Evidence map›Paper›PMID 21968569›Full record

ArticleJournal of visualized experiments : JoVE2011

Colorectal cancer cell surface protein profiling using an antibody microarray and fluorescence multiplexing.

Jerry Zhou, Larissa Belov, Michael J Solomon, Charles Chan, Stephen J Clarke, Richard I Christopherson

Erratum issuedOpen access · bronzeAbstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Single Domain Antibodies as New Biomarker Detectors.Diagnostics (Basel, Switzerland) · 2017
    Review
  5. Protein Array-based Approaches for Biomarker Discovery in Cancer.Genomics, proteomics & bioinformatics · 2017
    Review
  6. Article
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Jerry ZhouSchool of Molecular Bioscience, University of Sydney. j.zhou@uws.edu.au
Larissa Belov
Michael J Solomon
Charles Chan
Stephen J Clarke
Richard I Christopherson
University of Sydney · AUConcord Repatriation General Hospital · AURoyal Prince Alfred Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current prognosis and classification of CRC relies on staging systems that integrate histopathologic and clinical findings. However, in the majority of CRC cases, cell dysfunction is the result of numerous mutations that modify protein expression and post-translational modification(1). A number of cell surface antigens, including cluster of differentiation (CD) antigens, have been identified as potential prognostic or metastatic biomarkers in CRC. These antigens make ideal biomarkers as their expression often changes with tumour progression or interactions with other cell types, such as tumour-infiltrating lymphocytes (TILs) and tumour-associated macrophages (TAMs). The use of immunohistochemistry (IHC) for cancer sub-classification and prognostication is well established for some tumour types(2,3). However, no single 'marker' has shown prognostic significance greater than clinico-pathological staging or gained wide acceptance for use in routine pathology reporting of all CRC cases. A more recent approach to prognostic stratification of disease phenotypes relies on surface protein profiles using multiple 'markers'. While expression profiling of tumours using proteomic techniques such as iTRAQ is a powerful tool for the discovery of biomarkers4, it is not optimal for routine use in diagnostic laboratories and cannot distinguish different cell types in a mixed population. In addition, large amounts of tumour tissue are required for the profiling of purified plasma membrane glycoproteins by these methods. In this video we described a simple method for surface proteome profiling of viable cells from disaggregated CRC samples using a DotScan CRC antibody microarray. The 122-antibody microarray consists of a standard 82-antibody region recognizing a range of lineage-specific leukocyte markers, adhesion molecules, receptors and markers of inflammation and immune response(5), together with a satellite region for detection of 40 potentially prognostic markers for CRC. Cells are captured only on antibodies for which they express the corresponding antigen. The cell density per dot, determined by optical scanning, reflects the proportion of cells expressing that antigen, the level of expression of the antigen and affinity of the antibody(6). For CRC tissue or normal intestinal mucosa, optical scans reflect the immunophenotype of mixed populations of cells. Fluorescence multiplexing can then be used to profile selected sub-populations of cells of interest captured on the array. For example, Alexa 647-anti-epithelial cell adhesion molecule (EpCAM; CD326), is a pan-epithelial differentiation antigen that was used to detect CRC cells and also epithelial cells of normal intestinal mucosa, while Phycoerythrin-anti-CD3, was used to detect infiltrating T-cells(7). The DotScan CRC microarray should be the prototype for a diagnostic alternative to the anatomically-based CRC staging system.

Indexed as

Antibodies, NeoplasmColorectal NeoplasmsHumansNeoplasm ProteinsProtein Array AnalysisProteomeProteomicsAntibodies, NeoplasmNeoplasm ProteinsProteome

Identifiers

PMID21968569
PMCPMC3230216
OpenAlexW2035684482

What OpenQuestion holds

Textmetadata
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.