Genomic “Dark Matter” in Prostate Cancer: Exploring the Clinical Utility of ncRNA as Biomarkers

Prostate cancer is the most diagnosed cancer among men in the United States. While the majority of patients who undergo surgery (prostatectomy) will essentially be cured, about 30–40% men remain at risk for disease progression and recurrence. Currently, patients are deemed at risk by evaluation of clinical factors, but these do not resolve whether adjuvant therapy will significantly attenuate or delay disease progression for a patient at risk. Numerous efforts using mRNA-based biomarkers have been described for this purpose, but none have successfully reached widespread clinical practice in helping to make an adjuvant therapy decision. Here, we assess the utility of non-coding RNAs as biomarkers for prostate cancer recurrence based on high-resolution oligonucleotide microarray analysis of surgical tissue specimens from normal adjacent prostate, primary tumors, and metastases. We identify differentially expressed non-coding RNAs that distinguish between the different prostate tissue types and show that these non-coding RNAs can predict clinical outcomes in primary tumors. Together, these results suggest that non-coding RNAs are emerging from the “dark matter” of the genome as a new source of biomarkers for characterizing disease recurrence and progression. While this study shows that non-coding RNA biomarkers can be highly informative, future studies will be needed to further characterize the specific roles of these non-coding RNA biomarkers in the development of aggressive disease

Consequences of high temperatures and premature mortality on the transcriptome and blood physiology of wild adult sockeye salmon (Oncorhynchus nerka)

Elevated river water temperature in the Fraser River, British Columbia, Canada, hasbeen associated with enhanced mortality of adult sockeye salmon (Oncorhynchusnerka) during their upriver migration to spawning grounds.We undertook a studyto assess the effects of elevated water temperatures on the gill transcriptome andblood plasma variables in wild-caught sockeye salmon. Naturally migrating sockeyesalmon returning to the Fraser River were collected and held at ecologicallyrelevant temperatures of 14◦C and 19◦C for seven days, a period representing asignificant portion of their upstream migration. After seven days, sockeye salmonheld at 19◦C stimulated heat shock response genes as well as many genes associated with an immune response when compared with fish held at 14◦C. Additionally, fish at 19◦C had elevated plasma chloride and lactate, suggestive of a disturbance in osmoregulatory homeostasis and a stress response detectable in the blood plasma. Fish that died prematurely over the course of the holding study were compared with time-matched surviving fish; the former fish were characterized by an upregulation of several transcription factors associated with apoptosis and downregulation of genes involved in immune function and antioxidant activity. Ornithine decarboxylase(ODC1) was the most significantly upregulated gene in dying salmon, which suggests an association with cellular apoptosis. We hypothesize that the observed decrease in plasma ions and increases in plasma cortisol that occur in dying fish may be linked to the increase in ODC1. By highlighting these underlyingphysiological mechanisms, this study enhances our understanding of the processesinvolved in premature mortality and temperature stress in Pacific salmon duringmigration to spawning grounds.<br /

Méthodes probabilistes, floues et quantiques pour l'extraction de l'information biologique

Advances in measurement technology and sequencing of genomes, have led to the emergence of DNA microarray technology in the 90's, allowing overall measurement of gene expression. This type of experience is said "high throughput" because of the volume of data they generate requiring automatic processing for results interpretation. In this context, many approaches have been developed and can be divided into two families: supervised and unsupervised classification methods. We present here "semantic distillation" a novel unsupervised classification approach, based on a formalism inspired by the physical measurement in quantum mechanics, for the analysis of results from DNA chips. This method provides the user with an ordered list of specific genes for each biological sample of the experience, and describing each cellular context and the influence of each gene in these contexts. Semantic distillation was tested on two data sets: a "tissue-specific" set for which our method has correctly characterised specific genes for each tissue, and clinical data sets of patients with liver fibrosis at various stages for which semantic distillation helped to find signatures in metabolic pathways and biological processes associated with specific genes of each stage of the disease.Les progrès des technologies de mesure et le séquençage des génomes, ont permis l'émergence, dans les années 1990, de techniques de mesure globale de l'expression génique, les puces à ADN. Ce type d'expérience, dit à " haut débit ", en raison du volume de données qu'elles génèrent nécessitent un traitement automatique pour l'interprétation des résultats. Dans ce but, de nombreuses approches ont été développées, essentiellement réparties en deux familles : les méthodes de classification supervisées et non supervisées. Nous présentons ici la distillation sémantique, une approche de classification non supervisée originale fondée sur un formalisme inspiré de la mesure physique en mécanique quantique permettant l'analyse des résultats d'analyse de puces à ADN. Cette méthode fournit à l'utilisateur une liste de gènes ordonnée par spécificité pour chaque échantillon biologique de l'expérience, décrivant ainsi chaque contexte cellulaire ainsi que l'influence de chaque gène dans ces contextes. Celleci a été mise à l'épreuve sur deux jeux de données : un jeu " tissus-spécifique " pour lequel notre méthode a correctement caractérisé les gènes spécifiques de chaque tissu, et un jeu de données cliniques de patients atteints de fibroses hépatiques à divers stades pour lequel la distillation sémantique a permis de trouver des signatures dans les voies métaboliques et les processus biologiques associés aux gènes spécifiques de chaque stade de la maladie

