A systematic review of personalized therapy in patients with advanced cancer

Background: An increased understanding of the pathogenesis of cancer at the molecular level has led to the development of personalized cancer therapy based on the mutation status of the tumor. Tailoring treatments to genetic signatures has improved treatment outcomes in patients with advanced cancer. We conducted a meta-analysis to provide a quantitative summary of the response to treatment on a phase I clinical trial matched to molecular aberration in patients with advanced solid tumors. ^ Methods: Original studies that reported the results of phase I clinical trials in patients with advanced cancer treated with matched anti-cancer therapies between January 2006 and November 2011 were identified through an extensive search of Medline, Embase, Web of Science and Cochrane Library databases. Odds Ratio (OR) with 95% confidence interval (CI) was estimated for each study to assess the strength of an association between objective response rate (ORR) and mutation status. Random effects model was used to estimate the pooled OR and their 95% CI was derived. Funnel plot was used to assess publication bias. ^ Results: Thirteen studies published between January 2006 and November 2011that reported on responses to matched phase I clinical trials in patients with advanced cancer were included in the meta-analysis. Nine studies reported on the responses seen in 538 of the 835 patients with driver mutations responsive to therapy and seven studies on the responses observed in 234 of the 306 patients with mutation predictive for negative response. Random effects model was used to estimate pooled OR, which was 7.767(95% CI = 4.199 − 14.366; p-value=0.000) in patients with activating mutations that were responsive to therapy and 0.287 (95% CI = 0.119 − 0.694; p-value=0.009) in patients with mutation predictive of negative response. ^ Conclusion: It is evident from the meta-analysis that somatic mutations present in tumor tissue of patients are predictive of responses to therapy in patients with advanced cancer in phase I setting. Plethora of research and growing evidence base indicate that selection of patients based on mutation analysis of the tumor and personalizing therapy is a step forward in the war against cancer.^

MBIS: Multivariate Bayesian Image Segmentation Tool

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Diversidad funcional y diversidad filogenética en los bosques secos del sur del Ecuador

