PURPOSE Current prognostic factors are poor at identifying patients at risk of disease recurrence after surgery for stage II colon cancer. Here we describe a DNA microarray-based prognostic assay using clinically relevant formalin-fixed paraffin-embedded (FFPE) samples. PATIENTS AND METHODS A gene signature was developed from a balanced set of 73 patients with recurrent disease (high risk) and 142 patients with no recurrence (low risk) within 5 years of surgery.ResultsThe 634-probe set signature identified high-risk patients with a hazard ratio (HR) of 2.62 (P < .001) during cross validation of the training set. In an independent validation set of 144 samples, the signature identified high-risk patients with an HR of 2.53 (P < .001) for recurrence and an HR of 2.21 (P = .0084) for cancer-related death. Additionally, the signature was shown to perform independently from known prognostic factors (P < .001). CONCLUSION This gene signature represents a novel prognostic biomarker for patients with stage II colon cancer that can be applied to FFPE tumor samples.

In: ISMB/ECCB. Vienna (Austria); 2011

Biomarker discovery involves identifying variables (e.g. genes) that are related to an endpoint of interest, for instance patient risk stratification or drug response. When used in classification setting, the biomarker discovery process is commonly aimed towards identifying a signature consisting of a panel of variables that together allow prediction of clinical outcome.

When signatures are developed according to the best practices there are often several models being evaluated giving similar classification performance. Having only information related to classification performance available for each model makes the model selection step difficult and in part arbitrary, as one model can rarely be said to be significantly better than another.

In this research we have explored the use of additional model properties to make more informed and relevant decisions in selecting the top ranking models by building additional metrics into the model generation step, such as biological relevance, clinical utility and analytical precision. The methodology is evaluated using gene expression data sets from the MAQC-II project. We have generated predictive signatures within cross-validation using several classifiers and feature selection methods and ranked them given their classification performance across signature lengths. The signatures were simultaneously analysed by functional enrichment, independence to known clinical covariates, permutation tests and analytical precision, also within cross validation. Our proposed extended biomarker analysis gives considerably more insight into model properties that might otherwise be left unaccounted for and is crucial in selecting a signature that also generalises well and passes external validation.

Live Web Conference

One of the main challenges of biomarker discovery is the unbiased evaluation and rational selection of most promising biomarkers suitable for independent validation. The absence of a robust evaluation procedure can result in selecting too many biomarkers (thus creating multiplicity problems) with biased and often too optimistic estimates of performance and with limited clinical utility.

Almac’s Approach

Almac has developed an integrated approach to biomarker model development, evaluation and selection and applied it to real life projects. This approach is implemented in classification workflow software. This allows for parallel development of multiple biomarkers models and simultaneous generation of the data used in the model evaluation procedure we are going to describe in this webinar. An important feature of this procedure is the use of both statistical and functional characteristics of the models evaluated.

While traditional approaches to biomarker model generation commonly focus on purely statistical properties, in this webinar we demonstrate how functional analysis can be used as both an essential component of biomarker discovery as well as integral part of the biomarker selection process.

The root apical meristem is responsible for the growth of the plant root system. Because of the importance of root architecture in the performance of crop plants, we established a proteome reference map of the soybean root apex and compared this with the proteome of the differentiated root zone. Root apex samples contained the apical 1 mm of the root, comprising the root apical meristem, quiescent centre and root cap. We identified 342 protein spots from 550 excised proteins (∼62%) of root apex samples by MALDI-TOF MS/MS analysis. All of those proteins were also present in the differentiated root, but differed in abundance. Functional classification showed that the most numerous protein categories represented in the root were those of stress response, glycolysis, redox homeostasis and protein processing. Using DIGE, we identified 73 differentially accumulated proteins between root apex and differentiated root. Proteins over-represented in the root apex belonged primarily to the pathways for protein synthesis and processing, cell redox homeostasis and flavonoid biosynthesis. Proteins under-represented in the root apex were those of glycolysis, tricarboxylic acid metabolism and stress response. Our results highlight the importance of stress and defense response, redox control and flavonoid metabolism in the root apex.

In: Quantitative Biology and Bioinformatics in Modern Medicine. Dublin (Ireland); 2011

Large-scale gene expression profiling provides a powerful resource to identify cancer subtypes relevant for prognosis and/or therapy responses. To investigate the underlying molecular classes of tumors, a computational workflow has been developed for the automated integration of clinical information with functional genomics data.
Based on semi-supervised methods, this approach includes three main modules: i) the hierarchical clustering of gene expression data with bootstrap sampling, determination of optimum number of clusters and visualization of the results, ii) the statistical analysis of the associations between the observed subtypes and clinical parameters and iii) a functional enrichment analysis to determine the biological relevance of these groups. It has been validated by analyzing prostate cancer gene expression data in association with clinical parameters. It has thereby been possible to automatically rediscover previously described tumor subtypes. In addition, it provides evidence of potential novel subtype.
This integrated workflow, implemented in Perl and R/Bioconductor statistical packages, can be systematically applied to a wide range of datasets. It hence provides a useful resource for the automated analysis of cancer subtypes based on gene expression profiles.

