Evidence map›Paper›PMID 21045161›Full record

ReviewCancer prevention research (Philadelphia, Pa.)2010

Deploying mouse models of pancreatic cancer for chemoprevention studies.

Paul J Grippo, David A Tuveson

Open access · bronzeAbstract readReview
In one paragraph

Review in Cancer prevention research (Philadelphia, Pa.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.4field-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

15 citing papers in PubMed, 29 citations in OpenAlex.

  1. Review
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  14. Drug discovery today. Disease models · 2011
    Article
  15. 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 at 2 institutions in 2 countries.

Paul J GrippoRobert H Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, 303 East Superior Street, Chicago, IL 60611, USA. p-grippo@northwestern.edu
David A Tuveson
Cancer Research UK · GBNorthwestern University · US

Funding

Non Invasive Treatment Response Monitoring and molecular characterization of NSCLC using Cell-free DNA isolated by an AC Electrokinetic DeviceR01CA161283 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI GRIPPO, PAUL J · 2012 to 2016
$1.8M
Cancer Research UK A10211NCI NIH HHS R01 CA161283
6 · The paper itself

Abstract

With the advent of mouse models that recapitulate the cellular and molecular pathology of pancreatic neoplasia and cancer, it is now feasible to recruit and deploy these models for the evaluation of various chemopreventive and/or anticancer regimens. The highly lethal nature of pancreatic ductal adenocarcinoma (PDAC) makes multiple areas of research a priority, including assessment of compounds that prevent or suppress the development of early lesions that can transform into PDAC. Currently, there are over a dozen models available, which range from homogeneous preneoplastic lesions with remarkable similarity to human pancreatic intraepithelial neoplasms to models with a more heterogeneous population of lesions including cystic papillary and mucinous lesions. The molecular features of these models may also vary in a manner comparable with the differences observed in lesion morphology, and so, navigating the route of model selection is not trivial. Yet, arming the community of cancer investigators with a repertoire of models and the guidance to select relevant models that fit their research themes promises to produce findings that will have clinical relevance.

Indexed as

Disease Models, AnimalAnimalsCarcinoma, Pancreatic DuctalChemopreventionHumansMicePancreatic Neoplasms

Identifiers

PMID21045161
PMCPMC3242034
OpenAlexW2130576027

What OpenQuestion holds

Textmetadata
LicenceTDM
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.