Evidence map›Paper›PMID 33864039›Full record

ArticleNature biomedical engineering2021

Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma.

Amanda Randles, Hans-Georg Wirsching, Jamie A Dean, Yu-Kang Cheng, Samuel Emerson, Siobhan S Pattwell, Eric C Holland, Franziska Michor

Open access · greenAbstract read
In one paragraph

Article in Nature biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

21 citing papers in PubMed, 32 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

8 authors at 8 institutions in 3 countries.

Amanda RandlesDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.ORCID http://orcid.org/0000-0001-6318-3885
Hans-Georg WirschingDivision of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Jamie A DeanDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0002-5434-8032
Yu-Kang ChengDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
Samuel EmersonDepartment of Neurological Surgery, University of Washington, Seattle, WA, USA.
Siobhan S PattwellDivision of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Eric C HollandDivision of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. eholland@fredhutch.org.ORCID http://orcid.org/0000-0002-3792-7120
Franziska MichorDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. michor@jimmy.harvard.edu.ORCID http://orcid.org/0000-0003-4869-8842
Broad Institute · USCape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa · ZADana-Farber Cancer Institute · USDuke University · USFred Hutch Cancer Center · USHarvard University · USUniversity of Washington · USUniversity of Zurich · CH

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4M
Women's Cancer Research ProgramP30CA014236 · NCI · DUKE UNIVERSITY · PI JOHN N. NORTON · 1985 to 2026
$174.8M
Project 3: Single Cell Measures of Intratumor Diversity for Optimal Breast Cancer TherapyU54CA193461 · NCI · DANA-FARBER CANCER INST · PI HOLLAND, ERIC C. · 2015 to 2020
$11.2M
Toward coupled multiphysics models of hemodynamics on leadership systemsDP5OD019876 · OD · UNIVERSITY OF CALIF-LAWRNC LVRMR NAT LAB · PI RANDLES, AMANDA E · 2014 to 2018
$2.0M
Critical role for a TrkB splice variant in oncogenic signalingR21CA223531 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HOLLAND, ERIC C. · 2018 to 2019
$415k
NCI NIH HHS P30 CA014236NCI NIH HHS P30 CA015704NCI NIH HHS R21 CA223531NCI NIH HHS U54 CA193461NIH HHS DP5 OD019876
6 · The paper itself

Abstract

Glioblastoma stem-like cells dynamically transition between a chemoradiation-resistant state and a chemoradiation-sensitive state. However, physical barriers in the tumour microenvironment restrict the delivery of chemotherapy to tumour compartments that are distant from blood vessels. Here, we show that a massively parallel computational model of the spatiotemporal dynamics of the perivascular niche that incorporates glioblastoma stem-like cells and differentiated tumour cells as well as relevant tissue-level phenomena can be used to optimize the administration schedules of concurrent radiation and temozolomide-the standard-of-care treatment for glioblastoma. In mice with platelet-derived growth factor (PDGF)-driven glioblastoma, the model-optimized treatment schedule increased the survival of the animals. For standard radiation fractionation in patients, the model predicts that chemotherapy may be optimally administered about one hour before radiation treatment. Computational models of the spatiotemporal dynamics of the tumour microenvironment could be used to predict tumour responses to a broader range of treatments and to optimize treatment regimens.

Indexed as

Models, BiologicalAnimalsAntineoplastic Agents, AlkylatingBrain NeoplasmsDisease Models, AnimalDrug Administration ScheduleDrug Resistance, NeoplasmGlioblastomaHumansMicePlatelet-Derived Growth FactorRadiation, IonizingSurvival RateTemozolomideTreatment OutcomeTumor MicroenvironmentAntineoplastic Agents, AlkylatingPlatelet-Derived Growth FactorTemozolomide

Identifiers

PMID33864039
PMCPMC8054983
OpenAlexW3154662408

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.