Evidence map›Paper›PMID 26405815›Full record

ArticlePloS one2015

Systems Analysis of Adaptive Responses to MAP Kinase Pathway Blockade in BRAF Mutant Melanoma.

Brian J Capaldo, Devin Roller, Mark J Axelrod, Alex F Koeppel, Emanuel F Petricoin, Craig L Slingluff, Michael J Weber, Aaron J Mackey, Daniel Gioeli, Stefan Bekiranov

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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Trial
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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

10 authors at 2 institutions in 1 country.

Brian J CapaldoDepartment of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America.
Devin RollerDepartment of Microbiology, Immunology, and Cancer, University of Virginia, Charlottesville, Virginia, United States of America.
Mark J AxelrodDepartment of Microbiology, Immunology, and Cancer, University of Virginia, Charlottesville, Virginia, United States of America.
Alex F KoeppelBioinfomatics Core Facility, University of Virginia, Charlottesville, Virginia, United States of America.
Emanuel F PetricoinCenter for Applied Proteomics and Molecular Medicine, School of Systems Biology, College of Science, George Mason University, Manassas, Virginia, United States of America.
Craig L SlingluffDepartment of Surgery, University of Virginia, Charlottesville, Virginia, United States of America.
Michael J WeberDepartment of Microbiology, Immunology, and Cancer, University of Virginia, Charlottesville, Virginia, United States of America.
Aaron J MackeyCenter for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.
Daniel GioeliDepartment of Microbiology, Immunology, and Cancer, University of Virginia, Charlottesville, Virginia, United States of America.
Stefan BekiranovDepartment of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America.
University of Virginia · USGeorge Mason University · US

Funding

Women's Oncology Program - WONP30CA044579 · NCI · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI Thomas P. Loughran · 1987 to 2026
$72.1M
Checkpoint Signaling and Androgen Receptor Function in Prostate CancerR01CA178338 · NCI · UNIVERSITY OF VIRGINIA · PI GIOELI, DANIEL G · 2014 to 2018
$1.6M
NCI NIH HHS P30 CA044579NCI NIH HHS R01 CA178338
6 · The paper itself

Abstract

Fifty percent of cutaneous melanomas are driven by activated BRAFV600E, but tumors treated with RAF inhibitors, even when they respond dramatically, rapidly adapt and develop resistance. Thus, there is a pressing need to identify the major mechanisms of intrinsic and adaptive resistance and develop drug combinations that target these resistance mechanisms. In a combinatorial drug screen on a panel of 12 treatment-naïve BRAFV600E mutant melanoma cell lines of varying levels of resistance to mitogen-activated protein kinase (MAPK) pathway inhibition, we identified the combination of PLX4720, a targeted inhibitor of mutated BRaf, and lapatinib, an inhibitor of the ErbB family of receptor tyrosine kinases, as synergistically cytotoxic in the subset of cell lines that displayed the most resistance to PLX4720. To identify potential mechanisms of resistance to PLX4720 treatment and synergy with lapatinib treatment, we performed a multi-platform functional genomics analysis to profile the genome as well as the transcriptional and proteomic responses of these cell lines to treatment with PLX4720. We found modest levels of resistance correlated with the zygosity of the BRAF V600E allele and receptor tyrosine kinase (RTK) mutational status. Layered over base-line resistance was substantial upregulation of many ErbB pathway genes in response to BRaf inhibition, thus generating the vulnerability to combination with lapatinib. The transcriptional responses of ErbB pathway genes are associated with a number of transcription factors, including ETS2 and its associated cofactors that represent a convergent regulatory mechanism conferring synergistic drug susceptibility in the context of diverse mutational landscapes.

Indexed as

Mutation, MissenseAmino Acid SubstitutionCell Line, TumorDrug Resistance, NeoplasmHumansIndolesMAP Kinase Signaling SystemMelanomaProto-Oncogene Protein c-ets-2Proto-Oncogene Proteins B-rafSulfonamidesBRAF protein, humanETS2 protein, humanIndolesPLX 4720Proto-Oncogene Protein c-ets-2Proto-Oncogene Proteins B-rafSulfonamides

Identifiers

PMID26405815
PMCPMC4583389
OpenAlexW2182724389

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.