Evidence map›Paper›PMID 21379332›Full record

ArticlePLoS computational biology2011

A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition.

Neda Bagheri, Marisa Shiina, Douglas A Lauffenburger, W Michael Korn

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact, top 95% 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

22 citing papers in PubMed, 2 citations in OpenAlex.

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  9. DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.Proceedings of the National Academy of Sciences of the United States of America · 2018
    Article
  10. Article
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  12. Review
  13. Computational modeling approaches to the dynamics of oncolytic viruses.Wiley interdisciplinary reviews. Systems biology and medicine · 2016
    Review
  14. Article
  15. Article
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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

4 authors at 3 institutions in 1 country.

Neda BagheriDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Marisa Shiina
Douglas A Lauffenburger
W Michael Korn
Massachusetts Institute of Technology · USUCSF Helen Diller Family Comprehensive Cancer Center · USUniversity of California, San Francisco · US

Funding

Tumor Cell Networks CenterU54CA112967 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI LAUFFENBURGER, DOUGLAS A · 2004 to 2015
$26.2M
The Adenovirus Receptor CAR in Epithelial MalignanciesR01CA095701 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KORN, WOLFGANG MICHAEL · 2003 to 2007
$1.3M
Experimentally Validated Mathematical Modeling of Oncolytic Virus InfectionR01CA118545 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KORN, WOLFGANG MICHAEL · 2006 to 2009
$853k
NCI NIH HHS R01 CA095701NCI NIH HHS R01 CA118545NCI NIH HHS U54 CA112967NCI NIH HHS U54-CA112967NCI NIH HHS U54 CA11297
6 · The paper itself

Abstract

Oncolytic adenoviruses, such as ONYX-015, have been tested in clinical trials for currently untreatable tumors, but have yet to demonstrate adequate therapeutic efficacy. The extent to which viruses infect targeted cells determines the efficacy of this approach but many tumors down-regulate the Coxsackievirus and Adenovirus Receptor (CAR), rendering them less susceptible to infection. Disrupting MAPK pathway signaling by pharmacological inhibition of MEK up-regulates CAR expression, offering possible enhanced adenovirus infection. MEK inhibition, however, interferes with adenovirus replication due to resulting G1-phase cell cycle arrest. Therefore, enhanced efficacy will depend on treatment protocols that productively balance these competing effects. Predictive understanding of how to attain and enhance therapeutic efficacy of combinatorial treatment is difficult since the effects of MEK inhibitors, in conjunction with adenovirus/cell interactions, are complex nonlinear dynamic processes. We investigated combinatorial treatment strategies using a mathematical model that predicts the impact of MEK inhibition on tumor cell proliferation, ONYX-015 infection, and oncolysis. Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies. Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study. Further analysis revealed that a CAR-independent mechanism may be responsible for amplified virus production and cell death. We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis. Enhanced oncolytic therapy has the potential to dramatically improve non-surgical cancer treatment, especially in locally advanced or metastatic cases where treatment options remain limited.

Indexed as

Models, BiologicalAdenoviridaeAntineoplastic Combined Chemotherapy ProtocolsCell CycleCell SurvivalCombined Modality TherapyComputer SimulationCoxsackie and Adenovirus Receptor-Like Membrane ProteinHCT116 CellsHumansMAP Kinase Signaling SystemMitogen-Activated Protein Kinase KinasesNeoplasmsNonlinear DynamicsOncolytic VirotherapyReceptors, VirusCLMP protein, humanCoxsackie and Adenovirus Receptor-Like Membrane Proteindl1520Mitogen-Activated Protein Kinase KinasesReceptors, VirusViral Vaccines

Identifiers

PMID21379332
PMCPMC3040662
OpenAlexW1515671756

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.