A pseudo-R2 measure for selecting genomic markers with crossing hazards functions

<p>Abstract</p> <p>Background</p> <p>In genomic medical studies, one of the major objectives is to identify genomic factors with a prognostic impact on time-to-event outcomes so as to provide new insights into the disease process. Selection usually relies on statistical univariate indices based on the Cox model. Such model assumes proportional hazards (PH) which is unlikely to hold for each genomic marker.</p> <p>Methods</p> <p>In this paper, we introduce a novel pseudo-R²measure derived from a crossing hazards model and designed for the selection of markers with crossing effects. The proposed index is related to the score statistic and quantifies the extent of a genomic factor to separate patients according to their survival times and marker measurements. We also show the importance of considering genomic markers with crossing effects as they potentially reflect the complex interplay between markers belonging to the same pathway.</p> <p>Results</p> <p>Simulations show that our index is not affected by the censoring and the sample size of the study. It also performs better than classical indices under the crossing hazards assumption. The practical use of our index is illustrated in a lung cancer study. The use of the proposed pseudo-R²allows the identification of cell-cycle dependent genes not identified when relying on the PH assumption.</p> <p>Conclusions</p> <p>The proposed index is a novel and promising tool for selecting markers with crossing hazards effects.</p

Efficiency of Phytoseiulus longipes Evans as a control agent of Tetranychus evansi Baker & Pritchard (Acari: Phytoseiidae: Tetranychidae) on screenhouse tomatoes

The spider mite Tetranychus evansi Baker & Pritchard can cause severe damage to tomato crops. The predatory mite Phytoseiulus longipes Evans was recently reported in association with T. evansi in Uruguaiana, Rio Grande do Sul State, Brazil. The objective of the present study was to evaluate the effects of P. longipes on the population of T. evansi on tomatoes under screenhouse condition. The study consisted on four experiments, in each of which 80 potted plantlets were distributed in two plots of 40 plantlets each. Two weeks later, each plantlet of both plots was infested with eight adult females of T. evansi; one week after, four adult females of P. longipes were released onto each plant of one plot. The population levels of T. evansi and the damage caused by these mites were significantly lower (P < 0.05; linear mixed-effect model) in the plots where P. longipes had been released. The results indicate the potential of this predator as a candidate for classical biological control of T. evansi by inoculative releases on tomato plants.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Federal Ministry for Economic Cooperation and Developmen

Development of a separability index for genomic data in survival analysis

Dans le domaine de l’oncogénomique, l’un des axes actuels de recherche est l’identification de nouveaux marqueurs génétiques permettant entre autres de construire des règles prédictives visant à classer les patients selon le risque d’apparition d’un événement d’intérêt (décès ou récidive tumorale). En présence de telles données de haute dimension, une première étape de sélection parmi l’ensemble des variables candidates est généralement employée afin d’identifier les marqueurs ayant un intérêt explicatif jugé suffisant. Une question récurrente pour les biologistes est le choix de la règle de sélection. Dans le cadre de l’analyse de survie, les approches classiques consistent à ranger les marqueurs génétiques à partir du risque relatif ou de quantités issues de test statistiques (p-value, q-value). Cependant, ces méthodes ne sont pas adaptées à la combinaison de résultats provenant d’études hétérogènes dont les tailles d’échantillons sont très différentes.Utiliser un indice tenant compte à la fois de l’importance de l’effet pronostique et ne dépendant que faiblement de la taille de l’échantillon permet de répondre à cette problématique. Dans ce travail, nous proposons un nouvel indice de capacité de prédiction afin de sélectionner des marqueurs génomiques ayant un impact pronostique sur le délai de survenue d’un évènement.Cet indice étend la notion de pseudo-R2 dans le cadre de l’analyse de survie. Il présente également une interprétation originale et intuitive en terme de « séparabilité ». L’indice est tout d’abord construit dans le cadre du modèle de Cox, puis il est étendu à d’autres modèles plus complexes à risques non-proportionnels. Des simulations montrent que l’indice est peu affectée par la taille de l’échantillon et la censure. Il présente de plus une meilleure séparabilité que les indices classiques de la littérature. L’intérêt de l’indice est illustré sur deux exemples. Le premier consiste à identifier des marqueurs génomiques communs à différents types de cancers. Le deuxième, dans le cadre d’une étude sur le cancer broncho-pulmonaire, montre l’intérêt de l’indice pour sélectionner des facteurs génomiques entraînant un croisement des fonctions de risques instantanés pouvant être expliqué par un effet « modulateur » entre les marqueurs. En conclusion, l’indice proposé est un outil prometteur pouvant aider les chercheurs à identifier des listes de gènes méritant des études plus approfondies.In oncogenomics research, one of the main objectives is to identify new genomic markers so as to construct predictive rules in order to classify patients according to time-to-event outcomes (death or tumor relapse). Most of the studies dealing with such high throughput data usually rely on a selection process in order to identify, among the candidates, the markers having a prognostic impact. A common problem among biologists is the choice of the selection rule. In survival analysis, classical procedures consist in ranking genetic markers according to either the estimated hazards ratio or quantities derived from a test statistic (p-value, q-value). However, these methods are not suitable for gene selection across multiple genomic datasets with different sample sizes.Using an index taking into account the magnitude of the prognostic impact of factors without being highly dependent on the sample size allows to address this issue. In this work, we propose a novel index of predictive ability for selecting genomic markers having a potential impact on timeto-event outcomes. This index extends the notion of "pseudo-R2" in the ramework of survival analysis. It possesses an original and straightforward interpretation in terms of "separability". The index is first derived in the framework of the Cox model and then extended to more complex non-proportional hazards models. Simulations show that our index is not substantially affected by the sample size of the study and the censoring. They also show that its separability performance is higher than indices from the literature. The interest of the index is illustrated in two examples. The first one aims at identifying genomic markers with common effects across different cancertypes. The second shows, in the framework of a lung cancer study, the interest of the index for selecting genomic factor with crossing hazards functions, which could be explained by some "modulating" effects between markers. The proposed index is a promising tool, which can help researchers to select a list of features of interest for further biological investigations

