Evidence map›Paper›PMID 24435043›Full record

ArticleScientific reports2014

An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models.

Jonathan P Celli, Imran Rizvi, Adam R Blanden, Iqbal Massodi, Michael D Glidden, Brian W Pogue, Tayyaba Hasan

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed
11.5field-weighted citation impact, top 1% 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

75 citing papers in PubMed, 147 citations in OpenAlex.

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15 more citing papers are in PubMed but not listed here.

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

7 authors at 2 institutions in 1 country.

Jonathan P Celli1] Wellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, U.S.A. [2] Department of Physics, University of Massachusetts Boston, Boston, MA, U.S.A.
Imran RizviWellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, U.S.A.
Adam R BlandenWellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, U.S.A.
Iqbal MassodiWellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, U.S.A.
Michael D Glidden1] Wellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, U.S.A. [2] Department of Physics, University of Massachusetts Boston, Boston, MA, U.S.A.
Brian W PogueThayer School of Engineering, Dartmouth College, Hanover, NH, U.S.A.
Tayyaba HasanWellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, U.S.A.
Massachusetts General Hospital · USDartmouth College · US

Funding

Small Molecule Enhancers of Photodynamic Therapy for Skin CancerP01CA084203 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI Tayyaba Hasan · 2001 to 2026
$27.9M
Heterocellular 3D ovarian tumor arrays for imaging and mechanistic combinationsR01CA158415 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI HASAN, TAYYABA · 2012 to 2016
$2.0M
Targeted Photoactivatable Nanocells to Image and Treat Metastic Ovarian CancerRC1CA146337 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI HASAN, TAYYABA · 2009 to 2010
$971k
Mechanism-based therapies for pancreatic cancer informed by stromal microrheologyR00CA155045 · NCI · UNIVERSITY OF MASSACHUSETTS BOSTON · PI CELLI, JONATHAN P · 2013 to 2015
$708k
Mechanism-based therapies for pancreatic cancer informed by stromal microrheologyK99CA155045 · NCI · UNIVERSITY OF MASSACHUSETTS BOSTON · PI CELLI, JONATHAN P · 2011 to 2012
$324k
NCI NIH HHS 1RC1CA146337NCI NIH HHS K99 CA155045NCI NIH HHS K99CA155045NCI NIH HHS P01 CA084203NCI NIH HHS P01CA084203-06NCI NIH HHS R00 CA155045NCI NIH HHS R00CA155045NCI NIH HHS R01 CA158415NCI NIH HHS R01CA158415NCI NIH HHS R01CA1609998NCI NIH HHS RC1 CA146337
6 · The paper itself

Abstract

While it is increasingly recognized that three-dimensional (3D) cell culture models recapitulate drug responses of human cancers with more fidelity than monolayer cultures, a lack of quantitative analysis methods limit their implementation for reliable and routine assessment of emerging therapies. Here, we introduce an approach based on computational analysis of fluorescence image data to provide high-content readouts of dose-dependent cytotoxicity, growth inhibition, treatment-induced architectural changes and size-dependent response in 3D tumour models. We demonstrate this approach in adherent 3D ovarian and pancreatic multiwell extracellular matrix tumour overlays subjected to a panel of clinically relevant cytotoxic modalities and appropriately designed controls for reliable quantification of fluorescence signal. This streamlined methodology reads out the high density of information embedded in 3D culture systems, while maintaining a level of speed and efficiency traditionally achieved with global colorimetric reporters in order to facilitate broader implementation of 3D tumour models in therapeutic screening.

Indexed as

Cell Culture TechniquesHigh-Throughput Screening AssaysMicroscopyAnimalsAntineoplastic AgentsCell Line, TumorCell Membrane PermeabilityCell SurvivalDose-Response Relationship, DrugDrug Screening Assays, AntitumorFemaleHumansMiceTumor Cells, CulturedAntineoplastic Agents

Identifiers

PMID24435043
PMCPMC3894557
OpenAlexW2046561846

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.