El concepto tradicional de reglas de ensamblaje refleja la idea de que las especies no co-ocurren al azar sino que están restringidos en su co-ocurrencia por la competencia interespecífica o por un filtrado ambiental. En está tesis abordé la importancia de los procesos que determinan el ensamble de la comunidad en la estructuración de los Bosques Secos en el Sur del Ecuador. Este estudio se realizó en la región biogeográfica Tumbesina, donde se encuentra la mayor concentración de bosques secos tropicales bien conservados del sur de Ecuador, y que constituyen una de las áreas de endemismo más importantes del mundo. El clima se caracteriza por una estación seca que va desde mayo a diciembre y una estación lluviosa de enero a abril, su temperatura anual varía entre 20°C y 26°C y una precipitación promedio anual entre 300 y 700 mm. Mi primer tema fue orientado a evaluar si la distribución de los rasgos funcionales a nivel comunitario es compatible con la existencia de un filtro ambiental (filtrado del hábitat) o con la existencia de un proceso de limitación de la semejanza funcional impuesta por la competencia inter-específica entre 58 especies de plantas leñosas repartidas en 109 parcelas (10x50m). Para ello, se analizó la distribución de los valores de cinco rasgos funcionales (altura máxima, densidad de la madera, área foliar específica, tamaño de la hoja y de masa de la semilla), resumida mediante varios estadísticos (rango, varianza, kurtosis y la desviación estándar de la distribución de distancias funcionales a la especies más próxima) y se comparó con la distribución esperada bajo un modelo nulo con ausencia de competencia. Los resultados obtenidos apoyan que tanto el filtrado ambiental como la limitación a la semejanza afectan el ensamble de las comunidades vegetales de los bosques secos Tumbesinos. Un segundo tema fue identificar si la diversidad funcional está condicionada por los gradientes ambientales, y en concreto si disminuye en los ambientes más estresantes a causa del filtrado ambiental, y si por el contrario aumenta en los ambientes más benignos donde la competencia se vuelve más importante, teniendo en cuenta las posibles modificaciones a este patrón general a causa de las interacciones de facilitación. Para abordar este estudio analizamos tanto las variaciones en la diversidad funcional (respecto a los de los cinco rasgos funcionales empleados en el primer capítulo de la tesis) como las variaciones de diversidad filogenética a lo largo de un gradiente de estrés climático en los bosques tumbesinos, y se contrastaron frente a las diversidades esperadas bajo un modelo de ensamblaje completamente aleatorio de la comunidad. Los análisis mostraron que tan sólo la diversidad de tamaños foliares siguió el patrón de variación esperado, disminuyendo a medida que aumentó el estrés abiótico mientras que ni el resto de rasgos funcionales ni la diversidad funcional multivariada ni la diversidad filogenética mostraron una variación significativa a lo largo del gradiente ambiental. Un tercer tema fue evaluar si los procesos que organizan la estructura funcional de la comunidad operan a diferentes escalas espaciales. Para ello cartografié todos los árboles y arbustos de más de 5 cm de diámetro en una parcela de 9 Ha de bosque seco y caractericé funcionalmente todas las especies. Dicha parcela fue dividida en subparcelas de diferente tamaño, obteniéndose subparcelas a seis escalas espaciales distintas. Los resultados muestran agregación de estrategias funcionales semejantes a escalas pequeñas, lo que sugiere la existencia bien de filtros ambientales actuando a escala fina o bien de procesos competitivos que igualan la estrategia óptima a dichas escalas. Finalmente con la misma información de la parcela permanente de 9 Ha. Nos propusimos evaluar el efecto y comportamiento de las especies respecto a la organización de la diversidad taxonómica, funcional y filogenética. Para ello utilicé tres funciones sumario espaciales: ISAR- para el nivel taxonómico, IFDAR para el nivel funcional y IPSVAR para el nivel filogenética y las contrastamos frente a modelos nulos que describen la distribución espacial de las especies individuales. Los resultados mostraron que en todas las escalas espaciales consideradas para ISAR, IFDAR y IPSVAR, la mayoría de las especies se comportaron como neutras, es decir, que están rodeados por la riqueza de diversidad semejante a la esperada. Sin embargo, algunas especies aparecieron como acumuladoras de diversidad funcional y filogenética, lo que sugiere su implicación en procesos competitivos de limitación de la semejanza. Una pequeña proporción de las especies apareció como repelente de la diversidad funcional y filogenética, lo que sugiere su implicación en un proceso de filtrado de hábitat. En este estudio pone de relieve cómo el análisis de las dimensiones alternativas de la biodiversidad, como la diversidad funcional y filogenética, puede ayudarnos a entender la co-ocurrencia de especies en diversos ensambles de comunidad. Todos los resultados de este estudio aportan nuevas evidencias de los procesos de ensamblaje de la comunidad de los Bosques Estacionalmente secos y como las variables ambientales y la competencia juegan un papel importante en la estructuración de la comunidad. ABSTRACT The traditional concept of the rules assembly for species communities reflects the idea that species do not co-occur at random but are restricted in their co-occurrence by interspecific competition or an environmental filter. In this thesis, I addressed the importance of the se processes in the assembly of plant communities in the dry forests of southern Ecuador. This study was conducted in the biogeographic region of Tumbesina has the largest concentration of well-conserved tropical dry forests of southern Ecuador, and is recognized as one of the most important areas of endemism in the world. The climate is characterized by a dry season from May to December and a rainy season from January to April. The annual temperature varies between 20 ° C and 26 ° C and an average annual rainfall between 300 and 700 mm. I first assessed whether the distribution of functional traits at the level of the community is compatible with the existence of an environmental filter (imposed by habitat) or the existence of a limitation on functional similarity imposed by interspecific competition. This analysis was conducted for 58 species of woody plants spread over 109 plots of 10 x 50 m. Specifically, I compared the distribution of values of five functional traits (maximum height, wood density, specific leaf area, leaf size and mass of the seed), via selected statistical properties (range, variance, kurtosis and analyzed the standard deviation of the distribution of the closest functional species) distances and compared with a expected distribution under a null model of no competition. The results support that both environmental filtering and a limitation on trait similarity affect the assembly of plant communities in dry forests Tumbesina. My second chapter evaluated whether variation in functional diversity is conditioned by environmental gradients. In particular, I tested whether it decreases in the most stressful environments because of environmental filters, or if, on the contrary, functional diversity is greater in more benign environments where competition becomes more important (notwithstanding possible changes to this general pattern due to facilitation). To address this theme I analyzed changes in both the functional diversity (maximum height, wood density, specific leaf area, leaf size and mass of the seed) and the phylogenetic diversity, along a gradient of climatic stress in Tumbes forests. The observed patterns of variation were contrasted against the diversity expected under a completely random null model of community assembly. Only the diversity of leaf sizes followed the hypothesis decreasing in as trait variation abiotic stress increased, while the other functional traits multivariate functional diversity and phylogenetic diversity no showed significant variation along the environmental gradient. The third theme assess whether the processes that organize the functional structure of the community operate at different spatial scales. To do this I mapped all the trees and shrubs of more than 5 cm in diameter within a plot of 9 hectares of dry forest and functionally classified each species. The plot was divided into subplots of different sizes, obtaining subplots of six different spatial scales. I found aggregation of similar functional strategies at small scales, which may indicate the existence of environmental filters or competitive processes that correspond to the optimal strategy for these fine scales. Finally, with the same information from the permanent plot of 9 ha, I evaluated the effect and behavior of individual species on the organization of the taxonomic, functional and phylogenetic diversity. The analysis comprised three spatial summary functions: ISAR- for taxonomic level analysis, IFDAR for functional level analysis, and IPSVAR for phylogenetic level analysis, in each case the pattern of diversity was contrasted against null models that randomly reallocate describe the spatial distribution of individual species and their traits. For all spatial scales considering ISAR, IFDAR and IPSVAR, most species behaved as neutral, i.e. they are surrounded by the diversity of other traits similar to that expected under a null model. However, some species appeared as accumulator of functional and phylogenetic diversity, suggesting that they may play a role in competitive processes that limiting similarity. A small proportion of the species appeared as repellent of functional and phylogenetic diversity, suggesting their involvement in a process of habitat filtering. These analysis highlights that the analysis of alternative dimensions of biodiversity, such as functional and phylogenetic diversity, can help us understand the co-occurrence of species in the assembly of biotic communities. All results of this study provide further evidence of the processes of assembly of the community of the seasonally dry forests as environmental variables and competition play an important role in structuring the community.

