ProbCD: enrichment analysis accounting for categorization uncertainty

As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R package to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for
the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/. We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation

Investigating the validity of the DN4 in a consecutive population of patients with chronic pain

Neuropathic pain is clinically described as pain caused by a lesion or disease of the somatosensory nervous system. The aim of this study was to assess the validity of the Dutch version of the DN4, in a cross-sectional multicentre design, as a screening tool for detecting a neuropathic pain component in a large consecutive, not pre-stratified on basis of the target outcome, population of patients with chronic pain. Patients’ pain was classified by two independent (pain-)physicians as the gold standard. The analysis was initially performed on the outcomes of those patients (n = 228 out of 291) in whom both physicians agreed in their pain classification. Compared to the gold standard the DN4 had a sensitivity of 75% and specificity of 76%. The DN4-symptoms (seven interview items) solely resulted in a sensitivity of 70% and a specificity of 67%. For the DN4-signs (three examination items) it was respectively 75% and 75%. In conclusion, because it seems that the DN4 helps to identify a neuropathic pain component in a consecutive population of patients with chronic pain in a moderate way, a comprehensive (physical-) examination by the physician is still obligate

Gene set analysis exploiting the topology of a pathway

BACKGROUND: Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it. RESULTS: We propose a novel gene set analysis specifically designed for gene sets defined by pathways. Such analysis, based on graphical models, explicitly incorporates the dependence structure among genes highlighted by the topology of pathways. The analysis is designed to be used for overall surveillance of changes in a pathway in different experimental conditions. In fact, under different circumstances, not only the expression of the genes in a pathway, but also the strength of their relations may change. The methods resulting from the proposal allow both to test for variations in the strength of the links, and to properly account for heteroschedasticity in the usual tests for differential expression. CONCLUSIONS: The use of graphical models allows a deeper look at the components of the pathway that can be tested separately and compared marginally. In this way it is possible to test single components of the pathway and highlight only those involved in its deregulation

Testing the additional predictive value of high-dimensional molecular data

While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature. We suggest an intuitive permutation-based testing procedure for assessing the additional predictive value of high-dimensional molecular data. Our method combines two well-known statistical tools: logistic regression and boosting regression. We give clear advice for the choice of the only method parameter (the number of boosting iterations). In simulations, our novel approach is found to have very good power in different settings, e.g. few strong predictors or many weak predictors. For illustrative purpose, it is applied to two publicly available cancer data sets. Our simple and computationally efficient approach can be used to globally assess the additional predictive power of a large number of candidate predictors given that a few clinical covariates or a known prognostic index are already available

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2

Genetic Variation in the TP53 Pathway and Bladder Cancer Risk. A Comprehensive Analysis

Introduction: Germline variants in TP63 have been consistently associated with several tumors, including bladder cancer, indicating the importance of TP53 pathway in cancer genetic susceptibility. However, variants in other related genes, including TP53 rs1042522 (Arg72Pro), still present controversial results. We carried out an in depth assessment of associations between common germline variants in the TP53 pathway and bladder cancer risk. Material and Methods: We investigated 184 tagSNPs from 18 genes in 1,058 cases and 1,138 controls from the Spanish Bladder Cancer/EPICURO Study. Cases were newly-diagnosed bladder cancer patients during 1998–2001. Hospital controls were age-gender, and area matched to cases. SNPs were genotyped in blood DNA using Illumina Golden Gate and TaqMan assays. Cases were subphenotyped according to stage/grade and tumor p53 expression. We applied classical tests to assess individual SNP associations and the Least Absolute Shrinkage and Selection Operator (LASSO)-penalized logistic regression analysis to assess multiple SNPs simultaneously. Results: Based on classical analyses, SNPs in BAK1 (1), IGF1R (5), P53AIP1 (1), PMAIP1 (2), SERINPB5 (3), TP63 (3), and TP73 (1) showed significant associations at p-value#0.05. However, no evidence of association, either with overall risk or with specific disease subtypes, was observed after correction for multiple testing (p-value$0.8). LASSO selected the SNP rs6567355 in SERPINB5 with 83% of reproducibility. This SNP provided an OR = 1.21, 95%CI 1.05–1.38, p-value = 0.006, and a corrected p-value = 0.5 when controlling for over-estimation. Discussion: We found no strong evidence that common variants in the TP53 pathway are associated with bladder cancer susceptibility. Our study suggests that it is unlikely that TP53 Arg72Pro is implicated in the UCB in white Europeans. SERPINB5 and TP63 variation deserve further exploration in extended studies.This work was supported by the Fondo de Investigacion Sanitaria, Spain (grant numbers 00/0745, PI051436, PI061614, G03/174); Red Tematica de Investigacion Cooperativa en Cancer (grant number RD06/0020-RTICC), Spain; Marato TV3 (grant number 050830); European Commission (grant numbers EU-FP7-HEALTH-F2-2008-201663-UROMOL; US National Institutes of Health (grant number USA-NIH-RO1-CA089715); and the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute at the National Institutes of Health, USA; Consolider ONCOBIO (Ministerio de Economia y Competitividad, Madrid, Spain). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Organising care, practice and participative research : Papers from the cognitive decline partnership centre

