Evidence map›Paper›PMID 26397099›Full record

ArticlePloS one2015

Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.

Mary E Sehl, Miki Shimada, Alfonso Landeros, Kenneth Lange, Max S Wicha

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.6field-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

23 citing papers in PubMed, 38 citations in OpenAlex.

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  7. Cancer informatics · 2022
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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

5 authors at 3 institutions in 2 countries.

Mary E SehlDivision of Hematology-Oncology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America; Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America.
Miki ShimadaWaseda University, Shinjuku, Japan.
Alfonso LanderosDepartment of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America.
Kenneth LangeDepartment of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America; Department of Statistics, University of California, Los Angeles, Los Angeles, CA, United States of America.
Max S WichaUniversity of Michigan Comprehensive Cancer Center, Ann Arbor, MI, United States of America.
University of California, Los Angeles · USUniversity of Michigan–Ann Arbor · USWaseda University · JP

Funding

UCLA Clinical and Translational Science InstituteKL2TR000122 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$4.5M
NCATS NIH HHS KL2 TR000122NCATS NIH HHS KL2TR000122
6 · The paper itself

Abstract

Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochastic stimulation techniques allow for the careful study of complex systems in biology and medicine and are ideal for the investigation of strategies aimed at CSC eradication. We present a mathematical model of the breast cancer stem cell (BCSC) niche to predict population dynamics during carcinogenesis and in response to treatment. Using data from cell line and mouse xenograft experiments, we estimate rates of interconversion between mesenchymal and epithelial states in BCSCs and find that EMT/MET transitions occur frequently. We examine bulk tumor growth dynamics in response to alterations in the rate of symmetric self-renewal of BCSCs and find that small changes in BCSC behavior can give rise to the Gompertzian growth pattern observed in breast tumors. Finally, we examine stochastic reaction kinetic simulations in which elements of the breast cancer stem cell niche are inhibited individually and in combination. We find that slowing self-renewal and disrupting the positive feedback loop between IL-6, Stat3 activation, and NF-κB signaling by simultaneous inhibition of IL-6 and HER2 is the most effective combination to eliminate both mesenchymal and epithelial populations of BCSCs. Predictions from our model and simulations show excellent agreement with experimental data showing the efficacy of combined HER2 and Il-6 blockade in reducing BCSC populations. Our findings will be directly examined in a planned clinical trial of combined HER2 and IL-6 targeted therapy in HER2-positive breast cancer.

Indexed as

Epithelial-Mesenchymal TransitionModels, TheoreticalAlgorithmsBreast NeoplasmsCell Transformation, NeoplasticComputer SimulationFemaleHumansModels, BiologicalModels, StatisticalNeoplastic Stem CellsStem Cell NicheTreatment OutcomeTumor Microenvironment

Identifiers

PMID26397099
PMCPMC4580445
OpenAlexW2207784886

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.