A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.

Guanidine hydrochloride blocks a critical step in the propagation of the prion-like determinant [PSI+] of Saccharomyces cerevisiae

The cytoplasmic heritable determinant [PSI+] of the yeast Saccharomyces cerevisiae reflects the prion-like properties of the chromosome-encoded protein Sup35p. This protein is known to be an essential eukaryote polypeptide release factor, namely eRF3. In a [PSI+] background, the prion conformer of Sup35p forms large oligomers, which results in the intracellular depletion of functional release factor and hence inefficient translation termination. We have investigated the process by which the [PSI+] determinant can be efficiently eliminated from strains, by growth in the presence of the protein denaturant guanidine hydrochloride (GuHCl). Strains are “cured” of [PSI+] by millimolar concentrations of GuHCl, well below that normally required for protein denaturation. Here we provide evidence indicating that the elimination of the [PSI+] determinant is not derived from the direct dissolution of self-replicating [PSI+] seeds by GuHCl. Although GuHCl does elicit a moderate stress response, the elimination of [PSI+] is not enhanced by stress, and furthermore, exhibits an absolute requirement for continued cell division. We propose that GuHCl inhibits a critical event in the propagation of the prion conformer and demonstrate that the kinetics of curing by GuHCl fit a random segregation model whereby the heritable [PSI+] element is diluted from a culture, after the total inhibition of prion replication by GuHCl.

Confirming the Association Between Affective Disturbance and Perceived Cognitive Dysfunction Among Breast Cancer Patients: A Meta-Analysis of Available Literature

Purpose: Breast cancer is the most frequently diagnosed cancer among women worldwide. While undergoing chemotherapy treatment for breast cancer, patients often report experiencing "chemobrain." Previous literature reports correlations between psychological distress and these perceived cognitive problems. The aim of the present study was to examine the strength of the association between affective disturbance and subjective cognitive dysfunction.Methods: This study included a meta-analysis of the literature reporting a correlation between mood and subjective cognitive dysfunction. Eight studies with 1344 breast cancer patients treated with chemotherapy were selected based on stringent study inclusion criteria. Studies reporting a correlation coefficient between mood and subjective cognitive dysfunction were included.Results: In these data, there was no significant correlation between affective disturbance and subjective cognitive dysfunction. A random effects model yielded an overall weighted mean effect size of 0.12.Conclusion: Although this meta-analysis did not confirm the correlation between mood and subjective cognitive dysfunction, there was a clear association between these factors in the original disaggregated analyses, and they are clearly impactful from the time of diagnosis through long-term after care. The clinical implications of the present study and future directions for research are discussed.

Sea surface temperature reconstruction for the Southwest Pacific Ocean

Paleoceanographic archives derived from 17 marine sediment cores reconstruct the response of the Southwest Pacific Ocean to the peak interglacial, Marine Isotope Stage (MIS) 5e (ca. 125?ka). Paleo-Sea Surface Temperature (SST) estimates were obtained from the Random Forest model-an ensemble decision tree tool-applied to core-top planktonic foraminiferal faunas calibrated to modern SSTs. The reconstructed geographic pattern of the SST anomaly (maximum SST between 120 and 132?ka minus mean modern SST) seems to indicate how MIS 5e conditions were generally warmer in the Southwest Pacific, especially in the western Tasman Sea where a strengthened East Australian Current (EAC) likely extended subtropical influence to ca. 45°S off Tasmania. In contrast, the eastern Tasman Sea may have had a modest cooling except around 45°S. The observed pattern resembles that developing under the present warming trend in the region. An increase in wind stress curl over the modern South Pacific is hypothesized to have spun-up the South Pacific Subtropical Gyre, with concurrent increase in subtropical flow in the western boundary currents that include the EAC. However, warmer temperatures along the Subtropical Front and Campbell Plateau to the south suggest that the relative influence of the boundary inflows to eastern New Zealand may have differed in MIS 5e, and these currents may have followed different paths compared to today.

