In this derivation, which gives insight into the physics underlying the hopping process, some essential features of the standard TSH algorithm are retrieved, namely (i) non-zero electronic transition rate ("hopping probability") at avoided crossings; (ii) rescaling of the nuclear velocities to conserve total energy; (iii) electronic transition rates linear in the nonadiabatic coupling vectors. The well-known Landau-Zener model is then used for illustration. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4770280]

The effect of helium accretion efficiency on rates of Type Ia supernovae: Double detonations in accreting binaries

he double-detonation explosion scenario of Type Ia supernovae (SNe Ia) has gained increased support from the SN Ia community as a viable progenitor model, making it a promising candidate alongside the well-known single degenerate and double degenerate scenarios. We present delay times of double-detonation SNe, in which a sub-Chandrasekhar mass carbon–oxygen white dwarf (WD) accretes non-dynamically from a helium-rich companion. One of the main uncertainties in quantifying SN rates from double detonations is the (assumed) retention efficiency of He-rich matter. Therefore, we implement a new prescription for the treatment of accretion/accumulation of He-rich matter on WDs. In addition, we test how the results change depending on which criteria are assumed to lead to a detonation in the helium shell. In comparing the results to our standard case (Ruiter et al.), we find that regardless of the adopted He accretion prescription, the SN rates are reduced by only ∼25 per cent if low-mass He shells (≲0.05 M⊙) are sufficient to trigger the detonations. If more massive (0.1 M⊙) shells are needed, the rates decrease by 85 per cent and the delay time distribution is significantly changed in the new accretion model – only SNe with prompt (<500 Myr) delay times are produced. Since theoretical arguments favour low-mass He shells for normal double-detonation SNe, we conclude that the rates from double detonations are likely to be high, and should not critically depend on the adopted prescription for accretion of He.

Seasonal differences in the effects of oscillatory and uni-directional flow on the growth and nitrate-uptake rates of juvenile Laminaria digitata (Phaeophyceae)

The influence of oscillatory versus unidirectional flow on the growth and nitrate-uptake rates of juvenile kelp, Laminaria digitata, was determined seasonally in experimental treatments that simulated as closely as possible natural environmental conditions. In winter, regardless of flow condition (oscillatory and unidirectional) or water velocity, no influence of water motion was observed on the growth rate of L. digitata. In summer, when ambient nitrate concentrations were low, increased water motion enhanced macroalgal growth, which is assumed to be related to an increase in the rate of supply of nutrients to the blade surface. Nitrate-uptake rates were significantly influenced by water motion and season. Lowest nitrate-uptake rates were observed for velocities <5 cm · s−1 and nitrate-uptake rates increased by 20%–50% under oscillatory motion compared to unidirectional flow at the same average speed. These data further suggested that the diffusion boundary layer played a significant role in influencing nitrate-uptake rates. However, while increased nitrate-uptake in oscillatory flow was clear, this was not reflected in growth rates and further work is required to understand the disconnection of nitrate-uptake and growth by L. digitata in oscillatory flow. The data obtained support those from related field-based studies, which suggest that in summer, when insufficient nitrogen is available in the water to saturate metabolic demand, the growth rate of kelps will be influenced by water motion restricting mass transfer of nitrogen.

Stressor intensity determines antagonistic interactions between species invasion and multiple stressor effects on ecosystem functioning

Biological invasions, nutrient enrichment and ocean warming are known to threaten biodiversity and ecosystem functioning. The independent effects of these ecological stressors are well studied, however, we lack understanding of their cumulative effects, which may be additive, antagonistic or synergistic. For example, the impacts of biological invasions are often determined by environmental context, which suggests that the effects of invasive species may vary with other stressors such as pollution or climate change. This study examined the effects of an invasive seaweed (Sargassum muticum) on the structure and functioning of a benthic marine assemblage and tested explicitly whether these effects varied with nutrient enrichment and ocean warming. Overall, the presence of Sargassum muticum increased assemblage productivity rates and warming altered algal assemblage structure, which was characterised by a decrease in kelp and an increase in ephemeral green algae. The effects of Sargassum muticum on total algal biomass accumulation, however, varied with nutrient enrichment and warming producing antagonistic cumulative effects on total algal biomass accumulation. These findings show that the nature of stressor interactions may vary with stressor intensity and among response variables, which leads to less predictable consequences for the structure and functioning of communities.

Reducible Diffusions with Time-Varying Transformations with Application to Short-Term Interest Rates

Reducible diffusions (RDs) are nonlinear transformations of analytically solvable Basic Diffusions (BDs). Hence, by construction RDs are analytically tractable and flexible diffusion processes. Existing literature on RDs has mostly focused on time-homogeneous transformations, which to a significant extent fail to explore the full potential of RDs from both theoretical and practical points of view. In this paper, we propose flexible and economically justifiable time variations to the transformations of RDs. Concentrating on the Constant Elasticity Variance (CEV) RDs, we consider nonlinear dynamics for our time-varying transformations with both deterministic and stochastic designs. Such time variations can greatly enhance the flexibility of RDs while maintaining sufficient tractability of the resulting models. In the meantime, our modeling approach enjoys the benefits of classical inferential techniques such as the Maximum Likelihood (ML). Our application to the UK and the US short-term interest rates suggests that from an empirical point of view time-varying transformations are highly relevant and statistically significant. We expect that the proposed models can describe more truthfully the dynamic time-varying behavior of economic and financial variables and potentially improve out-of-sample forecasts significantly.