Quantum information retrieval and gene networks

We report on the use of quantum information retrieval methods to enhance, by automatic contextualisation, the relevance of retrieved documents. This methods mimics brain ability of contextualisation and association and proves useful in organising information from DNA micro-arrays

Fuzzy and quantum methods of information retrieval to analyse genomic data from patients at different stages of fibrosis

International audienceWe have recently introduced a novel method of unsupervised clustering, we termed semantic distillation, inspired from the quantum theory of measurement, allowing to analyse statistical data and regroup objects according to their contextual specificity. Here we introduce an improvement of the method to make it applicable even in the more difficult case where data have very small statistical variability from sample to sample. We applied our method to a dataset from DNA arrays of different anatomopathological samples concerning 14 patients suffering from fibrosis. The clusters produced by our method have been automatically indexed by the evolutionary stage of the disease and the genes regrouped therein identified as responsible of biological processes specific for every stage

Méthodes probabilistes, floues et quantiques pour l'extraction de l'information biologique

Les progrès des technologies de mesure et le séquençage des génomes, ont permis l émergence, dans les années 1990, de techniques de mesure globale de l expression génique, les puces à ADN. Ce type d expérience, dit à haut débit , en raison du volume de données qu elles génèrent nécessitent un traitement automatique pour l interprétation des résultats. Dans ce but, de nombreuses approches ont été développées, essentiellement réparties en deux familles : les méthodes de classification supervisées et non supervisées. Nous présentons ici la distillation sémantique, une approche de classification non supervisée originale fondée sur un formalisme inspiré de la mesure physique en mécanique quantique permettant l analyse des résultats d analyse de puces à ADN. Cette méthode fournit à l utilisateur une liste de gènes ordonnée par spécificité pour chaque échantillon biologique de l expérience, décrivant ainsi chaque contexte cellulaire ainsi que l influence de chaque gène dans ces contextes. Celleci a été mise à l épreuve sur deux jeux de données : un jeu tissus-spécifique pour lequel notre méthode a correctement caractérisé les gènes spécifiques de chaque tissu, et un jeu de données cliniques de patients atteints de fibroses hépatiques à divers stades pour lequel la distillation sémantique a permis de trouver des signatures dans les voies métaboliques et les processus biologiques associés aux gènes spécifiques de chaque stade de la maladie.Advances in measurement technology and sequencing of genomes, have led to the emergence of DNA microarray technology in the 90 s, allowing overall measurement of gene expression. This type of experience is said high throughput because of the volume of data they generate requiring automatic processing for results interpretation. In this context, many approaches have been developed and can be divided into two families: supervised and unsupervised classification methods. We present here semantic distillation a novel unsupervised classification approach, based on a formalism inspired by the physical measurement in quantum mechanics, for the analysis of results from DNA chips. This method provides the user with an ordered list of specific genes for each biological sample of the experience, and describing each cellular context and the influence of each gene in these contexts. Semantic distillation was tested on two data sets: a tissue-specific set for which our method has correctly characterised specific genes for each tissue, and clinical data sets of patients with liver fibrosis at various stages for which semantic distillation helped to find signatures in metabolic pathways and biological processes associated with specific genes of each stage of the disease.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

M@IA: a modular open-source application for microarray workflow and integrative datamining.

International audienceMicroarray technology is a widely used approach to gene expression analysis. Many tools for microarray management and data analysis have been developed, and recently new methods have been proposed for deciphering biological pathways by integrating microarray data with other data sources. However, to improve microarray analysis and provide meaningful gene interaction networks, integrated software solutions are still needed. Therefore, we developed M@IA, an environment for DNA microarray data analysis allowing gene network reconstruction. M@IA is a microarray integrated application which includes all of the steps of a microarray study, from MIAME-compliant raw data storage and processing gene expression analysis. Furthermore, M@IA allows automatic gene annotation based on ontology, metabolic/signalling pathways, protein interaction, miRNA and transcriptional factor associations, as well as integrative analysis of gene interaction networks. Statistical and graphical methods facilitate analysis, yielding new hypotheses on gene expression data. To illustrate our approach, we applied M@IA modules to microarray data taken from an experiment on liver tissue. We integrated differentially expressed genes with additional biological information, thus identifying new molecular interaction networks that are associated with fibrogenesis. M@IA is a new application for microarray management and data analysis, offering functional insights into microarray data by the combination of gene expression data and biological knowledge annotation based on interactive graphs. M@IA is an interactive multi-user interface based on a flexible modular architecture and it is freely available for academic users at http://maia.genouest.org