Legume pods serve important functions during seed development and are themselves sources of food and feed. Compared to seeds, the metabolism and development of pods are not well defined. The present characterization of pods from the model legume Lotus japonicus, together with the detailed analyses of the pods and seeds proteomes in five developmental stages, paves the way for comparative pathway analysis and provides new metabolic information. Proteins were analyzed by two-dimensional gel electrophoresis and tandem-mass spectrometry. This analysis lead to the identification of 604 pod proteins and 965 seed proteins, including 263 proteins that distinguished the pod. The complete data-set is publicly available at: http://www.cbs.dtu.dk/cgi-bin/lotus/db.cgi, where spots in a reference map are linked to experimental data, such as matched peptides, quantification values, and gene accessions. Identified pod proteins represented enzymes from 85 different metabolic pathways, including storage globulins and a late embryogenesis abundant protein. In contrast to seed maturation, pod maturation was associated with decreasing total protein content, especially proteins involved in biosynthesis and photosynthesis. Proteins detected only in pods included three in the urea cycle and four participating in nitrogen and amino group metabolism, highlighting the importance of nitrogen metabolism during pod development. Five previously unidentified seed proteins in the glutamate metabolism pathway were identified.

The dengue 4 (DEN-4) epidemic that affected French Polynesia in 2009 gave us an opportunity to assess the performance of three commercial kits for this serotype (Biorad Platelia™, Dengue NS1 Ag ELISA, Panbio® Dengue Early ELISA – second generation and Standard Diagnostics Dengue NS1 Ag ELISA), using RT-PCR as our reference technique.

Method – The article relies on data from the dengue surveillance system, provided by laboratories, the sentinel network, reported cases at the emergency department of the French Polynesia hospital, and reported cases of patients hospitalised for dengue fever. Control strategies are presented in a second part.
Results – A dengue type 1 outbreak started in August 2006 and ended in November 2007. The outbreak was moderate in intensity and without any specific severity compared to 2001.
Conclusion – The dengue surveillance system needs to be strengthened, in particular the sentinel network requiring the participation of more physicians. Municipalities will have to get involved in long term communitybased actions such as the elimination of larval breeding sites in order to permanently maintain the lowest mosquito density levels as possible.

Phosphorus (P) deficiency is widespread in regions where the common bean (Phaseolus vulgaris L.), the most important legume for human consumption, is produced and is perhaps the factor that most limits nitrogen (N) fixation. Global gene expression and metabolome approaches were used to investigate the responses of nodules from common bean plants inoculated with Rhizobium tropici CIAT899 grown under P-deficient and P-sufficient conditions. P-deficient inoculated plants showed drastic reduction in nodulation and nitrogenase activity as determined by acetylene reduction assay. Nodule transcript profiling was performed through hybridization of nylon filter arrays spotted with cDNAs, ca. 4,000 unigene set, from nodule and P-deficient root library. A total of 459 genes, representing different biological processes according to updated annotation using the UniProt Knowledgebase database, showed significant differential expression in response to P: 59% of these were induced in P-deficient nodules. The expression platform for transcription factor (TF) genes based in quantitative reverse transcriptase-polymerase chain reaction revealed that 37 TF genes were differentially expressed in P-deficient nodules, only one gene was repressed. Data from performed non-targeted metabolic profile indicated that amino acids and other N metabolites were decreased while organic and polyhydroxy acids were accumulated in P-deficient nodules. Bioinformatics analyses using MapMan and PathExpress software tools, customized to common bean, were utilized for the analysis of global changes in gene expression that affected overall metabolism. Glycolysis and glycerolipid metabolism, and starch and sucrose metabolism were identified among the pathways significantly induced or repressed in P-deficient nodules, respectively.

Genetic analyses of plant symbiotic mutants has led to the identification of key genes involved in Rhizobium-legume communication as well as in development and function of nitrogen fixing root nodules. However, the impact of these genes in coordinating the transcriptional programs of nodule development has only been studied in limited and isolated studies. Here, we present an integrated genome-wide analysis of transcriptome landscapes in Lotus japonicus wild-type and symbiotic mutant plants. Encompassing five different organs, five stages of the sequentially developed determinate Lotus root nodules, and eight mutants impaired at different stages of the symbiotic interaction, our data set integrates an unprecedented combination of organ- or tissue-specific profiles with mutant transcript profiles. In total, 38 different conditions sampled under the same well-defined growth regimes were included. This comprehensive analysis unravelled new and unexpected patterns of transcriptional regulation during symbiosis and organ development. Contrary to expectations, none of the previously characterized nodulins were among the 37 genes specifically expressed in nodules. Another surprise was the extensive transcriptional response in whole root compared to the susceptible root zone where the cellular response is most pronounced. A large number of transcripts predicted to encode transcriptional regulators, receptors and proteins involved in signal transduction, as well as many genes with unknown function, were found to be regulated during nodule organogenesis and rhizobial infection. Combining wild type and mutant profiles of these transcripts demonstrates the activation of a complex genetic program that delineates symbiotic nitrogen fixation. The complete data set was organized into an indexed expression directory that is accessible from a resource database, and here we present selected examples of biological questions that can be addressed with this comprehensive and powerful gene expression data set.