Low rates of Pseudomonas aeruginosa misidentification in isolates from cystic fibrosis patients

Pseudomonas aeruginosa is an important cause of pulmonary infection in cystic fibrosis (CF). Its correct identification ensures effective patient management and infection control strategies. However, little is known about how often CF sputum isolates are falsely identified as P. aeruginosa. We used P. aeruginosa-specific duplex real-time PCR assays to determine if 2,267 P. aeruginosa sputum isolates from 561 CF patients were correctly identified by 17 Australian clinical microbiology laboratories. Misidentified isolates underwent further phenotypic tests, amplified rRNA gene restriction analysis, and partial 16S rRNA gene sequence analysis. Participating laboratories were surveyed on how they identified P. aeruginosa from CF sputum. Overall, 2,214 (97.7%) isolates from 531 (94.7%) CF patients were correctly identified as P. aeruginosa. Further testing with the API 20NE kit correctly identified only 34 (59%) of the misidentified isolates. Twelve (40%) patients had previously grown the misidentified species in their sputum. Achromobacter xylosoxidans (n = 21), Stenotrophomonas maltophilia (n = 15), and Inquilinus limosus (n = 4) were the species most commonly misidentified as P. aeruginosa. Overall, there were very low rates of P. aeruginosa misidentification among isolates from a broad cross section of Australian CF patients. Additional improvements are possible by undertaking a culture history review, noting colonial morphology, and performing stringent oxidase, DNase, and colistin susceptibility testing for all presumptive P. aeruginosa isolates. Isolates exhibiting atypical phenotypic features should be evaluated further by additional phenotypic or genotypic identification techniques.

Char oxidation of torrefied biomass at high temperatures and high heating rates

The char oxidation of a torrefied biomass and its parent material was carried out in an isothermal plug flow reactor (IPFR), which is able to rapidly heat the biomass particles to a maximum temperature of 1400 °C at a heating rate of 104 °C/s, similar to the real conditions found in power plant furnaces. During each char oxidation test, the residues of biomass particles were collected and analyzed to determine the weight loss based on the ash tracer method. According to the experimental results, it can be concluded that chars produced from a torrefied biomass are less reactive than the ones produced, under the same conditions, from its raw material. The apparent kinetics of the torrefied biomass and its parent material are determined by minimizing the difference between the modeled and the experimental results. The predicted weight loss during char oxidation, using the determined kinetics, agrees well with experimental results

Sediment accumulation rates in subarctic lakes: insights into age-depth modeling from 22 dated lake records from the Northwest Territories, Canada

Age-depth modeling using Bayesian statistics requires well-informed prior information about the behavior of sediment accumulation. Here we present average sediment accumulation rates (represented as deposition times, DT, in yr/cm) for lakes in an Arctic setting, and we examine the variability across space (intra- and inter-lake) and time (late Holocene). The dataset includes over 100 radiocarbon dates, primarily on bulk sediment, from 22 sediment cores obtained from 18 lakes spanning the boreal to tundra ecotone gradients in subarctic Canada. There are four to twenty-five radiocarbon dates per core, depending on the length and character of the sediment records. Deposition times were calculated at 100-year intervals from age-depth models constructed using the ‘classical’ age-depth modeling software Clam. Lakes in boreal settings have the most rapid accumulation (mean DT 20 ± 10 years), whereas lakes in tundra settings accumulate at moderate (mean DT 70 ± 10 years) to very slow rates, (>100 yr/cm). Many of the age-depth models demonstrate fluctuations in accumulation that coincide with lake evolution and post-glacial climate change. Ten of our sediment cores yielded sediments as old as c. 9,000 cal BP (BP = years before AD 1950). From between c. 9,000 cal BP and c. 6,000 cal BP, sediment accumulation was relatively rapid (DT of 20 to 60 yr/cm). Accumulation slowed between c. 5,500 and c. 4,000 cal BP as vegetation expanded northward in response to warming. A short period of rapid accumulation occurred near 1,200 cal BP at three lakes. Our research will help inform priors in Bayesian age modeling.

Automated tumour annotation and analysis for molecular pathology using TissueMark (R)

Objective: Molecular pathology relies on identifying anomalies using PCR or analysis of DNA/RNA. This is important in solid tumours where molecular stratification of patients define targeted treatment. These molecular biomarkers rely on examination of tumour, annotation for possible macro dissection/tumour cell enrichment and the estimation of % tumour. Manually marking up tumour is error prone. Method: We have developed a method for automated tumour mark-up and % cell calculations using image analysis called TissueMark® based on texture analysis for lung, colorectal and breast (cases=245, 100, 100 respectively). Pathologists marked slides for tumour and reviewed the automated analysis. A subset of slides was manually counted for tumour cells to provide a benchmark for automated image analysis. Results: There was a strong concordance between pathological and automated mark-up (100 % acceptance rate for macro-dissection). We also showed a strong concordance between manually/automatic drawn boundaries (median exclusion/inclusion error of 91.70 %/89 %). EGFR mutation analysis was precisely the same for manual and automated annotation-based macrodissection. The annotation accuracy rates in breast and colorectal cancer were 83 and 80 % respectively. Finally, region-based estimations of tumour percentage using image analysis showed significant correlation with actual cell counts. Conclusion: Image analysis can be used for macro-dissection to (i) annotate tissue for tumour and (ii) estimate the % tumour cells and represents an approach to standardising/improving molecular diagnostics.