Evidence map›Paper›PMID 29590606›Full record

ArticleBiophysical journal2018

A Nonquiescent "Idling" Population State in Drug-Treated, BRAF-Mutated Melanoma.

B Bishal Paudel, Leonard A Harris, Keisha N Hardeman, Arwa A Abugable, Corey E Hayford, Darren R Tyson, Vito Quaranta

Open access · bronzeAbstract read
In one paragraph

Article in Biophysical journal, 2018. 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
1.8field-weighted citation impact, top 15% 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, 41 citations in OpenAlex.

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  20. Review
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 1 institution in 1 country.

B Bishal PaudelChemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, Tennessee; Vanderbilt International Scholars Program, Vanderbilt University, Nashville, Tennessee; Vanderbilt Quantitative Systems Biology Center, Vanderbilt University, Nashville, Tennessee.
Leonard A HarrisVanderbilt Quantitative Systems Biology Center, Vanderbilt University, Nashville, Tennessee; Department of Biochemistry, Vanderbilt University, Nashville, Tennessee.
Keisha N HardemanVanderbilt Quantitative Systems Biology Center, Vanderbilt University, Nashville, Tennessee; Department of Biochemistry, Vanderbilt University, Nashville, Tennessee.
Arwa A AbugableVanderbilt International Scholars Program, Vanderbilt University, Nashville, Tennessee.
Corey E HayfordChemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, Tennessee; Vanderbilt Quantitative Systems Biology Center, Vanderbilt University, Nashville, Tennessee.
Darren R TysonVanderbilt Quantitative Systems Biology Center, Vanderbilt University, Nashville, Tennessee; Department of Biochemistry, Vanderbilt University, Nashville, Tennessee.
Vito QuarantaVanderbilt Quantitative Systems Biology Center, Vanderbilt University, Nashville, Tennessee; Department of Biochemistry, Vanderbilt University, Nashville, Tennessee. Electronic address: vito.quaranta@vanderbilt.edu.
Vanderbilt University · US

Funding

The Vanderbilt Institute for Clinical and Translational Research (VICTR)UL1TR000445 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$41.4M
Vanderbilt Biomedical Informatics Training ProgramT15LM007450 · NLM · VANDERBILT UNIVERSITY · PI Jessica S. Ancker, Bradley A. Malin · 2002 to 2026
$19.7M
TrainingU54CA113007 · NCI · VANDERBILT UNIVERSITY · PI ESTRADA-RODGERS, LOURDES · 2004 to 2014
$19.2M
Vanderbilt/Meharry Initiative for Minority Student DvptR25GM062459 · NIGMS · VANDERBILT UNIVERSITY · PI VELEZ EDWARDS, DIGNA R · 2002 to 2020
$10.4M
Chemistry-Biology Interface Training GrantT32GM065086 · NIGMS · VANDERBILT UNIVERSITY · PI BACHMANN, BRIAN O, SULIKOWSKI, GARY ALLEN · 2002 to 2022
$7.0M
Quantitative Multiscale Imaging to Optimize Cancer Treatment StrategiesR01CA186193 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, RERICHA, ERIN C · 2014 to 2019
$3.1M
Phenotype Transitions in Small Cell Lung CancerU01CA215845 · NCI · VANDERBILT UNIVERSITY · PI LOPEZ, CARLOS FEDERICO, QUARANTA, VITO · 2017 to 2021
$2.8M
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast CancerU01CA174706 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, YANKEELOV, THOMAS E · 2013 to 2018
$2.5M
Targeting Altered Redox Metabolism in Lapatinib-resistant Breast CancerF31CA174365 · NCI · VANDERBILT UNIVERSITY · PI HARDEMAN, KEISHA NICOLE · 2012 to 2016
$155k
Uncovering the Molecular Basis of Phenotypic Diversification in BRAF-mutant MelanomaF31CA221147 · NCI · VANDERBILT UNIVERSITY · PI HAYFORD, COREY EDUARDO · 2017 to 2019
$134k
NCATS NIH HHS UL1 TR000445NCI NIH HHS F31 CA174365NCI NIH HHS F31 CA221147NCI NIH HHS R01 CA186193NCI NIH HHS U01 CA174706NCI NIH HHS U01 CA215845NCI NIH HHS U54 CA113007NIGMS NIH HHS R25 GM062459NIGMS NIH HHS T32 GM065086NLM NIH HHS T15 LM007450
6 · The paper itself

Abstract

Targeted therapy is an effective standard of care in BRAF-mutated malignant melanoma. However, the duration of tumor remission varies unpredictably among patients, and relapse is almost inevitable. Here, we examine the responses of several BRAF-mutated melanoma cell lines (including isogenic subclones) to BRAF inhibitors. We observe complex response dynamics across cell lines, with short-term responses (<100 h) varying from cell line to cell line. In the long term, however, we observe equilibration of all drug-treated populations into a nonquiescent state characterized by a balanced rate of death and division, which we term the "idling" state, and to our knowledge, this state has not been previously reported. Using mathematical modeling, we propose that the observed population-level dynamics are the result of cells transitioning between basins of attraction within a drug-modified phenotypic landscape. Each basin is associated with a drug-induced proliferation rate, a recently introduced metric of an antiproliferative drug effect. The idling population state represents a new dynamic equilibrium in which cells are distributed across the landscape such that the population achieves zero net growth. By fitting our model to experimental drug-response data, we infer the phenotypic landscapes of all considered melanoma cell lines and provide a unifying view of how BRAF-mutated melanomas respond to BRAF inhibition. We hypothesize that the residual disease observed in patients after targeted therapy is composed of a significant number of idling cells. Thus, defining molecular determinants of the phenotypic landscape that idling populations occupy may lead to "targeted landscaping" therapies based on rational modification of the landscape to favor basins with greater drug susceptibility.

Indexed as

Molecular Targeted TherapyMutationCell Line, TumorDrug Resistance, NeoplasmEpigenesis, GeneticHumansMelanomaProto-Oncogene Proteins B-rafProto-Oncogene Proteins B-raf

Identifiers

PMID29590606
PMCPMC5883941
OpenAlexW2795185088

What OpenQuestion holds

Textmetadata
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.