Développement d’un indice de séparabilité adapté aux données de génomique en analyse de survie

In oncogenomics research, one of the main objectives is to identify new genomic markers so as to construct predictive rules in order to classify patients according to time-to-event outcomes (death or tumor relapse). Most of the studies dealing with such high throughput data usually rely on a selection process in order to identify, among the candidates, the markers having a prognostic impact. A common problem among biologists is the choice of the selection rule. In survival analysis, classical procedures consist in ranking genetic markers according to either the estimated hazards ratio or quantities derived from a test statistic (p-value, q-value). However, these methods are not suitable for gene selection across multiple genomic datasets with different sample sizes.Using an index taking into account the magnitude of the prognostic impact of factors without being highly dependent on the sample size allows to address this issue. In this work, we propose a novel index of predictive ability for selecting genomic markers having a potential impact on timeto-event outcomes. This index extends the notion of "pseudo-R2" in the ramework of survival analysis. It possesses an original and straightforward interpretation in terms of "separability". The index is first derived in the framework of the Cox model and then extended to more complex non-proportional hazards models. Simulations show that our index is not substantially affected by the sample size of the study and the censoring. They also show that its separability performance is higher than indices from the literature. The interest of the index is illustrated in two examples. The first one aims at identifying genomic markers with common effects across different cancertypes. The second shows, in the framework of a lung cancer study, the interest of the index for selecting genomic factor with crossing hazards functions, which could be explained by some "modulating" effects between markers. The proposed index is a promising tool, which can help researchers to select a list of features of interest for further biological investigations.Dans le domaine de l’oncogénomique, l’un des axes actuels de recherche est l’identification de nouveaux marqueurs génétiques permettant entre autres de construire des règles prédictives visant à classer les patients selon le risque d’apparition d’un événement d’intérêt (décès ou récidive tumorale). En présence de telles données de haute dimension, une première étape de sélection parmi l’ensemble des variables candidates est généralement employée afin d’identifier les marqueurs ayant un intérêt explicatif jugé suffisant. Une question récurrente pour les biologistes est le choix de la règle de sélection. Dans le cadre de l’analyse de survie, les approches classiques consistent à ranger les marqueurs génétiques à partir du risque relatif ou de quantités issues de test statistiques (p-value, q-value). Cependant, ces méthodes ne sont pas adaptées à la combinaison de résultats provenant d’études hétérogènes dont les tailles d’échantillons sont très différentes.Utiliser un indice tenant compte à la fois de l’importance de l’effet pronostique et ne dépendant que faiblement de la taille de l’échantillon permet de répondre à cette problématique. Dans ce travail, nous proposons un nouvel indice de capacité de prédiction afin de sélectionner des marqueurs génomiques ayant un impact pronostique sur le délai de survenue d’un évènement.Cet indice étend la notion de pseudo-R2 dans le cadre de l’analyse de survie. Il présente également une interprétation originale et intuitive en terme de « séparabilité ». L’indice est tout d’abord construit dans le cadre du modèle de Cox, puis il est étendu à d’autres modèles plus complexes à risques non-proportionnels. Des simulations montrent que l’indice est peu affectée par la taille de l’échantillon et la censure. Il présente de plus une meilleure séparabilité que les indices classiques de la littérature. L’intérêt de l’indice est illustré sur deux exemples. Le premier consiste à identifier des marqueurs génomiques communs à différents types de cancers. Le deuxième, dans le cadre d’une étude sur le cancer broncho-pulmonaire, montre l’intérêt de l’indice pour sélectionner des facteurs génomiques entraînant un croisement des fonctions de risques instantanés pouvant être expliqué par un effet « modulateur » entre les marqueurs. En conclusion, l’indice proposé est un outil prometteur pouvant aider les chercheurs à identifier des listes de gènes méritant des études plus approfondies

