Evidence map›Paper›PMID 30158688›Full record

ArticleScientific reports2018

Development of a Novel 3D Tumor-tissue Invasion Model for High-throughput, High-content Phenotypic Drug Screening.

T J Puls, Xiaohong Tan, Mahera Husain, Catherine F Whittington, Melissa L Fishel, Sherry L Voytik-Harbin

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
–field-weighted citation impact
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

41 citing papers in PubMed.

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  15. Engineering Heterogeneous Tumor Models for Biomedical Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
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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

6 authors.

T J PulsWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Xiaohong TanWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Mahera HusainWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Catherine F WhittingtonWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Melissa L FishelDepartment of Pediatrics, Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.ORCID 0000-0002-9436-6382
Sherry L Voytik-HarbinWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA. harbins@purdue.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While much progress has been made in the war on cancer, highly invasive cancers such as pancreatic cancer remain difficult to treat and anti-cancer clinical trial success rates remain low. One shortcoming of the drug development process that underlies these problems is the lack of predictive, pathophysiologically relevant preclinical models of invasive tumor phenotypes. While present-day 3D spheroid invasion models more accurately recreate tumor invasion than traditional 2D models, their shortcomings include poor reproducibility and inability to interface with automated, high-throughput systems. To address this gap, a novel 3D tumor-tissue invasion model which supports rapid, reproducible setup and user-definition of tumor and surrounding tissue compartments was developed. High-cell density tumor compartments were created using a custom-designed fabrication system and standardized oligomeric type I collagen to define and modulate ECM physical properties. Pancreatic cancer cell lines used within this model showed expected differential invasive phenotypes. Low-passage, patient-derived pancreatic cancer cells and cancer-associated fibroblasts were used to increase model pathophysiologic relevance, yielding fibroblast-mediated tumor invasion and matrix alignment. Additionally, a proof-of-concept multiplex drug screening assay was applied to highlight this model's ability to interface with automated imaging systems and showcase its potential as a predictive tool for high-throughput, high-content drug screening.

Indexed as

Antineoplastic AgentsCell Culture TechniquesCell Line, TumorCoculture TechniquesDrug Evaluation, PreclinicalHumansPancreatic NeoplasmsAntineoplastic Agents

Identifiers

PMID30158688
PMCPMC6115445

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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.