A Bayesian approach to imposing curvature on distance functions

The estimated parameters of output distance functions frequently violate the monotonicity, quasi-convexity and convexity constraints implied by economic theory, leading to estimated elasticities and shadow prices that are incorrectly signed, and ultimately to perverse conclusions concerning the effects of input and output changes on productivity growth and relative efficiency levels. We show how a Bayesian approach can be used to impose these constraints on the parameters of a translog output distance function. Implementing the approach involves the use of a Gibbs sampler with data augmentation. A Metropolis-Hastings algorithm is also used within the Gibbs to simulate observations from truncated pdfs. Our methods are developed for the case where panel data is available and technical inefficiency effects are assumed to be time-invariant. Two models-a fixed effects model and a random effects model-are developed and applied to panel data on 17 European railways. We observe significant changes in estimated elasticities and shadow price ratios when regularity restrictions are imposed. (c) 2004 Elsevier B.V. All rights reserved.

Characteristics and benefits of professional work - Assessment of their importance over a 30-year career

The Professions in Australia Study is the first longitudinal investigation of the professions in Australia; it spans 33 years. Self-administered questionnaires were distributed on at least eight occasions between 1965 and 1998 to cohorts of students and later practitioners from the professions of engineering, law and medicine. The longitudinal design of this study has allowed for an investigation of individual change over time of three archetypal characteristics of the professions, service, knowledge and autonomy and two of the benefits of professional work, financial rewards and prestige. A cumulative logit random effects model was used to statistically assess changes in the ordinal response scores for measuring importance of the characteristics and benefits through stages of the career path. Individuals were also classified by average trends in response scores over time and hence professions are described through their members' tendency to follow a particular path in attitudes either of change or constancy, in relation to the importance of the five elements (characteristics and benefits). Comparisons in trends are also made between the three professions.

Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.

Monetary models of exchange rates and sweep programs

Numerous studies find that monetary models of exchange rates cannot beat a random walk model. Such a finding, however, is not surprising given that such models are built upon money demand functions and traditional money demand functions appear to have broken down in many developed countries. In this article, we investigate whether using a more stable underlying money demand function results in improvements in forecasts of monetary models of exchange rates. More specifically, we use a sweep-adjusted measure of US monetary aggregate M1 which has been shown to have a more stable money demand function than the official M1 measure. The results suggest that the monetary models of exchange rates contain information about future movements of exchange rates, but the success of such models depends on the stability of money demand functions and the specifications of the models.

Sequential, Bayesian geostatistics:a principled method for large data sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of “basis vectors” that best represent the “true” posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.

Does money matter in inflation forecasting

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.

Efficacy of memantine on behavioral and psychological symptoms related to dementia:a systematic meta-analysis

BACKGROUND: The behavioral and psychological symptoms related to dementia (BPSD) are difficult to manage and are associated with adverse patient outcomes. OBJECTIVE: To systematically analyze the data on memantine in the treatment of BPSD. METHODS: We searched MEDLINE, EMBASE, Pharm-line, the Cochrane Centre Collaboration, www.clinicaltrials.gov, www.controlled-trials.com, and PsycINFO (1966-July 2007). We contacted manufacturers and scrutinized the reference sections of articles identified in our search for further references, including conference proceedings. Two researchers (IM and CF) independently reviewed all studies identified by the search strategy. We included 6 randomized, parallel-group, double-blind studies that rated BPSD with the Neuropsychiatric Inventory (NPI) in our meta-analysis. Patients had probable Alzheimer's disease and received treatment with memantine for at least one month. Overall efficacy of memantine on the NPI was established with a t-test for the average difference between means across studies, using a random effects model. RESULTS: Five of the 6 studies identified had NPI outcome data. In these 5 studies, 868 patients were treated with memantine and 882 patients were treated with placebo. Patients on memantine improved by 1.99 on the NPI scale (95% Cl -0.08 to -3.91; p = 0.041) compared with the placebo group. CONCLUSIONS: Initial data appear to indicate that memantine decreases NPI scores and may have a role in managing BPSD. However, there are a number of limitations with the current data; the effect size was relatively small, and whether memantine produces significant clinical benefit is not clear.

A comparison of sales response predictions from demand models applied to store-level versus panel data

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.