Développement d'un indice de séparabilité adapté aux données de génomique en analyse de survie

Dans le domaine de l oncogénomique, l un des axes actuels de recherche est l identification de nouveaux marqueurs génétiques permettant entre autres de construire des règles prédictives visant à classer les patients selon le risque d apparition d un événement d intérêt (décès ou récidive tumorale). En présence de telles données de haute dimension, une première étape de sélection parmi l ensemble des variables candidates est généralement employée afin d identifier les marqueurs ayant un intérêt explicatif jugé suffisant. Une question récurrente pour les biologistes est le choix de la règle de sélection. Dans le cadre de l analyse de survie, les approches classiques consistent à ranger les marqueurs génétiques à partir du risque relatif ou de quantités issues de test statistiques (p-value, q-value). Cependant, ces méthodes ne sont pas adaptées à la combinaison de résultats provenant d études hétérogènes dont les tailles d échantillons sont très différentes.Utiliser un indice tenant compte à la fois de l importance de l effet pronostique et ne dépendant que faiblement de la taille de l échantillon permet de répondre à cette problématique. Dansce travail, nous proposons un nouvel indice de capacité de prédiction afin de sélectionner des marqueurs génomiques ayant un impact pronostique sur le délai de survenue d un évènement.Cet indice étend la notion de pseudo-R2 dans le cadre de l analyse de survie. Il présente également une interprétation originale et intuitive en terme de séparabilité . L indice est tout d abord construit dans le cadre du modèle de Cox, puis il est étendu à d autres modèles plus complexes à risques non-proportionnels. Des simulations montrent que l indice est peu affectée par la taille de l échantillon et la censure. Il présente de plus une meilleure séparabilité que les indices classiques de la littérature. L intérêt de l indice est illustré sur deux exemples. Le premier consiste à identifier des marqueurs génomiques communs à différents types de cancers. Le deuxième, dans le cadre d une étude sur le cancer broncho-pulmonaire, montre l intérêt de l indice pour sélectionner des facteurs génomiques entraînant un croisement des fonctions de risques instantanés pouvant être expliqué par un effet modulateur entre les marqueurs. En conclusion, l indice proposé est un outil prometteur pouvant aider les chercheurs à identifier des listes de gènes méritant des études plus approfondies.In oncogenomics research, one of the main objectives is to identify new genomic markers so as to construct predictive rules in order to classify patients according to time-to-event outcomes (death or tumor relapse). Most of the studies dealing with such high throughput data usually rely on a selection process in order to identify, among the candidates, the markers having a prognostic impact. A common problem among biologists is the choice of the selection rule. In survival analysis, classical procedures consist in ranking genetic markers according to either the estimated hazards ratio or quantities derived from a test statistic (p-value, q-value). However, these methods are not suitable for gene selection across multiple genomic datasets with different sample sizes.Using an index taking into account the magnitude of the prognostic impact of factors without being highly dependent on the sample size allows to address this issue. In this work, we propose a novel index of predictive ability for selecting genomic markers having a potential impact on timeto-event outcomes. This index extends the notion of "pseudo-R2" in the ramework of survival analysis. It possesses an original and straightforward interpretation in terms of "separability". The index is first derived in the framework of the Cox model and then extended to more complex non-proportional hazards models. Simulations show that our index is not substantially affected by the sample size of the study and the censoring. They also show that its separability performance is higher than indices from the literature. The interest of the index is illustrated in two examples. The first one aims at identifying genomic markers with common effects across different cancertypes. The second shows, in the framework of a lung cancer study, the interest of the index for selecting genomic factor with crossing hazards functions, which could be explained by some "modulating" effects between markers. The proposed index is a promising tool, which can help researchers to select a list of features of interest for further biological investigations.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Identifying common prognostic factors in genomic cancer studies: a novel index for censored outcomes.

