Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS)

Vempati, Uma D.; Chung, Caty; Mader, Chris; Koleti, Amar; Datar, Nakul; Vidović, Dušica; Wrobel, David M.; Erickson, Sean D.; Muhlich, Jeremy; Berriz, Gabriel F.; Benes, Cyril H.; Subramanian, Aravind; Pillai, Ajay S.; Shamu, Caroline E.; Schürer, Stephan C.

doi:10.1177/1087057114522514

Cited by 70 publications

(73 citation statements)

References 23 publications

(27 reference statements)

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“…Studies utilizing RPPA data have employed a diverse range of data (pre)processing and benchmarking methods, but no single protocol for processing RPPA data has yet been universally accepted (32)(33)(34)75). Although inclusion of RPPA is still lacking, there are ongoing efforts to standardize and coordinate dissemination of different data types (76). Here, we mean-centered and standardized the protein arrays before model construction.…”

Section: Discussionmentioning

confidence: 99%

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways

Erdem

Nagle

Casà

et al. 2016

Molecular & Cellular Proteomics

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Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. Insulin (Ins)1 and type I insulin-like growth factor (IGF1) receptors (InsR and IGF1R, respectively) are receptor tyrosine kinases that are expressed in almost all types of cells. Signaling through InsR and IGF1R initiates a phosphorylation cascade that drives cell growth and proliferation (1-8). Overexpression of these receptors is correlated with higher breast cancer risk (9 -15) and has been shown to influence tumorigenesis, metastasis, and resistance to existing forms of cancer therapy (10,16,17). IGF receptor blockade can slow tumor growth and metastasis, but the receptor has proven to be difficult to target specifically (18 -20). A confounding factor in developing therapies targeting these receptors is their high sequence (ϳ60%) and structural homology. IGF1R and InsR are able to form functional hybrids, and each can partially compensate for the loss or suppression of the other (1-4, 21-24). Moreover, it has been shown that IGF1R signaling is one mechanism of resistance to conventional hormonal therapy (19,(25)(26)(27)(28)(29). Understanding the relationships between IGF1R and InsR signaling cascades and their combinatorial role in cancer is crucial to developing better diagnostics and personalized treatments for cancer.

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Section: Discussionmentioning

confidence: 99%

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways

Erdem

Nagle

Casà

et al. 2016

Molecular & Cellular Proteomics

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“…It should be noted that the aim here is not to create new cell-line/tissue ontologies – entries in the ChEMBL dictionaries are imported from established vocabularies and ontologies wherever possible (e.g. Cell Line Ontology (23), Experimental Factor Ontology (EFO) (24), Cellosaurus (http://web.expasy.org/cellosaurus/) and LINCS cell dictionary (25) for cells; and Uberon (26), EFO, Brenda Tissue Ontology (27) and CALOHA for tissues (ftp://ftp.nextprot.org/pub/current_release/controlled_vocabularies/caloha.obo)) and mappings to these ontologies are provided.…”

Section: New Functionalitymentioning

confidence: 99%

The ChEMBL database in 2017

et al. 2016

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ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 and 2014 Nucleic Acids Research Database Issues. Since then, alongside the continued extraction of data from the medicinal chemistry literature, new sources of bioactivity data have also been added to the database. These include: deposited data sets from neglected disease screening; crop protection data; drug metabolism and disposition data and bioactivity data from patents. A number of improvements and new features have also been incorporated. These include the annotation of assays and targets using ontologies, the inclusion of targets and indications for clinical candidates, addition of metabolic pathways for drugs and calculation of structural alerts. The ChEMBL data can be accessed via a web-interface, RDF distribution, data downloads and RESTful web-services.

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“…A few exceptions, such as connectivity map (CMap) 2 , represent proof-of-concept studies rather than scalable approaches and have either been restricted to a handful of cell lines or replaced by methodologies that report on a limited number of genes (e.g., Luminex L1000 reporters) 3 .…”

Section: Introductionmentioning

confidence: 99%

PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens

et al. 2017

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Pharmacological and functional genomic screens play an essential role in the discovery and characterization of therapeutic targets and associated pharmacological inhibitors. Although these screens affect thousands of gene products, the typical readout is based on low complexity rather than genome-wide assays. To address this limitation, we introduce pooled library amplification for transcriptome expression (PLATE-Seq), a low-cost, genome-wide mRNA profiling methodology specifically designed to complement high-throughput screening assays. Introduction of sample-specific barcodes during reverse transcription supports pooled library construction and low-depth sequencing that is 10- to 20-fold less expensive than conventional RNA-Seq. The use of network-based algorithms to infer protein activity from PLATE-Seq data results in comparable reproducibility to 30 M read sequencing. Indeed, PLATE-Seq reproducibility compares favorably to other large-scale perturbational profiling studies such as the connectivity map and library of integrated network-based cellular signatures.

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Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS)

Cited by 70 publications

References 23 publications

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways

The ChEMBL database in 2017

PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens

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