Evidence map›Paper›PMID 27567952›Full record

ArticleTranslational oncology2016

Antibody-Guided In Vivo Imaging for Early Detection of Mammary Gland Tumors.

Laura Jeffords Moore, Lopamudra Das Roy, Ru Zhou, Priyanka Grover, Shu-Ta Wu, Jennifer M Curry, Lloye M Dillon, Priya M Puri, Mahboubeh Yazdanifar, Rahul Puri and 2 more

Open access · goldAbstract read
In one paragraph

Article in Translational oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.0field-weighted citation impact, top 11% 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

18 citing papers in PubMed, 28 citations in OpenAlex.

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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

12 authors at 1 institution in 1 country.

Laura Jeffords MooreDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA.
Lopamudra Das RoyDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA; OncoTAb, Inc., 243 Bioinformatics, 9201 University City Blvd., Charlotte, NC 28223, USA.
Ru ZhouDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA.
Priyanka GroverDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA.
Shu-Ta WuDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA.
Jennifer M CurryDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA.
Lloye M DillonDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA; OncoTAb, Inc., 243 Bioinformatics, 9201 University City Blvd., Charlotte, NC 28223, USA.
Priya M PuriDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA.
Mahboubeh YazdanifarDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA.
Rahul PuriOncoTAb, Inc., 243 Bioinformatics, 9201 University City Blvd., Charlotte, NC 28223, USA.
Pinku MukherjeeDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA; OncoTAb, Inc., 243 Bioinformatics, 9201 University City Blvd., Charlotte, NC 28223, USA.
Didier DréauDepartment of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 USA. Electronic address: ddreau@uncc.edu.
University of North Carolina at Charlotte · US

Funding

Multifunctional nanoparticles for combinational therapy of pancreatic cancerR15CA192160 · NCI · UNIVERSITY OF NORTH CAROLINA CHARLOTTE · PI VIVERO-ESCOTO, JUAN LUIS · 2015 to 2015
$437k
6 · The paper itself

Abstract

backgroundEarlier detection of transformed cells using target-specific imaging techniques holds great promise. We have developed TAB 004, a monoclonal antibody highly specific to a protein sequence accessible in the tumor form of MUC1 (tMUC1). We present data assessing both the specificity and sensitivity of TAB 004 in vitro and in genetically engineered mice in vivo.

methodsPolyoma Middle T Antigen mice were crossed to the human MUC1.Tg mice to generate MMT mice. In MMT mice, mammary gland hyperplasia is observed between 6 and 10 weeks of age that progresses to ductal carcinoma in situ by 12 to 14 weeks and adenocarcinoma by 18 to 24 weeks. Approximately 40% of these mice develop metastasis to the lung and other organs with a tumor evolution that closely mimics human breast cancer progression. Tumor progression was monitored in MMT mice (from ages 8 to 22 weeks) by in vivo imaging following retro-orbital injections of the TAB 004 conjugated to indocyanine green (TAB-ICG). At euthanasia, mammary gland tumors and normal epithelial tissues were collected for further analyses.

resultsIn vivo imaging following TAB-ICG injection permitted significantly earlier detection of tumors compared with physical examination. Furthermore, TAB-ICG administration in MMT mice enabled the detection of lung metastases while sparing recognition of normal epithelia.

conclusionsThe data highlight the specificity and the sensitivity of the TAB 004 antibody in differentiating normal versus tumor form of MUC1 and its utility as a targeted imaging agent for early detection, tumor monitoring response, as well as potential clinical use for targeted drug delivery.

Identifiers

PMID27567952
PMCPMC5006816
OpenAlexW2509216739

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.