International audienceBACKGROUND: With the growing number of public repositories for high-throughput genomic data, it is of great interest to combine the results produced by independent research groups. Such a combination allows the identification of common genomic factors across multiple cancer types and provides new insights into the disease process. In the framework of the proportional hazards model, classical procedures, which consist of ranking genes according to the estimated hazard ratio or the p-value obtained from a test statistic of no association between survival and gene expression level, are not suitable for gene selection across multiple genomic datasets with different sample sizes. We propose a novel index for identifying genes with a common effect across heterogeneous genomic studies designed to remain stable whatever the sample size and which has a straightforward interpretation in terms of the percentage of separability between patients according to their survival times and gene expression measurements. RESULTS: The simulations results show that the proposed index is not substantially affected by the sample size of the study and the censoring. They also show that its separability performance is higher than indices of predictive accuracy relying on the likelihood function. A simulated example illustrates the good operating characteristics of our index. In addition, we demonstrate that it is linked to the score statistic and possesses a biologically relevant interpretation.The practical use of the index is illustrated for identifying genes with common effects across eight independent genomic cancer studies of different sample sizes. The meta-selection allows the identification of four genes (ESPL1, KIF4A, HJURP, LRIG1) that are biologically relevant to the carcinogenesis process and have a prognostic impact on survival outcome across various solid tumors. CONCLUSION: The proposed index is a promising tool for identifying factors having a prognostic impact across a collection of heterogeneous genomic datasets of various sizes

Identifying Driver Genes in Cancer by Triangulating Gene Expression, Gene Location, and Survival Data

Driver genes are directly responsible for oncogenesis and identifying them is essential in order to fully understand the mechanisms of cancer. However, it is difficult to delineate them from the larger pool of genes that are deregulated in cancer (ie, passenger genes). In order to address this problem, we developed an approach called TRIAngulating Gene Expression (TRIAGE through clinico-genomic intersects). Here, we present a refinement of this approach incorporating a new scoring methodology to identify putative driver genes that are deregulated in cancer. TRIAGE triangulates - or integrates -three levels of information: gene expression, gene location, and patient survival. First, TRIAGE identifies regions of deregulated expression (ie, expression footprints) by deriving a newly established measure called the Local Singular Value Decomposition (LSVD) score for each locus. Driver genes are then distinguished from passenger genes using dual survival analyses. Incorporating measurements of gene expression and weighting them according to the LSVD weight of each tumor, these analyses are performed using the genes located in significant expression footprints. Here, we first use simulated data to characterize the newly established LSVD score. We then present the results of our application of this refined version of TRIAGE to gene expression data from five cancer types. This refined version of TRIAGE not only allowed us to identify known prominent driver genes, such as MMP1, IL8 , and COL1A2 , but it also led us to identify several novel ones. These results illustrate that TRIAGE complements existing tools, allows for the identification of genes that drive cancer and could perhaps elucidate potential future targets of novel anticancer therapeutics

Systematic Study of Drosophila MicroRNA Functions Using a Collection of Targeted Knockout Mutations

SummaryMicroRNAs are abundant in animal genomes, yet little is known about their functions in vivo. Here, we report the production of 80 new Drosophila miRNA mutants by targeted homologous recombination. These mutants remove 104 miRNAs. Together with 15 previously reported mutants, this collection includes 95 mutants deleting 130 miRNAs. Collectively, these genes produce over 99% of all Drosophila miRNAs, measured by miRNA sequence reads. We present a survey of developmental and adult miRNA phenotypes. Over 80% of the mutants showed at least one phenotype using a p < 0.01 significance threshold. We observed a significant correlation between miRNA abundance and phenotypes related to survival and lifespan, but not to most other phenotypes. miRNA cluster mutants were no more likely than single miRNA mutants to produce significant phenotypes. This mutant collection will provide a resource for future analysis of the biological roles of Drosophila miRNAs