Evidence map›Paper›PMID 32456018›Full record

ReviewCancers2020

Patient-Derived Xenograft Models of Pancreatic Cancer: Overview and Comparison with Other Types of Models.

Patrick L Garcia, Aubrey L Miller, Karina J Yoon

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
3.3field-weighted citation impact, top 6% 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

48 citing papers in PubMed, 70 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

3 authors at 1 institution in 1 country.

Patrick L GarciaDepartment of Pharmacology and Toxicology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.ORCID 0000-0001-8721-6434
Aubrey L MillerDepartment of Pharmacology and Toxicology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Karina J YoonDepartment of Pharmacology and Toxicology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
University of Alabama at Birmingham · US

Funding

Developing novel combination therapies for pancreatic cancerR01CA208272 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI YOON, KARINA J · 2017 to 2021
$1.7M
NCI NIH HHS R01 CA208272NIH HHS R01 CA208272
6 · The paper itself

Abstract

Pancreatic cancer (PC) is anticipated to be second only to lung cancer as the leading cause of cancer-related deaths in the United States by 2030. Surgery remains the only potentially curative treatment for patients with pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. Multiple recent preclinical studies focus on identifying effective treatments for PDAC, but the models available for these studies often fail to reproduce the heterogeneity of this tumor type. Data generated with such models are of unknown clinical relevance. Patient-derived xenograft (PDX) models offer several advantages over human cell line-based in vitro and in vivo models and models of non-human origin. PDX models retain genetic characteristics of the human tumor specimens from which they were derived, have intact stromal components, and are more predictive of patient response than traditional models. This review briefly describes the advantages and disadvantages of 2D cultures, organoids and genetically engineered mouse (GEM) models of PDAC, and focuses on the applications, characteristics, advantages, limitations, and the future potential of PDX models for improving the management of PDAC.

Indexed as

3D organoidsgenetically engineered mouse modelspancreatic cancerpatient-derived xenograftprecision medicinepreclinical drug evaluationpreclinical study

Identifiers

PMID32456018
PMCPMC7281668
OpenAlexW3028400974

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.