Evidence map›Paper›PMID 25358623›Full record

ReviewMolecular diagnosis & therapy2014

Reverse phase protein arrays: mapping the path towards personalized medicine.

Rosa I Gallagher, Virginia Espina

Abstract readReview
In one paragraph

Review in Molecular diagnosis & therapy, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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  17. A pre-existing population of ZEB2Journal of experimental & clinical cancer research : CR · 2020
    Article
  18. Proteomics for cancer drug design.Expert review of proteomics · 2019
    Article
  19. Article
  20. 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

2 authors.

Rosa I GallagherCenter for Applied Proteomics and Molecular Medicine, George Mason University, 10900 University Blvd, MS1A9, Manassas, VA, 20110, USA.
Virginia Espina

Funding

Implementation of phosphoprotein preservation technology for cancer biospecimensR33CA157403 · NCI · GEORGE MASON UNIVERSITY · PI LIOTTA, LANCE ALLEN · 2011 to 2013
$922k
NCI NIH HHS 1R33CA157403-01NCI NIH HHS R33 CA157403
6 · The paper itself

Abstract

Reverse phase protein array (RPPA) technology evolved from the advent of miniaturized immunoassays and gene microarray technology. Reverse phase protein arrays provide either a low throughput or high throughput methodology for quantifying proteins and their post-translationally modified forms in both cellular and non-cellular samples. As the demand for patient tailored therapies increases so does the need for precise and sensitive technology to accurately profile the molecular circuitry driving an individual patient's disease. RPPAs are currently utilized in clinical trials for profiling and comparing the functional state of protein signaling pathways, either temporally within tumors, between patients, or within the same patients before/after treatment. RPPAs are generally employed for quantifying large numbers of samples on one array, under identical experimental conditions. However, the goal of personalized cancer medicine is to design therapies based on the molecular portrait of a patient's tumor, which in turn result in more efficacious treatments with less toxicity. Therefore, RPPAs are also being validated for low throughput assays of individual patient samples. This review explores RPPA technology in the cancer research field, concentrating on its role as a fundamental tool for deciphering protein signaling networks and its emerging role in personalized medicine.

Indexed as

Precision MedicineHumansNeoplasm ProteinsNeoplasmsProtein Array AnalysisProteomicsSignal TransductionNeoplasm Proteins

Identifiers

PMID25358623
PMCPMC4732707

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Registered trials

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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.