Developing Predictive Molecular Maps of Human Disease through Community-based Modeling

Derry, Jonathan M.J.; Mangravite, Lara M.; Suver, Christine; Furia, Matt; Henderson, David C.; Schildwachter, Xavier; Izant, Jonathan G.; Sieberts, Solveig K.; Kellen, Michael R.; Friend, Stephen H.

doi:10.1038/npre.2011.5883.1

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…[15] SAGE anticipe la nécessité de mettre en commun les masses d'information issues de l'analyse moléculaire des maladies, en particulier des cancers, pour décrire des modèles moléculaire prédictifs (et pertinents) de ces maladies. Ceci constituera une ressource considérable pour le développement de nouveaux médicaments.…”

Section: Des Exemples à Succès De Recherche Précompétitiveunclassified

Recherche précompétitive, une mise en synergie des ressources publiques et privées : pour quels objectifs, comment et dans quel cadre juridique ?

et al. 2011

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Résumé -L'objectif de la Table Ronde était d'aborder la Recherche Pré-compétitive pour la première fois lors des Rencontres de Giens. Les participants venaient de l'industrie du médicament, des biotech, de l'industrie du dispositif, de l'académie, des services et sociétés de valorisation, des ministères, du monde de la recherche clinique et fondamentale. Ils ont fait le constat des changements profonds dans la connaissance des maladies et dans le développement des médicaments. À l'aide d'exemples dans le monde de la Santé et en dehors, ils ont identifié les principes, les enjeux, les facteurs de succès et de risque de la recherche précompétitive. Les particularités de la gestion de la propriété intellectuelle ont été plus particulièrement étudiées. La Table Ronde a conclu sur les principes de la recherche précompétitive en Santé, une révolution culturelle nécessaire. C'est développer une recherche en partenariat fondée sur des principes nouveaux : i) comprendre et partager ; ii) intégrer la complexité du vivant ; iii) agir dans l'interdisciplinarité.

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Section: Des Exemples à Succès De Recherche Précompétitiveunclassified

Recherche précompétitive, une mise en synergie des ressources publiques et privées : pour quels objectifs, comment et dans quel cadre juridique ?

et al. 2011

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“…We downloaded TCGA breast cancer (BRCA) level 3 normalized mRNA expression data derived from the Agilent expression platform and the normalized RPPA protein expression data for 164 proteins and phosphoproteins (Supplemental Material 1, 2) from the Synapse website (https://www.synapse.org/; Derry et al 2012). Both gene expression and protein expression data were available for 397 BRCA tumors (excluding normal-like).…”

Section: Data and Preprocessingmentioning

confidence: 99%

“…We downloaded the METABRIC (Curtis et al 2012) from the Synapse website (Derry et al 2012), TRANSBIG (Desmedt et al 2007 Inferred transcription factor activity/protein activity and subtype associations Associations between inferred TF activity and subtype were assessed using the Mann-Whitney U-test on inferred activity values over paired groups of samples: (1) basal-like vs. HER2, LumA, LumB; (2) HER2 vs. LumA, LumB; (3) HER2 vs. basal-like; and (4) LumA vs. LumB. To evaluate the significance of each comparison, we used a permutation approach under which 1000 random W (TF-protein interaction) matrices were generated for each TF to compute an empirical null distribution for the test statistic.…”

Section: Data and Preprocessingmentioning

confidence: 99%

Linking signaling pathways to transcriptional programs in breast cancer

et al. 2014

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Cancer cells acquire genetic and epigenetic alterations that often lead to dysregulation of oncogenic signal transduction pathways, which in turn alters downstream transcriptional programs. Numerous methods attempt to deduce aberrant signaling pathways in tumors from mRNA data alone, but these pathway analysis approaches remain qualitative and imprecise. In this study, we present a statistical method to link upstream signaling to downstream transcriptional response by exploiting reverse phase protein array (RPPA) and mRNA expression data in The Cancer Genome Atlas (TCGA) breast cancer project. Formally, we use an algorithm called affinity regression to learn an interaction matrix between upstream signal transduction proteins and downstream transcription factors (TFs) that explains target gene expression. The trained model can then predict the TF activity, given a tumor sample’s protein expression profile, or infer the signaling protein activity, given a tumor sample’s gene expression profile. Breast cancers are comprised of molecularly distinct subtypes that respond differently to pathway-targeted therapies. We trained our model on the TCGA breast cancer data set and identified subtype-specific and common TF regulators of gene expression. We then used the trained tumor model to predict signaling protein activity in a panel of breast cancer cell lines for which gene expression and drug response data was available. Correlations between inferred protein activities and drug responses in breast cancer cell lines grouped several drugs that are clinically used in combination. Finally, inferred protein activity predicted the clinical outcome within the METABRIC Luminal A cohort, identifying high- and low-risk patient groups within this heterogeneous subtype.

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“…The data generators also provided metadata on the samples, including the geographical source of the malaria parasites, presence of specific mutations at key genetic loci associated with the emerging artemisinin resistance in Southeast Asia, experiment batch information, etc. All the data were stored on the Synapse platform (Derry et al 2012) and were accessible only to participants. It is important to note that the hackathon organizers undertook steps to ensure that the data set used in the hackathon remains confidential until the DREAM crowdsourcing challenge is launched.…”

Section: Data Setsmentioning

confidence: 99%

Hackathons as a means of accelerating scientific discoveries and knowledge transfer

et al. 2018

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Scientific research plays a key role in the advancement of human knowledge and pursuit of solutions to important societal challenges. Typically, research occurs within specific institutions where data are generated and subsequently analyzed. Although collaborative science bringing together multiple institutions is now common, in such collaborations the analytical processing of the data is often performed by individual researchers within the team, with only limited internal oversight and critical analysis of the workflow prior to publication. Here, we show how hackathons can be a means of enhancing collaborative science by enabling peer review before results of analyses are published by cross-validating the design of studies or underlying data sets and by driving reproducibility of scientific analyses. Traditionally, in data analysis processes, data generators and bioinformaticians are divided and do not collaborate on analyzing the data. Hackathons are a good strategy to build bridges over the traditional divide and are potentially a great agile extension to the more structured collaborations between multiple investigators and institutions.

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Developing Predictive Molecular Maps of Human Disease through Community-based Modeling

Cited by 9 publications

References 22 publications

Recherche précompétitive, une mise en synergie des ressources publiques et privées : pour quels objectifs, comment et dans quel cadre juridique ?

Recherche précompétitive, une mise en synergie des ressources publiques et privées : pour quels objectifs, comment et dans quel cadre juridique ?

Linking signaling pathways to transcriptional programs in breast cancer

Hackathons as a means of accelerating scientific discoveries and knowledge transfer

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