Evidence map›Paper›PMID 23691228›Full record

ArticlePloS one2013

Identification and characterization of poorly differentiated invasive carcinomas in a mouse model of pancreatic neuroendocrine tumorigenesis.

Karen E Hunter, Marsha L Quick, Anguraj Sadanandam, Douglas Hanahan, Johanna A Joyce

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 21 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

5 authors at 2 institutions in 2 countries.

Karen E HunterCancer Biology and Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Marsha L Quick
Anguraj Sadanandam
Douglas Hanahan
Johanna A Joyce
Memorial Sloan Kettering Cancer Center · USÉcole Polytechnique Fédérale de Lausanne · CH

Funding

Dissecting the Function of Cysteine Cathepsins in the Tumor MicroenvironmentR01CA125162 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI JOYCE, JOHANNA · 2007 to 2011
$1.8M
NCI NIH HHS R01 CA125162
6 · The paper itself

Abstract

Pancreatic neuroendocrine tumors (PanNETs) are a relatively rare but clinically challenging tumor type. In particular, high grade, poorly-differentiated PanNETs have the worst patient prognosis, and the underlying mechanisms of disease are poorly understood. In this study we have identified and characterized a previously undescribed class of poorly differentiated PanNETs in the RIP1-Tag2 mouse model. We found that while the majority of tumors in the RIP1-Tag2 model are well-differentiated insulinomas, a subset of tumors had lost multiple markers of beta-cell differentiation and were highly invasive, leading us to term them poorly differentiated invasive carcinomas (PDICs). In addition, we found that these tumors exhibited a high mitotic index, resembling poorly differentiated (PD)-PanNETs in human patients. Interestingly, we identified expression of Id1, an inhibitor of DNA binding gene, and a regulator of differentiation, specifically in PDIC tumor cells by histological analysis. The identification of PDICs in this mouse model provides a unique opportunity to study the pathology and molecular characteristics of PD-PanNETs.

Indexed as

CarcinogenesisDisease Models, AnimalAnimalsBiomarkers, TumorCarcinoma, NeuroendocrineGene Expression Regulation, NeoplasticInhibitor of Differentiation Protein 1MiceMitosisNeoplasm InvasivenessPancreatic NeoplasmsBiomarkers, TumorIdb1 protein, mouseInhibitor of Differentiation Protein 1

Identifiers

PMID23691228
PMCPMC3653861
OpenAlexW2022304175

What OpenQuestion holds

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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.