Evidence map›Paper›PMID 26615774›Full record

ArticleScientific reports2015

Drug target prioritization by perturbed gene expression and network information.

Zerrin Isik, Christoph Baldow, Carlo Vittorio Cannistraci, Michael Schroeder

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

68 citing papers in PubMed.

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8 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zerrin IsikBioinformatics Group, Biotechnology Center (BIOTEC), Technische Universitat Dresden, Tatzberg 47-49, 01307 Dresden, Germany.
Christoph BaldowInstitute for Medical Informatics and Biometry, Technische Universitat Dresden, Blasewitzer 86, 01307 Dresden, Germany.
Carlo Vittorio CannistraciBiomedical Cybernetics Group, BIOTEC, Technische Universitat Dresden, Tatzberg 47-49, 01307 Dresden, Germany.
Michael SchroederBioinformatics Group, Biotechnology Center (BIOTEC), Technische Universitat Dresden, Tatzberg 47-49, 01307 Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs' targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

Indexed as

Drug DiscoveryAlgorithmsCell LineComputational BiologyGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansProtein Interaction MapsReproducibility of ResultsROC CurveSignal Transduction

Identifiers

PMID26615774
PMCPMC4663505

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.