Non peer reviewe

PSMA PET/CT for treatment response evaluation at predefined time points is superior to PSA response for predicting survival in metastatic castration-resistant prostate cancer patients

Background: In metastatic castration-resistant prostate cancer (mCRPC), using serum prostate-specific antigen (PSA) levels to evaluate treatment response is not always accurate. This study aimed to assess the efficacy of PSMA PET/CT at specific time points for evaluating treatment response and predicting survival in mCRPC patients, compared to PSA. Methods: Sixty mCRPC patients underwent [18F]PSMA-1007 PET/CT at baseline and for treatment response evaluation of either androgen receptor-targeted agents (after 3 months) or chemotherapy (after completion), and were retrospectively analysed. Visual assessment categorised overall response and response of the worst responding lesion as partial response, stable disease, or progressive disease, using the EAU/EANM criteria. Additionally, percentage changes in SUVmax, total tumour volume and total lesion uptake (tumour volume * SUVmean) were calculated. PSA response was defined according to the PCWG3 criteria. Cox regression analysis identified predictors of overall survival. Results: PSMA PET/CT and PSA response were discordant in 47 % of patients, and PSMA PET/CT response was worse in 89 % of these cases. Overall response on PSMA PET/CT independently predicted overall survival (progression versus non-progression: HR = 4.05, p &lt; 0.001), outperforming PSA response (progression versus non-progression: HR = 2.53, p = 0.010) and other PSMA PET/CT parameters. Among patients with a PSA decline of &gt; 50 %, 31 % showed progressive disease on PSMA PET/CT, correlating with higher mortality risk (progression versus non-progression: HR = 4.38, p = 0.008). No flare in PSMA uptake was observed in this cohort. Conclusions: PSMA PET/CT for assessing treatment response at predefined time points was superior to PSA-based response for predicting overall survival in mCRPC patients treated with androgen receptor-targeted agents and chemotherapy. PSMA PET/CT showed the ability to detect disease progression earlier than PSA levels, which can affect treatment decisions and has the potential to improve patient outcomes. We recommend further research to validate these findings in larger patient cohorts, to extend the number of treatments, and to evaluate cost-effectiveness and impact on patient outcomes.</p

Outcome-related metabolomic patterns from 1H/31P NMR after mild hypothermia treatments of oxygen–glucose deprivation in a neonatal brain slice model of asphyxia

Human clinical trials using 72 hours of mild hypothermia (32°C–34°C) after neonatal asphyxia have found substantially improved neurologic outcomes. As temperature changes differently modulate numerous metabolite fluxes and concentrations, we hypothesized that 1H/31P nuclear magnetic resonance (NMR) spectroscopy of intracellular metabolites can distinguish different insults, treatments, and recovery stages. Three groups of superfused neonatal rat brain slices underwent 45 minutes oxygen–glucose deprivation (OGD) and then were: treated for 3 hours with mild hypothermia (32°C) that began with OGD, or similarly treated with hypothermia after a 15-minute delay, or not treated (normothermic control group, 37°C). Hypothermia was followed by 3 hours of normothermic recovery. Slices collected at different predetermined times were processed, respectively, for 14.1 Tesla NMR analysis, enzyme-linked immunosorbent assay (ELISA) cell-death quantification, and superoxide production. Forty-nine NMR-observable metabolites underwent a multivariate analysis. Separated clustering in scores plots was found for treatment and outcome groups. Final ATP (adenosine triphosphate) levels, severely decreased at normothermia, were restored equally by immediate and delayed hypothermia. Cell death was decreased by immediate hypothermia, but was equally substantially greater with normothermia and delayed hypothermia. Potentially important biomarkers in the 1H spectra included PCr-1H (phosphocreatine in the 1H spectrum), ATP-1H (adenosine triphosphate in the 1H spectrum), and ADP-1H (adenosine diphosphate in the 1H spectrum). The findings suggest a potential role for metabolomic monitoring during therapeutic hypothermia

Literature-aided interpretation of gene expression data with the weighted global test

Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.UB – Publicatie