Evidence map›Paper›PMID 27600238›Full record

ReviewMicroarrays (Basel, Switzerland)2015

Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery.

Astrid Wachter, Stephan Bernhardt, Tim Beissbarth, Ulrike Korf

Open access · goldAbstract readReview
In one paragraph

Review in Microarrays (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

6 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
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 at 2 institutions in 1 country.

Astrid WachterStatistical Bioinformatics, Department of Medical Statistics, University Medical Center Goettingen, Humboldtallee 32, D-37073 Goettingen, Germany. astrid.wachter@med.uni-goettingen.de.
Stephan BernhardtGerman Cancer Research Center (DKFZ). s.bernhardt@dkfz-heidelberg.de.
Tim BeissbarthStatistical Bioinformatics, Department of Medical Statistics, University Medical Center Goettingen, Humboldtallee 32, D-37073 Goettingen, Germany. tim.beissbarth@ams.med.uni-goettingen.de.
Ulrike KorfGerman Cancer Research Center (DKFZ). u.korf@dkfz.de.
German Cancer Research Center · DEUniversity of Göttingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.

Indexed as

antibodycancerimmunoassayreverse phase protein arrayreverse phase protein arrays (RPPA)signaling pathways

Identifiers

PMID27600238
PMCPMC4996411
OpenAlexW1959107924

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.