Evidence map›Paper›PMID 25013898›Full record

ArticlePloS one2014

Reliable quantification of protein expression and cellular localization in histological sections.

Michaela Schlederer, Kristina M Mueller, Johannes Haybaeck, Susanne Heider, Nicole Huttary, Margit Rosner, Markus Hengstschläger, Richard Moriggl, Helmut Dolznig, Lukas Kenner

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.0field-weighted citation impact, top 8% of its field
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

20 citing papers in PubMed, 41 citations in OpenAlex.

  1. Article
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  5. Growth hormone and nonalcoholic fatty liver disease.Immunometabolism (Cobham, Surrey) · 2023 · on this map
    Review
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  9. SOX2 and Rb1 in esophageal small-cell carcinoma: their possible involvement in pathogenesis.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2017
    Article
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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

10 authors at 3 institutions in 1 country.

Michaela SchledererLudwig Boltzmann Institute for Cancer Research (LBI-CR), Vienna, Austria.
Kristina M MuellerLudwig Boltzmann Institute for Cancer Research (LBI-CR), Vienna, Austria.
Johannes HaybaeckInstitute of Pathology, Medical University of Graz, Graz, Austria.
Susanne HeiderInstitute of Clinical Pathology, Medical University of Vienna, Vienna, Austria.
Nicole HuttaryInstitute of Clinical Pathology, Medical University of Vienna, Vienna, Austria.
Margit RosnerInstitute of Medical Genetics, Medical University of Vienna, Vienna, Austria.
Markus HengstschlägerInstitute of Medical Genetics, Medical University of Vienna, Vienna, Austria.
Richard MorigglLudwig Boltzmann Institute for Cancer Research (LBI-CR), Vienna, Austria; Unit for Translational Methods in Cancer Research University of Veterinary Medicine Vienna (Vetmeduni Vienna), Vienna, Austria.
Helmut DolznigInstitute of Medical Genetics, Medical University of Vienna, Vienna, Austria.
Lukas KennerLudwig Boltzmann Institute for Cancer Research (LBI-CR), Vienna, Austria; Institute of Clinical Pathology, Medical University of Vienna, Vienna, Austria; Unit of Pathology of Laboratory Animals, University of Veterinary Medicine Vienna (Vetmeduni Vienna), Vienna, Austria.
Medical University of Vienna · ATLudwig Boltzmann Institute for Cancer Research · ATMedical University of Graz · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In targeted therapy, patient tumors are analyzed for aberrant activations of core cancer pathways, monitored based on biomarker expression, to ensure efficient treatment. Thus, diagnosis and therapeutic decisions are often based on the status of biomarkers determined by immunohistochemistry in combination with other clinical parameters. Standard evaluation of cancer specimen by immunohistochemistry is frequently impeded by its dependence on subjective interpretation, showing considerable intra- and inter-observer variability. To make treatment decisions more reliable, automated image analysis is an attractive possibility to reproducibly quantify biomarker expression in patient tissue samples. We tested whether image analysis could detect subtle differences in protein expression levels. Gene dosage effects generate well-graded expression patterns for most gene-products, which vary by a factor of two between wildtype and haploinsufficient cells lacking one allele. We used conditional mouse models with deletion of the transcription factors Stat5ab in the liver as well Junb deletion in a T-cell lymphoma model. We quantified the expression of total or activated STAT5AB or JUNB protein in normal (Stat5ab+/+ or JunB+/+), hemizygous (Stat5ab+/Δ or JunB+/Δ) or knockout (Stat5abΔ/Δ or JunBΔ/Δ) settings. Image analysis was able to accurately detect hemizygosity at the protein level. Moreover, nuclear signals were distinguished from cytoplasmic expression and translocation of the transcription factors from the cytoplasm to the nucleus was reliably detected and quantified using image analysis. We demonstrate that image analysis supported pathologists to score nuclear STAT5AB expression levels in immunohistologically stained human hepatocellular patient samples and decreased inter-observer variability.

Indexed as

AnimalsBiomarkers, TumorCell NucleusCytoplasmImmunohistochemistryMiceMice, KnockoutReproducibility of ResultsSTAT5 Transcription FactorTranscription FactorsBiomarkers, TumorJunB protein, mouseStat5a protein, mouseStat5b protein, mouseSTAT5 Transcription FactorTranscription Factors

Identifiers

PMID25013898
PMCPMC4094387
OpenAlexW2047708479

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.